An intelligent indoor ventilation and purification system and method based on a cloud platform

Abstract

An intelligent indoor ventilation and purification system and method based on a cloud platform. System: The multi-dimensional air quality monitoring unit includes multiple groups of fixed and portable multi-dimensional air quality monitoring devices. The cloud server is respectively connected to the indoor three-dimensional modeling unit and the multi-dimensional air quality monitoring unit; the cloud processing platform is connected to the cloud server; the intelligent control unit of the ventilation equipment is connected to the cloud processing platform. Method: Obtain environmental monitoring data, location information, and indoor space three-dimensional point cloud data and upload them to the cloud server; the cloud processing platform can, through comprehensive analysis of real-time data and historical data, predict the future air quality change trend, formulate the optimal ventilation strategy, and visually display the system status and decision results through the user interface module; the intelligent control unit of the ventilation equipment dynamically adjusts the operating status of the ventilation equipment according to the decision instructions of the cloud processing platform, ensuring the optimal balance between indoor air quality and energy consumption. This system and method can ensure the optimal balance between indoor air quality and energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent environmental purification, and particularly relates to an intelligent indoor ventilation and purification system and method based on a cloud platform. Background Art

[0002] In modern cities and industrialized societies, the quality of indoor air directly affects people's health and quality of life. Indoor air pollutants include carbon dioxide, volatile organic compounds (VOCs), carbon monoxide, and particulate matter (such as PM2.5). Long-term exposure to these pollutants can lead to health problems such as respiratory diseases and cardiovascular diseases. Therefore, it is of great significance to keep indoor air clean in real time, ensure good ventilation and an appropriate air exchange rate, for improving the indoor environmental quality and protecting people's health.

[0003] Currently, indoor air quality monitoring and ventilation control mainly rely on traditional manual control and simple automatic control systems. These systems usually control based on preset schedules or the feedback of a single sensor, and cannot dynamically adapt to environmental changes. Although some advanced systems use sensor networks to monitor air quality in real time, their data processing and decision-making capabilities are still limited, making it difficult to achieve comprehensive control of complex environments. In addition, existing ventilation control systems often cannot balance air quality and energy consumption, resulting in problems such as energy waste or unqualified air quality. For example, in the case of high pollutant concentrations, traditional systems may not be able to respond in time, leading to a decline in air quality; while when the air quality is good, the system may over-ventilate, thus increasing unnecessary energy consumption. To solve the above existing problems, there is an urgent need to provide an intelligent and efficient indoor air purification solution. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention provides an intelligent indoor ventilation and purification system and method based on a cloud platform. The system has a high degree of automation, can formulate an optimal ventilation control strategy according to different environmental conditions, can dynamically adjust the operating state of ventilation equipment, and can ensure the optimal balance of indoor air quality and energy consumption. The method can comprehensively monitor and optimize the control of indoor air quality, and can significantly improve the indoor environmental quality and energy utilization efficiency.

[0005] To achieve the above object, the present invention provides an intelligent indoor ventilation and purification system based on a cloud platform, including an indoor three-dimensional modeling unit, a plurality of ventilation devices, a multi-dimensional air quality monitoring unit, a cloud server, a cloud processing platform, and an intelligent control unit for ventilation equipment;

[0006] The plurality of ventilation devices are respectively installed in each ventilation opening and each window in the indoor place to be purified;

[0007] The indoor 3D modeling unit includes multiple groups of camera matrices, a first data transmission module, and a first microcontroller. The first microcontroller is respectively connected to the multiple groups of camera matrices and the first data transmission module. The multiple groups of camera matrices are arranged at different positions in the indoor place to be purified, and are used to collect image data of the indoor place to be purified from different angles and positions. The first microcontroller is used to process the image data into 3D point cloud data of the indoor space, and is used to upload the 3D point cloud data of the indoor space to the cloud server through the first data transmission module;

[0008] The multi-dimensional air quality monitoring unit includes multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices. The multiple groups of fixed multi-dimensional air quality monitoring devices are respectively installed near each ventilation opening and each window in the indoor place to be purified, and the multiple groups of portable multi-dimensional air quality monitoring devices are respectively worn on different personnel in the indoor place to be purified. Both the fixed multi-dimensional air quality monitoring device and the portable multi-dimensional air quality monitoring device include a temperature monitoring module, a humidity monitoring module, a CO 2 concentration monitoring module, a VOC concentration monitoring module, a CO₂ concentration monitoring module, a wind speed monitoring module, a dust concentration monitoring module, a GPS position information module, a second data transmission module, and a second microcontroller. The temperature monitoring module, the humidity monitoring module, the CO 2 concentration monitoring module, the VOC concentration monitoring module, the CO₂ concentration monitoring module, the wind speed monitoring module, the dust concentration monitoring module, and the GPS position information module are respectively used to collect temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO₂ concentration data, wind speed data, dust concentration data, and position information at the location where they are located in real time. The second microcontroller is respectively connected to the temperature monitoring module, the humidity monitoring module, the CO 2 concentration monitoring module, the VOC concentration monitoring module, the CO₂ concentration monitoring module, the wind speed monitoring module, the dust concentration monitoring module, and the GPS position information module, and is used to perform sequential processing on the received temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO₂ concentration data, wind speed data, and dust concentration data based on the internal clock module to obtain sequential environmental monitoring data, and is used to upload the received position information and sequential environmental monitoring data to the cloud server through the second data transmission module;

[0009] The cloud server is respectively connected to the indoor 3D modeling unit and the multi-dimensional air quality monitoring unit; the cloud server includes an air quality detection database and a 3D space reconstruction module; the air quality detection database includes a 3D space point cloud information storage area, a multi-dimensional air quality monitoring data storage area, and a GPS information storage area, which is used to store the indoor space 3D point cloud data in the 3D space point cloud information storage area after receiving it, and is used to store the time-series environmental monitoring data sent by each multi-dimensional air quality monitoring device in the multi-dimensional air quality monitoring data storage area, and is used to store the position information sent by each multi-dimensional air quality monitoring device in the GPS information storage area; the 3D space reconstruction module is used to read the indoor space 3D point cloud data in the 3D space point cloud information storage area, read the position information in the GPS information storage area, generate a 3D model of the indoor place to be purified based on the indoor space 3D point cloud data, mark the relative positions of multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices in the constructed 3D model of the indoor place to be purified based on the position information, and store the 3D model of the indoor place to be purified marked with relative position information in the 3D space point cloud information storage area;

[0010] The cloud processing platform is connected to the cloud server; the cloud processing platform includes a data processing module, a data analysis and modeling module, a decision engine module, a data storage and management module, and a user interface module;

[0011] The data processing module is used to read the time-series environmental monitoring data in the multi-dimensional air quality monitoring data storage area, read the position information of the multi-dimensional air quality monitoring device in the GPS information storage area, and the 3D model of the indoor place to be purified marked with relative position information in the 3D space point cloud information storage area, and send it to the data analysis and modeling module after data preprocessing;

[0012] The data analysis and modeling module is used to analyze and process the time-series environmental monitoring data, the position information of the multi-dimensional air quality monitoring device, and the 3D model of the indoor place to be purified marked with relative position information, and obtain the air quality data at different positions in the indoor place. At the same time, predict the future air quality change situation in the indoor place, and generate a detailed data analysis report and visualization chart based on the air quality data at different positions in the indoor place and the data of the future air quality change situation in the indoor place. At the same time, send the detailed data analysis report, visualization chart, and the 3D model of the indoor place to be purified marked with relative position information to the decision engine module;

[0013] The decision engine module is used to generate a ventilation control strategy based on a detailed data analysis report, visualization charts, a 3D model of the indoor space to be purified with relative position information marked, the position information and data information of multiple ventilation devices, the operating parameters of multiple ventilation devices, and relevant input control instructions, and send the detailed data analysis report, visualization charts, 3D model of the indoor space to be purified with relative position information marked, and the ventilation control strategy to the data storage and management module and the user interface module, and send the ventilation control strategy to the intelligent control unit of the ventilation device;

[0014] The user interface module is used to provide a human-computer interaction interface, receive the position information and data information of multiple ventilation devices and relevant control instructions uploaded by relevant personnel, and send them to the decision engine module, and receive the data sent by the decision engine module, and display the detailed data analysis report, visualization charts, 3D model of the indoor space to be purified with relative position information marked, and the ventilation control strategy in real time;

[0015] The intelligent control unit of the ventilation device is connected to the cloud processing platform, and includes a monitoring sensor group, a driving module, a storage module, a data transmission module three, and a controller;

[0016] The monitoring sensor group is respectively connected to multiple ventilation devices, and is used to collect the operating parameters of multiple ventilation devices in real time and feedback them to the controller;

[0017] The driving module is respectively connected to multiple ventilation devices, and is used to drive the actions of the corresponding ventilation devices according to the driving control signals;

[0018] The storage module is used to store the ventilation control strategy and the operating parameters of the ventilation devices;

[0019] The controller is respectively connected to the monitoring sensor group, the driving module, the storage module, and the data transmission module three, and is used to send the received ventilation control strategy and the operating parameters of the ventilation devices to the storage module, upload the operating parameters of the ventilation devices to the decision engine module in the cloud processing platform, and send corresponding driving control signals to the driving module according to the ventilation control strategy.

[0020] In order to be able to timely remind relevant personnel by means of sound alarm when an abnormality occurs, the intelligent control unit of the ventilation device further includes an alarm module connected to the controller. The controller is used to analyze the operating parameters of multiple ventilation devices, control the alarm module to give an alarm when abnormal data is found, and at the same time, upload the abnormal data to the cloud processing platform.

[0021] As a preference, the controller is a PLC controller.

[0022] To ensure the convenience of wearing, the portable multi-dimensional air quality monitoring device further includes a power supply module for supplying power.

[0023] In the present invention, multiple sets of fixed multi-dimensional air quality monitoring devices are respectively installed near each ventilation opening and near each window in the indoor place to be purified. Multiple sets of portable multi-dimensional air quality monitoring devices are respectively worn on the bodies of different personnel in the indoor place to be purified. And the multi-dimensional air quality monitoring device includes a temperature monitoring module, a humidity monitoring module, a CO 2 concentration monitoring module, a VOC concentration monitoring module, a CO concentration monitoring module, a wind speed monitoring module, a dust concentration monitoring module, and a GPS position information module. The environmental parameters at different positions in the indoor place can be comprehensively detected by using the fixed and randomly movable multi-dimensional air quality monitoring devices, which is beneficial to realizing the accurate monitoring of the air quality data in the indoor place and helps to accurately predict the future air quality. The indoor three-dimensional modeling unit includes multiple sets of camera matrices arranged at different positions in the indoor place to be purified, and the image data of the indoor place can be comprehensively collected, so that the three-dimensional point cloud data of the indoor space can be easily obtained accurately. The cloud server includes an air quality detection database and a three-dimensional space reconstruction module, which can not only store the obtained environmental monitoring data, the position information sent by the multi-dimensional air quality monitoring device, and the indoor three-dimensional point cloud data, but also use the three-dimensional space reconstruction module to generate an accurate three-dimensional model of the indoor place to be purified based on the indoor three-dimensional point cloud data, and further obtain a three-dimensional model of the indoor place to be purified marked with relative position information. Thus, after obtaining the air quality data at different positions, it can be displayed in the three-dimensional model of the indoor place to be purified, so that relevant personnel can intuitively understand the air quality situation in the indoor place in real time. The cloud processing platform includes a data analysis and modeling module and a decision engine module. The real-time data and historical data can be comprehensively analyzed through the data analysis and modeling module, and then the air quality data at different positions in the current indoor place can be obtained, and the change trend of the future air quality in the indoor place can be predicted, so that the optimal ventilation control strategy can be formulated. This strategy can minimize the total energy consumption of multiple ventilation devices while ensuring the purification effect. The intelligent control unit of the ventilation device is connected to the cloud processing platform and multiple ventilation devices, which can facilitate the output of drive control instructions according to the ventilation control strategy of the cloud processing platform, so that the corresponding drive control signals can be sent to the corresponding ventilation devices according to the ventilation control strategy, realizing the intelligent and low-energy consumption control process of the ventilation devices. Through the setting of the monitoring sensor group, the operation parameters of each ventilation device can be collected in real time and uploaded to the decision engine module in the cloud processing platform, so that the decision engine module can dynamically adjust the ventilation control strategy, and then dynamically adjust the operation state of the ventilation devices, ensuring that the indoor air quality and energy consumption always maintain the optimal balance.

[0024] The present invention also provides an intelligent indoor ventilation and purification method based on a cloud platform, which adopts an intelligent indoor ventilation and purification system based on a cloud platform, and is characterized by including the following steps:

[0025] Step 1: Arrange multiple groups of fixed multi-dimensional air quality monitoring devices at different positions in the indoor place to be purified, and supply power to the multiple groups of fixed multi-dimensional air quality monitoring devices. At the same time, wear multiple groups of portable multi-dimensional air quality monitoring devices for different moving individuals in the indoor place to be purified;

[0026] Arrange a space modeling unit in the indoor place to be purified, and supply power to the indoor three-dimensional modeling unit, wherein multiple groups of camera matrices are distributed at different positions in the indoor place to be purified;

[0027] Establish communication connections between multiple groups of fixed multi-dimensional air quality monitoring devices, multiple groups of portable multi-dimensional air quality monitoring devices, the indoor three-dimensional modeling unit and the cloud server by using a network, establish a communication connection between the cloud server and the cloud processing platform by using a network, and establish a communication connection between the cloud processing platform and the intelligent control unit of the ventilation equipment by using a network; establish a communication connection between the intelligent control unit of the ventilation equipment and multiple ventilation equipment;

[0028] Step 2: Collect image data of the indoor place to be purified from different angles and positions through multiple groups of camera matrices, and use a microcontroller to process the image data to obtain indoor space three-dimensional point cloud data, and then upload the indoor space three-dimensional point cloud data to the cloud server through data transmission module 1 and a network;

[0029] Use multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices to perform synchronous monitoring operations. For each multi-dimensional air quality monitoring device, collect temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO concentration data, wind speed data, dust concentration data, and GPS location information at the location where it is located through a temperature monitoring module, a humidity monitoring module, a CO 2 concentration monitoring module, a VOC concentration monitoring module, a CO concentration monitoring module, a wind speed monitoring module, a dust concentration monitoring module, and a GPS location information module, and use a microcontroller 2 to perform time series processing on the temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO concentration data, wind speed data, and dust concentration data to obtain time series environmental monitoring data, and then upload the time series environmental monitoring data and location information to the cloud server through data transmission module 2 and a network;

[0030] Step 3: After receiving the 3D point cloud data of the indoor space, the cloud server stores it in the 3D space point cloud information storage area. After receiving the time-series environmental monitoring data sent by each multi-dimensional air quality monitoring device, it stores it in the multi-dimensional air quality monitoring data storage area. After receiving the location information sent by each multi-dimensional air quality monitoring device, it stores it in the GPS information storage area;

[0031] The 3D space reconstruction module reads the 3D point cloud data of the indoor space in the 3D space point cloud information storage area and the location information in the GPS information storage area, generates a 3D model of the indoor place to be purified based on the 3D point cloud data of the indoor space, marks the relative positions of multiple sets of fixed multi-dimensional air quality monitoring devices and multiple sets of portable multi-dimensional air quality monitoring devices in the constructed 3D model of the indoor place to be purified, and stores the 3D model of the indoor place to be purified marked with relative position information in the 3D space point cloud information storage area;

[0032] Step 4: The cloud processing platform reads the time-series environmental monitoring data in the multi-dimensional air quality monitoring data storage area, the location information of the multi-dimensional air quality monitoring device in the GPS information storage area, and the 3D model of the indoor place to be purified marked with relative position information in the 3D space point cloud information storage area through the data processing module, and after preprocessing the data, forms a unified integrated data set, and then sends the integrated data set to the data analysis and modeling module; At the same time, the location information and data information of multiple ventilation devices are input into the decision engine module through the user interface module, the operating parameters of multiple ventilation devices are collected in real time by the monitoring sensor group, and the controller uploads the operating parameters of multiple ventilation devices to the decision engine module in the cloud processing platform through the data transmission module three and the network;

[0033] The data analysis and modeling module uses spatio-temporal data analysis technology to capture the spatio-temporal characteristics of air quality changes from real-time integrated datasets and historical integrated datasets, identify and extract key influencing factor data; the physical model within the data analysis and modeling module simulates the air flow and pollutant diffusion processes in indoor spaces based on the key influencing factor data, and combines the macroscopic fluid mechanics model and the microscopic particle dynamics model to comprehensively analyze the spatio-temporal changes in indoor air quality and obtain spatio-temporal change data of air quality; the machine learning model within the data analysis and modeling module is trained based on the key influencing factor data and the spatio-temporal change data of air quality, and continuously improves the prediction accuracy and robustness through self-learning and optimization. Then, using time series analysis and machine learning algorithms, and based on the key influencing factor data and the spatio-temporal change data of air quality obtained in real time, it obtains air quality data at different positions in the indoor space, predicts the future trend of air quality changes and potential high-concentration pollution events, and finally generates a detailed data analysis report and visualization charts. Then, it sends the detailed data analysis report, visualization charts, and model data of the three-dimensional model of the indoor space to be purified marked with relative position information to the decision engine module;

[0034] The decision engine module generates a preliminary control strategy through predefined rules and strategies, and based on the detailed data analysis report, visualization charts, model data of the three-dimensional model of the indoor space to be purified marked with relative position information, the position information and data information of multiple ventilation devices, and the operating parameters of multiple ventilation devices. It uses advanced optimization algorithms to optimize the preliminary control strategy to obtain the optimal ventilation control strategy that balances air quality and energy consumption, and then sends the ventilation control strategy to the intelligent control unit of the ventilation device. At the same time, it sends the detailed data analysis report, visualization charts, three-dimensional model of the indoor space to be purified marked with relative position information, and ventilation control strategy to the data storage and management module and the user interface module respectively for intuitive display through the user interface module;

[0035] Step Five: After receiving the ventilation control strategy, the intelligent control unit of the ventilation device sends the control strategy to the storage module for storage, and sends corresponding drive control signals to the drive module according to the ventilation control strategy to control the operation of each ventilation device. Furthermore, it uses the actions of multiple ventilation devices to purify the air quality in the indoor space, and while ensuring the purification effect, achieves the goal of minimizing the total energy consumption of multiple ventilation devices;

[0036] Synchronously, the monitoring module collects the operating parameters of multiple ventilation devices in real time and sends them to the controller; the controller sends the operating parameters of multiple ventilation devices to the storage module for storage, and at the same time, uploads the operating parameters of multiple ventilation devices to the decision engine module in the cloud processing platform and adjusts the drive control signal in real time according to the latest received ventilation control strategy.

[0037] As a preference, in Step 4, data preprocessing includes data cleaning, data normalization, and data fusion processing.

[0038] As a preference, in Step 4, during the process of formulating the preliminary control strategy in the decision engine module, when the detailed data analysis report and visualization chart contain the possibility of high pollution concentration events in a certain future time period, the preliminary control strategy is adjusted in advance to prevent and reduce the impact of high pollution events.

[0039] In the present invention, image data of an indoor venue is collected by multiple camera matrices in the indoor 3D modeling unit, which facilitates obtaining accurate 3D point cloud information of the indoor venue and is conducive to the 3D space reconstruction module generating a high-precision 3D model of the indoor venue. The multi-dimensional air quality monitoring device can use multiple monitoring modules to real-time monitor various environmental parameters such as temperature, humidity, CO2 concentration, VOC concentration, CO concentration, wind speed, dust concentration, and location information, and through the processing of the second microcontroller, sequential environmental monitoring data can be obtained. Using multiple sets of fixed multi-dimensional air quality monitoring devices and multiple sets of portable multi-dimensional air quality monitoring devices for synchronous monitoring operations can not only collect environmental parameters at fixed position points but also use individuals with autonomous movement capabilities to collect environmental parameters at random position points. Thus, on the basis of arranging a relatively small number of multi-dimensional air quality monitoring devices, comprehensive collection of environmental parameters within the indoor venue can be achieved. The location information collected by the multi-dimensional air quality monitoring device is stored in the GPS information storage area. It is convenient for the 3D space reconstruction module to read the location information data, and then the location of the multi-dimensional air quality monitoring device can be marked in the generated 3D model of the indoor venue, which is conducive to intuitively displaying the space of the indoor venue and also helps to more accurately analyze the air flow and pollutant diffusion paths, significantly improving the accuracy of environmental monitoring, prediction, and analysis. The data processing module in the cloud processing platform reads and preprocesses the data in the cloud server to ensure the quality and consistency of the data, and then a unified integrated data set that can comprehensively reflect the environmental characteristics can be formed, providing a reliable basis for subsequent analysis and decision-making. The cloud processing platform comprehensively analyzes real-time data and historical data through the data analysis and modeling module, comprehensively analyzes the spatio-temporal changes in the air quality of the indoor venue, and then predicts the future air quality change trend, and generates detailed data analysis reports and visualization charts, so as to provide comprehensive data support for the decision-making of the decision engine module. At the same time, since high-pollution concentration events that may occur in the future can be predicted in advance, it is beneficial to adjust the ventilation strategy in advance and prevent and reduce the impact of high-pollution events. The decision engine module generates a preliminary control strategy through detailed data analysis reports, visualization charts, model data of the 3D model of the indoor venue to be purified marked with relative location information, location information and data information of multiple ventilation devices, and operating parameters of multiple ventilation devices, taking into account both the environmental conditions of the indoor venue itself and fully combining the location and capabilities of the ventilation devices, so as to obtain a scientific decision. An advanced optimization algorithm is used to optimize the preliminary control strategy to obtain the optimal ventilation control strategy. In this way, it can respond to environmental changes in a timely manner according to real-time monitoring data and prediction results, provide a flexible and precise ventilation control method, and achieve the best balance between air quality and energy consumption.The detailed data analysis reports, visualization charts, three-dimensional models of indoor places to be purified with relative position information marked, and ventilation control strategies are intuitively displayed through the user interface module, which is convenient for relevant personnel to understand the operation of the system in real time.

[0040] The present invention adopts advanced sensing technologies and intelligent algorithms to achieve comprehensive monitoring and optimized control of indoor air quality. This method, through the automated processes of data processing, analysis, and decision-making, fully combines the advantages of intelligent algorithms and models. Through self-learning and optimization, it improves the accuracy and efficiency of air quality prediction and ventilation control, ensures the best balance between air quality and energy consumption under different environmental conditions, and significantly improves the indoor environmental quality and energy utilization efficiency. Brief Description of the Drawings

[0041] Figure 1 is the principle block diagram of the system part in the present invention;

[0042] Figure 2 is the principle block diagram of the multi-dimensional air quality monitoring device in the system part of the present invention;

[0043] Figure 3 is the principle block diagram of the intelligent control unit of the ventilation equipment in the system part of the present invention. Detailed Embodiments

[0044] The present invention will be further described below with reference to the drawings.

[0045] As Figures 1 to 3 shown, the present invention provides an intelligent indoor ventilation and purification system based on a cloud platform, including an indoor three-dimensional modeling unit, multiple ventilation devices, a multi-dimensional air quality monitoring unit, a cloud server, a cloud processing platform, and an intelligent control unit for ventilation equipment;

[0046] Multiple ventilation devices are respectively installed in each ventilation opening and each window in the indoor place to be purified;

[0047] The indoor three-dimensional modeling unit includes multiple groups of camera matrices, a data transmission module 1, and a microcontroller 1. The microcontroller 1 is respectively connected to multiple groups of camera matrices and the data transmission module 1; multiple groups of camera matrices are arranged at different positions in the indoor place to be purified for collecting image data of the indoor place to be purified from different angles and positions; the microcontroller 1 is used for processing the image data into three-dimensional point cloud data of the indoor space and uploading the three-dimensional point cloud data of the indoor space to the cloud server through the data transmission module 1;

[0048] The multi-dimensional air quality monitoring unit includes multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices. The multiple groups of fixed multi-dimensional air quality monitoring devices are respectively installed near each ventilation opening and near each window in the indoor place to be purified. In this way, it can ensure that various data at positions where interaction with the indoor environment can occur can be monitored.

[0049] The multiple groups of portable multi-dimensional air quality monitoring devices are respectively worn on different personnel in the indoor place to be purified; to ensure the convenience of wearing, the portable multi-dimensional air quality monitoring device also includes a power supply module, and the power supply module is used for power supply. Preferably, the portable multi-dimensional air quality monitoring device adopts a low-power design, and the power supply module therein adopts a lithium-ion battery pack.

[0050] Both the fixed multi-dimensional air quality monitoring device and the portable multi-dimensional air quality monitoring device include a temperature monitoring module, a humidity monitoring module, a CO 2 concentration monitoring module, a VOC concentration monitoring module, a CO concentration monitoring module, a wind speed monitoring module, a dust concentration monitoring module, a GPS position information module, a data transmission module two, and a microcontroller two; the temperature monitoring module is used to collect the temperature data at the location in real time; the humidity monitoring module is used to collect the humidity data at the location in real time; the CO 2 concentration monitoring module is used to collect the CO 2 concentration data at the location in real time; the VOC concentration monitoring module is used to collect the VOC concentration data at the location in real time; the CO concentration monitoring module is used to collect the CO concentration data at the location in real time; the wind speed monitoring module is used to collect the wind speed data at the location in real time; the dust concentration monitoring module is used to collect the dust concentration data at the location in real time; the GPS position information module is used to collect the position information at the location; the microcontroller two is respectively connected to the temperature monitoring module, the humidity monitoring module, the CO 2 concentration monitoring module, the VOC concentration monitoring module, the CO concentration monitoring module, the wind speed monitoring module, the dust concentration monitoring module, and the GPS position information module, and is used to perform timing processing on the received temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO concentration data, wind speed data, and dust concentration data based on the internal clock module to obtain timed environmental monitoring data, and is used to upload the received position information and timed environmental monitoring data to the cloud server through the data transmission module two;

[0051] As a preference, the dust concentration monitoring module can monitor the concentration of environmental suspended particulate matter using the light scattering principle, and the main monitoring objects include but are not limited to PM2.5.

[0052] The cloud server is respectively connected to the indoor 3D modeling unit and the multi-dimensional air quality monitoring unit; the cloud server includes an air quality detection database and a 3D space reconstruction module; the air quality detection database includes a 3D space point cloud information storage area, a multi-dimensional air quality monitoring data storage area, and a GPS information storage area, which are used to store the indoor space 3D point cloud data in the 3D space point cloud information storage area after receiving it, to store the time-series environmental monitoring data sent by each multi-dimensional air quality monitoring device in the multi-dimensional air quality monitoring data storage area, and to store the position information sent by each multi-dimensional air quality monitoring device in the GPS information storage area; the 3D space reconstruction module is used to read the indoor space 3D point cloud data in the 3D space point cloud information storage area, read the position information in the GPS information storage area, generate a 3D model of the indoor place to be purified based on the indoor space 3D point cloud data, mark the relative positions of multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices in the constructed 3D model of the indoor place to be purified based on the position information, and store the 3D model of the indoor place to be purified marked with relative position information in the 3D space point cloud information storage area;

[0053] The cloud processing platform is connected to the cloud server; the cloud processing platform includes a data processing module, a data analysis and modeling module, a decision engine module, a data storage and management module, and a user interface module;

[0054] The data processing module is used to read the time-series environmental monitoring data in the multi-dimensional air quality monitoring data storage area, read the position information of the multi-dimensional air quality monitoring device in the GPS information storage area, and the 3D model of the indoor place to be purified marked with relative position information in the 3D space point cloud information storage area, and perform data preprocessing on it and then send it to the data analysis and modeling module;

[0055] The internal of the data analysis and modeling module has advanced learning models and physical models, which are responsible for performing complex data analysis and modeling, obtaining air quality data at different positions in the indoor venue, predicting air quality changes, and formulating optimization strategies; the data analysis and modeling module can predict possible high-concentration events in the future based on the received time-series data, and can continuously improve the prediction accuracy and robustness through self-learning and optimization. Specifically, it is used to analyze and process the time-series environmental monitoring data, the position information of the multi-dimensional air quality monitoring device, and the three-dimensional model of the indoor venue to be purified with relative position information marked, and obtain the air quality data at different positions in the indoor venue. At the same time, it predicts the future air quality changes in the indoor venue, and generates a detailed data analysis report and visualization chart based on the air quality data at different positions in the indoor venue and the data of the future air quality changes in the indoor venue. At the same time, it sends the detailed data analysis report, visualization chart and the three-dimensional model of the indoor venue to be purified with relative position information marked to the decision engine module;

[0056] The decision engine module, as the core of the cloud processing platform, is responsible for comprehensively analyzing the data processing module and the spatial three-dimensional model and formulating the optimal ventilation strategy. The decision engine module includes a powerful rule engine, which can quickly generate preliminary decisions through predefined rules and strategies. These rules are usually based on the standard requirements for indoor air quality and the operating characteristics of ventilation equipment, starting the ventilation equipment when the concentration of certain pollutants exceeds a specific threshold, or reducing the ventilation volume to save energy when the air quality is good; after the preliminary decision is generated, the decision engine module can further optimize the ventilation strategy using advanced optimization algorithms. Optimization algorithms such as genetic algorithms and reinforcement learning are used to find the best strategy to balance air quality and energy consumption. The genetic algorithm continuously evolves and optimizes the solution by simulating the natural selection process, and finally finds the optimal ventilation control strategy. Reinforcement learning gradually learns the optimal action plan through interaction with the environment to achieve the goal of meeting the air quality requirements and minimizing energy consumption; the decision engine module not only relies on historical data and predefined rules, but also can dynamically adjust the ventilation strategy according to real-time monitoring data, by continuously monitoring the indoor and outdoor air quality, temperature and humidity, CO 2For multi-dimensional data such as concentration, VOC concentration, CO concentration, wind speed, and dust concentration, the decision-making engine can respond immediately to environmental changes. When the concentration of indoor air pollutants is detected to increase, the system can immediately increase the ventilation volume; when the external air quality deteriorates, the system can switch to the internal circulation mode to protect the indoor air quality. The decision-making engine module supports the switching of multiple modes and parameter adjustment. During working hours and non-working hours, different ventilation strategies can be adopted to adapt to changes in the personnel density; in different seasons, the ventilation strategy can be adjusted to adapt to changes in temperature and humidity. By comprehensively considering various environmental factors and user needs, the decision-making engine module can provide personalized and dynamically adjusted ventilation solutions.

[0057] Specifically, the decision-making engine module is used to generate a ventilation control strategy based on a detailed data analysis report, visualization charts, a three-dimensional model of the indoor place to be purified marked with relative position information, the position information and data information of multiple ventilation devices, the operating parameters of multiple ventilation devices, and relevant input control instructions, and send the detailed data analysis report, visualization charts, and three-dimensional model of the indoor place to be purified marked with relative position information, and the ventilation control strategy to the data storage and management module and the user interface module, and send the ventilation control strategy to the intelligent control unit of the ventilation device;

[0058] The data storage and management module is used to store a detailed data analysis report, visualization charts, a three-dimensional model of the indoor place to be purified marked with relative position information, and a ventilation control strategy; this module has an efficient database structure, ensuring the rapid access and secure storage of data. By storing historical data and decision-making information, it is conducive to long-term trend analysis and model improvement. At the same time, data access permissions can also be set to ensure the security of the system and data privacy.

[0059] The user interface module provides a friendly operation interface for users to view and manage the system. The user interface module includes a visualization client and a control panel. The visualization client can facilitate the real-time display of air quality data, three-dimensional models, and ventilation control strategies. The control panel allows users to manually input and adjust the operating status and parameters of ventilation devices, and at the same time, can manually control the generation method of reports and export detailed air quality reports and visualization charts. Specifically, the user interface module is used to provide a human-computer interaction interface, receive the position information and data information of multiple ventilation devices and relevant control instructions uploaded by relevant personnel, and send them to the decision-making engine module, and receive the data sent by the decision-making engine module and display the detailed data analysis report, visualization charts, three-dimensional model of the indoor place to be purified marked with relative position information, and ventilation control strategy in real time;

[0060] The intelligent control unit of the ventilation equipment is connected to the cloud processing platform, and it includes a monitoring sensor group, a driving module, a storage module, a data transmission module III, and a controller;

[0061] The monitoring sensor group is respectively connected to multiple ventilation equipment, and is used for collecting the operation parameters of the multiple ventilation equipment in real time and feeding them back to the controller; among them, the ventilation equipment includes but is not limited to fans, exhaust fans, air conditioners, etc. By collecting the operation parameters of the ventilation equipment, it is convenient to detect whether there is abnormal operation of the ventilation equipment, which is beneficial to give an alarm in time when the ventilation equipment is abnormal.

[0062] The driving module is respectively connected to multiple ventilation equipment, and is used for driving the corresponding ventilation equipment to act according to the driving control signal, including but not limited to starting and stopping of the ventilation equipment, operation mode and air volume adjustment. The driving module can be compatible with various ventilation equipment and can accurately control the operation state of each ventilation equipment.

[0063] The storage module is used for storing ventilation control strategies and the operation parameters of the ventilation equipment. Through this storage method, it can ensure that the intelligent control unit of the ventilation equipment can effectively control the actions of multiple ventilation equipment in the state of network disconnection, and thus can still have the indoor air purification ability in the offline state;

[0064] The controller is respectively connected to the monitoring sensor group, the driving module, the storage module, and the data transmission module III, and is used for sending the received ventilation control strategies and the operation parameters of the ventilation equipment to the storage module, uploading the operation parameters of the ventilation equipment to the decision engine module in the cloud processing platform, and sending corresponding driving control signals to the driving module according to the ventilation control strategies.

[0065] Furthermore, in order to be able to timely remind relevant personnel by means of sound alarm when an abnormality occurs, the intelligent control unit of the ventilation equipment further includes an alarm module connected to the controller. The controller is used for analyzing the operation parameters of multiple ventilation equipment, and controlling the alarm module to give an alarm when abnormal data is found. At the same time, abnormal data is uploaded to the cloud processing platform.

[0066] As a preference, the controller is a PLC controller.

[0067] In the present invention, multiple groups of fixed multi-dimensional air quality monitoring devices are respectively installed near each ventilation opening and near each window in the indoor place to be purified, multiple groups of portable multi-dimensional air quality monitoring devices are respectively worn on the bodies of different personnel in the indoor place to be purified, and the multi-dimensional air quality monitoring device includes a temperature monitoring module, a humidity monitoring module, CO 2The concentration monitoring module, VOC concentration monitoring module, CO concentration monitoring module, wind speed monitoring module, dust concentration monitoring module, and GPS location information module can comprehensively detect environmental parameters at different positions in an indoor venue using multi-dimensional air quality monitoring devices that are fixed in position and randomly movable, which is conducive to achieving accurate monitoring of indoor venue air quality data and helps to accurately predict future air quality. The indoor 3D modeling unit includes multiple groups of camera matrices arranged at different positions in the indoor venue to be purified, which can comprehensively collect image data of the indoor venue, thus facilitating the accurate acquisition of indoor space 3D point cloud data. The cloud server includes an air quality detection database and a 3D space reconstruction module, which can not only store the obtained environmental monitoring data, location information sent by the multi-dimensional air quality monitoring devices, and indoor 3D point cloud data, but also use the 3D space reconstruction module to generate an accurate 3D model of the indoor venue to be purified based on the indoor 3D point cloud data, and further obtain a 3D model of the indoor venue to be purified marked with relative location information. Thus, after obtaining air quality data at different positions, it can be displayed in the 3D model of the indoor venue to be purified, facilitating relevant personnel to understand the air quality situation in the indoor venue in real time through an intuitive display method. The cloud processing platform includes a data analysis and modeling module and a decision engine module, which can comprehensively analyze real-time data and historical data through the data analysis and modeling module to obtain air quality data at different positions in the current indoor venue and predict the future air quality change trend in the indoor venue, so as to formulate an optimal ventilation control strategy. This strategy can minimize the total energy consumption of multiple ventilation devices while ensuring the purification effect. The intelligent control unit of the ventilation device is connected to the cloud processing platform and multiple ventilation devices, which can facilitate the corresponding output of drive control instructions according to the ventilation control strategy of the cloud processing platform, so as to send corresponding drive control signals to the corresponding ventilation devices according to the ventilation control strategy, realizing the intelligent and low-energy consumption control process of the ventilation devices. Through the setting of the monitoring sensor group, the operation parameters of each ventilation device can be collected in real time and uploaded to the decision engine module in the cloud processing platform, which can facilitate the decision engine module to dynamically adjust the ventilation control strategy and then dynamically adjust the operation state of the ventilation devices, ensuring that the indoor air quality and energy consumption always maintain the optimal balance.

[0068] The present invention also provides an intelligent indoor ventilation and purification method based on a cloud platform, which uses an intelligent indoor ventilation and purification system based on a cloud platform, and is characterized by including the following steps:

[0069] Step 1: Arrange multiple groups of fixed multi-dimensional air quality monitoring devices at different positions in the indoor venue to be purified, supply power to the multiple groups of fixed multi-dimensional air quality monitoring devices, and at the same time, wear multiple groups of portable multi-dimensional air quality monitoring devices for different moving individuals in the indoor venue to be purified;

[0070] Arrange a spatial modeling unit in the indoor area to be purified, and supply power to the indoor 3D modeling unit. Among them, multiple groups of camera matrices are distributed at different positions in the indoor area to be purified;

[0071] Use the network to establish communication connections between multiple groups of fixed multi-dimensional air quality monitoring devices, multiple groups of portable multi-dimensional air quality monitoring devices, the indoor 3D modeling unit and the cloud server, use the network to establish a communication connection between the cloud server and the cloud processing platform, and use the network to establish a communication connection between the cloud processing platform and the intelligent control unit of the ventilation equipment; establish a communication connection between the intelligent control unit of the ventilation equipment and multiple ventilation equipment;

[0072] Step 2: Collect image data of the area to be purified from different angles and positions through multiple groups of camera matrices, and use a microcontroller to process the image data to obtain indoor space 3D point cloud data. Then, upload the indoor space 3D point cloud data to the cloud server through Data Transmission Module 1 and the network;

[0073] Use multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices to perform synchronous monitoring operations. For each multi-dimensional air quality monitoring device, through the temperature monitoring module, humidity monitoring module, CO 2 concentration monitoring module, VOC concentration monitoring module, CO concentration monitoring module, wind speed monitoring module, dust concentration monitoring module, and GPS location information module, collect temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO concentration data, wind speed data, dust concentration data, and location information at the location where it is located, and use a microcontroller to perform time series processing on the temperature data, humidity data, CO 2 concentration data, VOC concentration data, CO concentration data, wind speed data, and dust concentration data to obtain time series environmental monitoring data. Then, upload the time series environmental monitoring data and location information to the cloud server through Data Transmission Module 2 and the network;

[0074] Step 3: After receiving the indoor space 3D point cloud data, the cloud server stores it in the 3D space point cloud information storage area. After receiving the time series environmental monitoring data sent by each multi-dimensional air quality monitoring device, it stores it in the multi-dimensional air quality monitoring data storage area. After receiving the location information sent by each multi-dimensional air quality monitoring device, it stores it in the GPS information storage area;

[0075] Using a three-dimensional space reconstruction module to read the indoor space three-dimensional point cloud data in the three-dimensional space point cloud information storage area, read the location information in the GPS information storage area, generate a three-dimensional model of the indoor place to be purified based on the indoor space three-dimensional point cloud data, mark the relative positions of multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices in the constructed three-dimensional model of the indoor place to be purified based on the location information, and store the three-dimensional model of the indoor place to be purified marked with relative position information in the three-dimensional space point cloud information storage area;

[0076] Step 4: The cloud processing platform reads the time-series environmental monitoring data in the multi-dimensional air quality monitoring data storage area, reads the location information of the multi-dimensional air quality monitoring devices in the GPS information storage area, and the three-dimensional model of the indoor place to be purified marked with relative position information in the three-dimensional space point cloud information storage area through the data processing module, and after preprocessing the data, forms a unified integrated data set, and then sends the integrated data set to the data analysis and modeling module; Specifically, the data preprocessing includes data cleaning, data normalization and data fusion processing. The data cleaning removes noise and outliers, thereby ensuring the accuracy of the data; the data normalization converts data with different dimensions to the same scale for subsequent processing; the data integration integrates multi-source data (sensor monitoring data, historical data, etc.) into a unified data set, and can provide a comprehensive environmental information basis; at the same time, the location information and data information of multiple ventilation devices are input into the decision engine module through the user interface module, the operation parameters of multiple ventilation devices are collected in real time by the monitoring sensor group, and the controller uploads the operation parameters of multiple ventilation devices to the decision engine module in the cloud processing platform through the data transmission module three and the network;

[0077] The data analysis and modeling module uses spatio-temporal data analysis technology to capture the spatio-temporal characteristics of air quality changes from real-time integrated datasets and historical integrated datasets, identify and extract key influencing factor data; the physical model inside the data analysis and modeling module simulates the air flow and pollutant diffusion processes in indoor places based on the key influencing factor data, and combines the macroscopic fluid mechanics model and the microscopic particle dynamics model to comprehensively analyze the spatio-temporal changes in indoor air quality and obtain spatio-temporal change data of air quality; the machine learning model inside the data analysis and modeling module is trained according to the key influencing factor data and the spatio-temporal change data of air quality, and continuously improves the prediction accuracy and robustness through self-learning and optimization. Then, using time series analysis and machine learning algorithms, and based on the key influencing factor data and the spatio-temporal change data of air quality obtained in real time, it obtains the air quality data at different positions in the indoor place, predicts the future trend of air quality changes and potential high-concentration pollution events, and finally generates a detailed data analysis report and visualization charts. Then, it sends the detailed data analysis report, visualization charts, and model data of the three-dimensional model of the indoor place to be purified marked with relative position information to the decision engine module;

[0078] The decision engine module generates a preliminary control strategy through predefined rules and strategies, and based on the detailed data analysis report, visualization charts, model data of the three-dimensional model of the indoor place to be purified marked with relative position information, the position information and data information of multiple ventilation devices, and the operating parameters of multiple ventilation devices. It uses advanced optimization algorithms to optimize the preliminary control strategy to obtain the optimal ventilation control strategy that balances air quality and energy consumption, and then sends the ventilation control strategy to the intelligent control unit of the ventilation device. At the same time, it sends the detailed data analysis report, visualization charts, three-dimensional model of the indoor place to be purified marked with relative position information, and ventilation control strategy to the data storage and management module and the user interface module respectively for intuitive display through the user interface module. The user interface module can intuitively display the air quality at different positions in the room and the energy consumption status of each ventilation device based on the detailed data analysis report, visualization charts, and three-dimensional model of the indoor place to be purified marked with relative position information, which is conducive to providing a scientific basis for the decision-making of the system;

[0079] When it is predicted that a high pollution event may occur in a future period, it is added to the detailed data analysis report and visualization charts so that the decision engine module can adjust the ventilation control strategy in advance. In this way, by improving the prediction accuracy and early intervention, the response ability of the ventilation device can be further optimized;

[0080] As an optimization, during the process of the decision engine module formulating the preliminary control strategy, when the detailed data analysis report and visualization chart contain the possibility of high pollution concentration events in a certain future time period, the preliminary control strategy is adjusted in advance to prevent and reduce the impact of high pollution events.

[0081] As an optimization, the physical model inside the data analysis and modeling module is a computational fluid dynamics model (CFD), which can monitor data in real time and dynamically adjust parameters to ensure that the simulated results are consistent with the actual situation.

[0082] Step Five: After receiving the ventilation control strategy, the device intelligent control unit sends the control strategy to the storage module for storage, and sends corresponding drive control signals to the drive module according to the ventilation control strategy to control the operation of each ventilation device. Furthermore, the actions of multiple ventilation devices are used to purify the air quality of the indoor venue, and while ensuring the purification effect, the goal of minimizing the total energy consumption of multiple ventilation devices is achieved;

[0083] Synchronously, the operation parameters of multiple ventilation devices are collected in real time through the monitoring module and sent to the controller; the controller sends the operation parameters of multiple ventilation devices to the storage module for storage. At the same time, the operation parameters of multiple ventilation devices are uploaded to the decision engine module in the cloud processing platform, and the drive control signals are adjusted in real time according to the latest received ventilation control strategy.

[0084] In the present invention, image data of an indoor venue is collected by multiple camera matrices in the indoor 3D modeling unit, which facilitates obtaining accurate 3D point cloud information of the indoor venue and is conducive to the 3D space reconstruction module generating a high-precision 3D model of the indoor venue. The multi-dimensional air quality monitoring device can use multiple monitoring modules to real-time monitor various environmental parameters such as temperature, humidity, CO2 concentration, VOC concentration, CO concentration, wind speed, dust concentration, position information, etc., and through the processing of the second microcontroller, time-sequenced environmental monitoring data can be obtained. Using multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices for synchronous monitoring operations can not only collect environmental parameters at fixed position points but also use individuals with autonomous movement capabilities to collect environmental parameters at random position points. Thus, on the basis of arranging a relatively small number of multi-dimensional air quality monitoring devices, comprehensive collection of environmental parameters within the indoor venue can be achieved. The position information collected by the multi-dimensional air quality monitoring device is stored in the GPS information storage area. This facilitates the 3D space reconstruction module to read the position information data, and then the position of the multi-dimensional air quality monitoring device can be marked in the generated 3D model of the indoor venue, which is conducive to intuitively displaying the space of the indoor venue and also helps to more accurately analyze the air flow and pollutant diffusion paths, significantly improving the accuracy of environmental monitoring, prediction, and analysis. The data processing module in the cloud processing platform reads and preprocesses the data in the cloud server, which can ensure the quality and consistency of the data, and then a unified integrated data set that can comprehensively reflect the environmental characteristics can be formed, providing a reliable basis for subsequent analysis and decision-making. The cloud processing platform comprehensively analyzes real-time data and historical data through the data analysis and modeling module, comprehensively analyzes the spatio-temporal changes in the air quality of the indoor venue, then predicts the future air quality change trend, and generates detailed data analysis reports and visualization charts, so as to provide comprehensive data support for the decision-making of the decision engine module. At the same time, since high-pollution concentration events that may occur in the future can be predicted in advance, it is conducive to adjusting the ventilation strategy in advance and can prevent and reduce the impact of high-pollution events. The decision engine module generates a preliminary control strategy through detailed data analysis reports, visualization charts, model data of the 3D model of the indoor venue to be purified marked with relative position information, position information and data information of multiple ventilation devices, and operating parameters of multiple ventilation devices. It takes into account both the environmental conditions of the indoor venue itself and fully combines the position and capabilities of the ventilation devices, so that a scientific decision can be obtained. An advanced optimization algorithm is used to optimize the preliminary control strategy to obtain the optimal ventilation control strategy. In this way, it can respond to environmental changes in a timely manner according to real-time monitoring data and prediction results, provide a flexible and precise ventilation control method, and achieve the best balance between air quality and energy consumption.The detailed data analysis reports, visualization charts, three-dimensional models of indoor places to be purified marked with relative position information, and ventilation control strategies are intuitively displayed through the user interface module, which facilitates relevant personnel to understand the operation of the system in real time.

[0085] The present invention adopts advanced sensing technologies and intelligent algorithms to achieve comprehensive monitoring and optimized control of indoor air quality. This method, through the automated process of data processing, analysis, and decision-making, fully combines the advantages of intelligent algorithms and models. Through self-learning and optimization, it improves the accuracy and efficiency of air quality prediction and ventilation control, ensures the best balance between air quality and energy consumption under different environmental conditions, and significantly improves the indoor environmental quality and energy utilization efficiency.

Claims

1. An intelligent indoor ventilation and purification method based on a cloud platform, characterized in that: The following steps are involved: Step 1: Arrange multiple sets of fixed multi-dimensional air quality monitoring devices at different locations of the indoor place to be purified, and power the multiple sets of fixed multi-dimensional air quality monitoring devices. At the same time, different mobile individuals in the indoor place to be purified wear multiple sets of portable multi-dimensional air quality monitoring devices; Arranging a spatial modeling unit in an indoor place to be purified, and supplying power to an indoor three-dimensional modeling unit, wherein a plurality of camera matrices are distributed at different positions in the indoor place to be purified; Using the network to establish communication connections between multiple groups of fixed multi-dimensional air quality monitoring devices, multiple groups of portable multi-dimensional air quality monitoring devices, indoor three-dimensional modeling units and cloud servers, using the network to establish communication connections between cloud servers and cloud processing platforms, using the network to establish communication connections between cloud processing platforms and intelligent control units of ventilation equipment; establishing communication connections between intelligent control units of ventilation equipment and multiple ventilation equipment; multiple ventilation equipment are respectively installed in various vents and windows in indoor places to be purified; Step 2: Collect image data of the purification workplace from different angles and positions through multiple camera matrices, and use microcontroller 1 to process the image data to obtain three-dimensional point cloud data of the indoor space, and then upload the three-dimensional point cloud data of the indoor space to the cloud server through data transmission module 1 and the network; By using multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices for synchronous monitoring operations, environmental parameters can be collected at fixed locations, and individuals with autonomous mobility can be used to collect environmental parameters at random locations. Thus, on the basis of arranging a small number of multi-dimensional air quality monitoring devices, comprehensive collection of environmental parameters in indoor places can be achieved; for each multi-dimensional air quality monitoring device, the temperature data, humidity data, CO2 concentration data, VOC concentration data, CO concentration data, wind speed data, dust concentration data, and location information at the location are collected respectively through the temperature monitoring module, humidity monitoring module, CO2 concentration data, VOC concentration data, CO concentration data, wind speed data, dust concentration data, and location information, and the temperature data, humidity data, CO2 concentration data, VOC concentration data, CO concentration data, wind speed data, and dust concentration data are processed in time series by using the second microcontroller to obtain time-series environmental monitoring data, and then the time-series environmental monitoring data and location information are uploaded to the cloud server through the second data transmission module and the network; Step 3: After receiving the indoor space three-dimensional point cloud data, the cloud server stores it in the three-dimensional space point cloud information storage area, after receiving the time-series environmental monitoring data sent by each multi-dimensional air quality monitoring device, it stores it in the multi-dimensional air quality monitoring data storage area, and after receiving the location information sent by each multi-dimensional air quality monitoring device, it stores it in the GPS information storage area; The three-dimensional space reconstruction module is used to read the indoor space three-dimensional point cloud data in the three-dimensional space point cloud information storage area, read the position information in the GPS information storage area, and generate a three-dimensional model of the indoor place to be purified based on the indoor space three-dimensional point cloud data, and mark the relative positions of multiple groups of fixed multi-dimensional air quality monitoring devices and multiple groups of portable multi-dimensional air quality monitoring devices in the constructed three-dimensional model of the indoor place to be purified based on the position information, and store the three-dimensional model of the indoor place to be purified marked with the relative position information in the three-dimensional space point cloud information storage area; Step 4: The cloud processing platform reads the time-series environmental monitoring data in the multi-dimensional air quality monitoring data storage area, the position information of the multi-dimensional air quality monitoring device in the GPS information storage area, and the three-dimensional model of the indoor place to be purified marked with relative position information in the three-dimensional space point cloud information storage area through the data processing module, and forms a unified integrated data set after data preprocessing, and then sends the integrated data set to the data analysis and modeling module; at the same time, the user interface module is used to input the position information and data information of multiple ventilation equipment into the decision engine module, and the monitoring sensor group is used to collect the operating parameters of multiple ventilation equipment in real time. The controller uploads the operating parameters of multiple ventilation equipment to the decision engine module in the cloud processing platform through the data transmission module three and the network; The data analysis and modeling module uses spatiotemporal data analysis technology to capture the spatiotemporal characteristics of air quality changes from real-time integrated data sets and historical integrated data sets, and identifies and extracts key influencing factor data; the physical model inside the data analysis and modeling module simulates the air flow and pollutant diffusion process in indoor places based on the key influencing factor data, and combines the macro fluid mechanics model and the micro particle dynamics model to comprehensively analyze the spatiotemporal changes of air quality in indoor places and obtain the spatiotemporal change data of air quality; the machine learning model inside the data analysis and modeling module is trained based on the key influencing factor data and the spatiotemporal change data of air quality, and continuously improves the prediction accuracy and robustness through self-learning and optimization, and then uses time series analysis and machine learning algorithms to obtain air quality data at different locations in indoor places based on the key influencing factor data and the spatiotemporal change data of air quality obtained in real time, and predicts the trend of future air quality changes and potential high-concentration pollution events, and finally generates a detailed data analysis report and visualization charts, and then sends the detailed data analysis report, visualization charts and model data of the three-dimensional model of the indoor place to be purified with relative position information to the decision engine module; The decision engine module generates a preliminary control strategy through predefined rules and strategies, and based on detailed data analysis reports, visual charts, model data of the three-dimensional model of the indoor place to be purified marked with relative position information, location information and data information of multiple ventilation equipment, and operating parameters of multiple ventilation equipment, and uses advanced optimization algorithms to optimize the preliminary control strategy to obtain the optimal ventilation control strategy that balances air quality and energy consumption, and then sends the ventilation control strategy to the intelligent control unit of the ventilation equipment. At the same time, the detailed data analysis report, visual chart, the three-dimensional model of the indoor place to be purified marked with relative position information, and the ventilation control strategy are sent to the data storage and management module and the user interface module respectively, so as to be intuitively displayed through the user interface module; Step 5: After receiving the ventilation control strategy, the intelligent control unit of the ventilation equipment sends the control strategy to the storage module for storage, and sends a corresponding drive control signal to the drive module according to the ventilation control strategy to control the operation of each ventilation equipment, thereby using the actions of multiple ventilation equipment to purify the air quality of indoor places, and while ensuring the purification effect, achieve the goal of minimizing the total energy consumption of multiple ventilation equipment; Synchronously, the operating parameters of multiple ventilation devices are collected in real time through the monitoring module and sent to the controller; the controller sends the operating parameters of the multiple ventilation devices to the storage module for storage, and at the same time, uploads the operating parameters of the multiple ventilation devices to the decision engine module in the cloud processing platform, and adjusts the drive control signal in real time according to the latest received ventilation control strategy.

2. The intelligent indoor ventilation and purification method based on a cloud platform according to claim 1 is characterized in that: In step four, data preprocessing includes data cleaning, data normalization and data fusion processing.

3. The intelligent indoor ventilation and purification method based on a cloud platform according to claim 1 is characterized in that: In step 4, when the decision engine module formulates a preliminary control strategy, when the detailed data analysis report and visualization charts contain high pollution concentration events that may occur in a certain time period in the future, the preliminary control strategy is adjusted in advance to prevent and reduce the impact of high pollution events.

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