Intelligent air quality monitoring, regulating and controlling system and method based on Mesh network

Abstract

The invention relates to the technical field of environment monitoring and pollution treatment, and discloses an intelligent air quality monitoring, regulating and controlling system and method based on a Mesh network, and the system comprises a phase extraction module which is used for extracting the phase offset of scattered sound waves when pollutant molecules generate vibration; the pollution positioning module is used for identifying an initially estimated pollution source position and a neighbor grid in the space grid by using the phase offset, and performing pollution source fine positioning on the space grid to obtain a pollution source position; the intensity calculation module is used for analyzing the pollution gradient in the space grid and calculating the causal intensity between the pollution source position and the air regulation and control equipment in the air monitoring area; the instruction generation module is used for generating an air regulation and control instruction of the air regulation and control equipment by utilizing the pollution gradient and the causal intensity; and the air regulation and control module is used for converting the air regulation and control instruction into an energy supply signal of air regulation and control equipment. According to the invention, the defect of double failures of pollution control response timeliness and spatial precision can be reduced.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and pollution control technology, and in particular to an intelligent air quality monitoring and control system and method based on a mesh network. Background Technology

[0002] Current air pollution monitoring and control technologies suffer from severe lags in pollution control, failing to meet the demands for real-time and precise regulation. Existing grid-based systems rely on traditional micro-stations with insufficient density and incomplete parameter coverage, resulting in monitoring blind spots. Furthermore, sensors exhibit zero drift, temperature drift, and time drift, requiring frequent manual calibration and failing to provide high-precision real-time pollution distribution maps. For example, a single particulate matter monitoring grid cannot simultaneously capture the synergistic pollution processes of ozone and VOCs, leading to misidentification of pollution sources, inaccurate source location, and a large error range, making centimeter-level control impossible. Existing positioning technologies rely on time-series data post-processing, requiring minute-level calculation cycles and failing to capture real-time pollution diffusion dynamics. Additionally, far-field environmental noise masks pollution signal characteristics, causing a decrease in near-field positioning signal-to-noise ratio. For instance, traditional phase analysis methods, due to sound wave scattering interference in complex urban environments, cannot resolve micron-level molecular vibrational state changes, resulting in delays in identifying pollution gradients and diffusion directions, missing optimal control windows. Existing control systems employ a fragmented monitoring-manual decision-making-execution model, with fan control only supporting fixed power levels, static traffic speed limits, and equipment maintenance relying on periodic filter replacements.

[0003] Therefore, the aforementioned defects create a vicious cycle of monitoring distortion, location delays, and extensive regulation, ultimately leading to a double failure in the timeliness and spatial accuracy of pollution control response. Summary of the Invention

[0004] This invention provides an intelligent air quality monitoring and control system based on a mesh network, the main purpose of which is to reduce the defects of both timeliness and spatial accuracy failure in pollution control response.

[0005] To achieve the above objectives, the present invention provides an intelligent air quality monitoring and control system based on a mesh network, the intelligent air quality monitoring and control system based on a mesh network comprising: a phase extraction module, a pollution location module, an intensity calculation module, an instruction generation module, and an air control module; The phase extraction module is used to divide the air monitoring area into various spatial grids, cause pollutant molecules in the spatial grids to vibrate, and use the sensor node at the center of the spatial grid to extract the phase shift of the scattered sound wave when the pollutant molecules vibrate. The pollution location module is used to identify the estimated pollution source location and neighboring grids in the spatial grid using the phase offset, and to perform fine pollution source location on the spatial grid based on the estimated pollution source location and the neighboring grids to obtain the pollution source location. The intensity calculation module is used to analyze the pollution gradient in the spatial grid and calculate the causal strength between the pollution source location and the air control equipment in the air monitoring area. The instruction generation module is used to generate air control instructions for the air control device using the pollution gradient and the causal intensity. The air control module is used to convert the air control command into a power supply signal for the air control device, and to realize air monitoring and control processing of the air monitoring area through the air control device according to the power supply signal.

[0006] Optionally, causing the pollutant molecules in the spatial grid to vibrate includes: At the center of each spatial grid, the sensor node synchronously transmits acoustic vortex signals belonging to the target frequency band to the spatial grid through the Mesh network; When the acoustic vortex signal reaches the pollutant molecule, it causes the pollutant molecule to vibrate.

[0007] Optionally, extracting the phase shift of the scattered sound wave when the pollutant molecules vibrate using the sensor node at the center of the spatial grid includes: In the sensor node, the phase offset between the acoustic vortex signal emitted by the sensor node and the scattered sound wave reflected by the pollutant molecules is calculated.

[0008] Optionally, the step of using the phase offset to identify the estimated location of the pollution source and neighboring grids in the spatial grid includes: Based on the phase offset, the pollution diffusion weight in the spatial grid is calculated using the following formula: ; in, This represents the pollution diffusion weight between the center of the i-th spatial grid and the center of the j-th spatial grid. The coincidence resonant frequency of the center of the i-th spatial grid. The kth phase offset, The coincidence resonant frequency of the center of the j-th spatial grid. The kth phase offset, This represents the spatial coordinates of the center of the i-th spatial grid. This represents the spatial coordinates of the center of the j-th spatial grid. Indicates distance according to Euclidean equation Weighting coefficients that increase but decay exponentially; Based on the phase offset, the pollution source is initially located using the spatial grid using the following formula to obtain the preliminary estimated pollution source location: ; in, This indicates the initial estimated location of the pollution source. The index representing the center of the spatial grid. This represents the phase offset of the center of the i-th spatial grid belonging to the l-th pollutant molecule and conforming to the r-th frequency. Indicates the number of pollutant molecules. Indicates the number of frequencies in the target frequency band. Select the grid index i with the largest sum of pollution intensities; Select target diffusion weights from the pollution diffusion weights that match the estimated location of the pollution source; When the target diffusion weight is greater than a preset weight threshold, the non-pollution source grid corresponding to the target diffusion weight is taken as the neighbor grid.

[0009] Optionally, the step of performing precise pollution source localization on the spatial grid based on the initially estimated pollution source location and the neighboring grid to obtain the pollution source location includes: Based on the preliminary estimated location of the pollution source, the center coordinates of the neighboring grid are compressed and deformed using the following formula to obtain the compressed and deformed location: ; in, Indicates the location of compression deformation. This indicates the initial estimated location of the pollution source. This represents the center coordinates of the neighboring grid with index j in the neighboring grid. A set of indices representing the neighboring grid; Based on the compression deformation location, the pollution source location is precisely determined using the following formula on the spatial grid: ; in, Indicates the location of the pollution source. This represents the normalized attention weights converted from the target diffusion weights.

[0010] Optionally, the analysis of the pollution gradient in the spatial grid includes: Obtain the phase offset; The total pollution concentration of each spatial grid is calculated using the phase offset. Based on the total pollution concentration, the pollution gradient in the spatial grid is calculated using the following formula: ; in, Indicates the pollution gradient. This represents the total pollution concentration in the i-th spatial grid. This represents the total pollution concentration in the j-th spatial grid. This represents the x-coordinate of the center of the i-th spatial grid. This represents the x-coordinate of the center of the j-th spatial grid. This represents the pollution diffusion weight between the center of the i-th spatial grid and the center of the j-th spatial grid. Represents the set of indices of neighboring grids. This represents the center coordinates of the neighboring grid with index j in the neighboring grid. This represents the center coordinates of the neighboring grid with index i in the neighboring grid.

[0011] Optionally, calculating the causal strength between the pollution source location and the air control equipment in the air monitoring area includes: The hyperbolic distance factor between the pollution source location and the air control equipment is calculated using the following formula: ; in, Represents the hyperbolic distance factor. Indicates the location of the pollution source. Indicates the location of the air conditioning equipment. express and Euclidean distance between them Represents normalization , Represents normalization ; Based on the hyperbolic distance factor, the hyperbolic distance factor is nonlinearly mapped to causal strength using the following formula: ; in, Indicates the strength of causality. Indicates the environmental degradation coefficient. This represents the inverse hyperbolic cosine function.

[0012] Optionally, generating air control commands for the air control device using the pollution gradient and the causal intensity includes: Divide the causal intensity into a first intensity interval, a second intensity interval, and a third intensity interval; When the causal intensity belongs to the first intensity range, the fan power of the air purifier in the air control equipment is calculated using the following formula based on the pollution gradient: ; in, Indicates the power of the fan. Indicates the pollution gradient. Indicates the power conversion factor. Indicates the effective cross-sectional area; When the causal intensity falls within the second intensity range, the speed limit value of the road intelligent transportation system in the air control device is calculated using the following formula based on the causal intensity: ; in, Indicates the speed limit value. Indicates the strength of causality; When the causal intensity falls within the third intensity range, the historical reliability of the monitoring equipment in the air control device is analyzed; The fan power, the speed limit value, and the historical reliability are used as air control commands.

[0013] Optionally, converting the air conditioning command into a power supply signal for the air conditioning device includes: Obtain the wind turbine power, speed limit value, and historical reliability mentioned above; Calculate the target current value of the air purifying fan in the air control equipment using the fan power; The speed limit value is used to calculate the traffic light frequency of the intelligent transportation system in the air conditioning device; The power outage delay of the monitoring equipment in the air control system is calculated using the historical reliability. The target current value, the traffic light frequency, and the power outage delay are used as the power supply signal.

[0014] This invention also provides a smart air quality monitoring and control method based on a mesh network, the method comprising: The air monitoring area is divided into various spatial grids, and pollutant molecules in the spatial grids are made to vibrate. The phase shift of the scattered sound waves when the pollutant molecules vibrate is extracted using the sensor node at the center of the spatial grid. The estimated pollution source location and neighboring grids in the spatial grid are identified using the phase offset. Based on the estimated pollution source location and the neighboring grids, the pollution source is precisely located in the spatial grid to obtain the pollution source location. Analyze the pollution gradient in the spatial grid and calculate the causal strength between the pollution source location and the air control equipment in the air monitoring area; The air control command for the air control device is generated using the pollution gradient and the causal intensity. The air control command is converted into a power supply signal for the air control device, and the air monitoring and control processing of the air monitoring area is realized through the air control device according to the power supply signal.

[0015] In contrast to the shortcomings of the prior art, this invention achieves refined mapping of pollution distribution through grid segmentation. The central sensor node covers the entire grid, avoiding monitoring blind spots and providing a high-precision, high-resolution real-time data foundation for pollution source localization. Furthermore, this invention excites pollutant molecules to resonate using acoustic vortex signals, enhancing signal strength. The phase offset accurately reflects the molecular vibration state, with sensitivity reaching the micrometer level. Further, this invention filters the grid with the highest pollution intensity and uses hyperbolic mapping compression deformation to compress far-field interference, improving near-field localization accuracy. Attention weight aggregation strengthens the contribution of highly correlated neighboring grids, reducing pollution source localization errors and achieving precise localization. Furthermore, this invention uses pollution gradients to reflect the direction and rate of pollution diffusion, integrates spatial attenuation characteristics using hyperbolic distance factors, and converts physical distance into control priorities using nonlinear mapping, establishing a quantitative control relationship between pollution sources and equipment to avoid ineffective energy consumption. This invention adapts to pollution levels through a graded strategy; the greater the pollution gradient, the higher the power. Traffic speed limits dynamically mitigate pollution diffusion, low-reliability sensors are shut down, and control commands are dynamically matched with pollution severity, thereby reducing energy consumption. This invention improves system reliability by converting control commands 100% into equipment actions. This invention can reduce the defects of both timeliness and spatial accuracy failure in pollution control response. Attached Figure Description

[0016] Figure 1 A functional block diagram of an intelligent air quality monitoring and control system based on a mesh network provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of air monitoring and control processing in an embodiment of the present invention, which provides an intelligent air quality monitoring and control method based on a Mesh network. Figure 3 This is a flowchart illustrating an intelligent air quality monitoring and control method based on a mesh network, as provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0019] In practice, the server-side equipment deployed in a mesh network-based intelligent air quality monitoring and control system may consist of one or more devices. This mesh network-based intelligent air quality monitoring and control system can be implemented as: a service instance, a virtual machine, and hardware devices. For example, this mesh network-based intelligent air quality monitoring and control system can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, this mesh network-based intelligent air quality monitoring and control system can be understood as software deployed on a cloud node, used to provide a mesh network-based intelligent air quality monitoring and control service to various user terminals. Alternatively, this mesh network-based intelligent air quality monitoring and control system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this mesh network-based intelligent air quality monitoring and control system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a mesh network-based intelligent air quality monitoring and control service to various user terminals.

[0020] In terms of implementation, the intelligent air quality monitoring and control system based on a mesh network and the user terminal are mutually compatible. That is, if the intelligent air quality monitoring and control system based on a mesh network is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the intelligent air quality monitoring and control system based on a mesh network is implemented as a website, then the user terminal is implemented as a webpage; or if the intelligent air quality monitoring and control system based on a mesh network is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0021] Reference Figure 1 The diagram shown is a functional block diagram of an intelligent air quality monitoring and control system based on a Mesh network provided in an embodiment of the present invention.

[0022] The intelligent air quality monitoring and control system 100 based on a mesh network described in this invention can be installed on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed on the cloud (e.g., a server or server cluster for intelligent air quality monitoring and control based on a mesh network), or it can be developed as a website. Depending on the functions implemented, the intelligent air quality monitoring and control system 100 based on a mesh network includes a phase extraction module 101, a pollution location module 102, an intensity calculation module 103, an instruction generation module 104, and an air control module 105.

[0023] In this embodiment of the invention, in the tracking of intelligent air quality monitoring and control based on a mesh network, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the intelligent air quality monitoring and control system based on a mesh network provided by this embodiment of the invention, the applicable scope of the intelligent air quality monitoring and control architecture based on a mesh network can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion. This allows for quick and flexible expansion of an intelligent air quality monitoring and control system based on a mesh network. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0024] The following describes, with reference to specific embodiments, each component and specific workflow of the intelligent air quality monitoring and control system based on Mesh networks.

[0025] The phase extraction module 101 is used to divide the air monitoring area into various spatial grids, causing pollutant molecules in the spatial grids to vibrate, and using the sensor node at the center of the spatial grid to extract the phase offset of the scattered sound wave when the pollutant molecules vibrate.

[0026] This invention achieves refined mapping of pollution distribution through grid segmentation, with central sensor nodes covering the entire grid, avoiding monitoring blind spots and providing a high-precision, high-resolution real-time data foundation for pollution source location.

[0027] In one embodiment of the present invention, the step of causing pollutant molecules in the spatial grid to vibrate includes: a sensor node at the center of each spatial grid synchronously transmitting an acoustic vortex signal belonging to a target frequency band to the spatial grid through a mesh network; and causing the pollutant molecules to vibrate when the acoustic vortex signal reaches the pollutant molecules.

[0028] Among them, the acoustic vortex signal refers to a helical sound wave carrying orbital angular momentum, the target frequency band refers to the resonant frequency range of pollutants, such as PM2.5: 28.3kHz, formaldehyde: 31.5kHz, and the sensor node refers to a device that emits acoustic vortex signals and extracts the phase shift of the scattered sound waves. For example, a piezoelectric sensor utilizes the piezoelectric effect. When the acoustic vortex signal reaches the pollutant molecules and causes them to vibrate, the scattered sound waves reflected by the pollutant molecules act on the piezoelectric sensor, causing deformation of the piezoelectric material and generating an electrical signal. By processing and analyzing the electrical signal, the phase shift can be calculated. An ultrasonic sensor can emit and receive ultrasonic signals. It can emit acoustic vortex signals. When the signal interacts with the pollutant molecules, it receives the scattered sound waves and calculates the phase difference between the emitted signal and the scattered sound waves through a signal processing circuit to obtain the phase shift.

[0029] Furthermore, in this embodiment of the invention, the resonance of pollutant molecules is excited by acoustic vortex signals, thereby enhancing the signal intensity. The phase shift accurately reflects the molecular vibration state, with a sensitivity down to the micrometer level.

[0030] In one embodiment of the present invention, the step of extracting the phase offset of the scattered sound wave when the pollutant molecule vibrates using the sensor node at the center of the spatial grid includes: calculating the phase offset between the acoustic vortex signal emitted by the sensor node and the scattered sound wave reflected by the pollutant molecule in the sensor node.

[0031] The scattered sound waves are sound waves that are scattered around after the pollutant molecules vibrate.

[0032] For example, the calculation of the phase shift between the acoustic vortex signal emitted by the sensor node and the scattered acoustic wave reflected by the pollutant molecules is as follows: Taking a piezoelectric sensor as an example, when an acoustic vortex signal is emitted, an initial electrical signal corresponding to the signal is generated. This signal has certain phase characteristics. When the scattered acoustic wave is received and converted into an electrical signal, the two electrical signals are compared by a phase detection circuit. The phase difference is obtained by using a phase difference calculation formula (such as based on Fourier transform), i.e., the phase shift.

[0033] The pollution location module 102 is used to identify the estimated pollution source location and neighboring grids in the spatial grid using the phase offset, and to perform fine-tuning of the pollution source location in the spatial grid based on the estimated pollution source location and the neighboring grids to obtain the pollution source location.

[0034] In one embodiment of the present invention, identifying the estimated location of a pollution source and its neighboring grid within the spatial grid using the phase offset includes: calculating the pollution diffusion weight within the spatial grid based on the phase offset using the following formula: ; in, This represents the pollution diffusion weight between the center of the i-th spatial grid and the center of the j-th spatial grid. The coincidence resonant frequency of the center of the i-th spatial grid. The kth phase offset, The coincidence resonant frequency of the center of the j-th spatial grid. The kth phase offset, This represents the spatial coordinates of the center of the i-th spatial grid. This represents the spatial coordinates of the center of the j-th spatial grid. Indicates distance according to Euclidean equation Weighting coefficients that increase but decay exponentially; Based on the phase offset, the pollution source is initially located using the spatial grid using the following formula to obtain the preliminary estimated pollution source location: ; in, This indicates the initial estimated location of the pollution source. The index representing the center of the spatial grid. This represents the phase offset of the center of the i-th spatial grid belonging to the l-th pollutant molecule and conforming to the r-th frequency. Indicates the number of pollutant molecules. Indicates the number of frequencies in the target frequency band. Select the grid index i with the largest sum of pollution intensities; Select a target diffusion weight that matches the estimated location of the pollution source from the pollution diffusion weights; when the target diffusion weight is greater than a preset weight threshold, use the non-pollution source grid corresponding to the target diffusion weight as a neighbor grid.

[0035] The set of neighboring grids is as follows: , The weight threshold can be preset or set according to the actual scenario. As the target diffusion weight, a preset weight threshold is used to determine the minimum association strength of neighboring meshes, conforming to the resonant frequency. The frequency points are all sampling frequency points near the resonant frequency.

[0036] Furthermore, in this embodiment of the invention, far-field interference is compressed by selecting the grid with the highest pollution intensity and using hyperbolic mapping compression deformation, which improves near-field positioning accuracy. Attention weight aggregation enhances the contribution of highly correlated neighbor grids, thereby reducing pollution source positioning error and achieving accurate positioning.

[0037] In one embodiment of the present invention, the step of performing precise pollution source localization on the spatial grid based on the initially estimated pollution source location and the neighboring grid to obtain the pollution source location includes: compressing and deforming the center coordinates of the neighboring grid according to the initially estimated pollution source location using the following formula. The location of compression deformation is obtained: ; in, Indicates the location of compression deformation. This indicates the initial estimated location of the pollution source. This represents the center coordinates of the neighboring grid with index j in the neighboring grid. A set of indices representing the neighboring grid; Based on the compression deformation location, the pollution source location is precisely determined using the following formula on the spatial grid: ; in, Indicates the location of the pollution source. This represents the normalized attention weights converted from the target diffusion weights.

[0038] in, The calculation formula is: ; in, For the target diffusion weight, This represents the diffusion weights between all neighboring grids and the initially estimated pollution source location. This represents the adjustment factor, used to adjust the strength of the diffusion weights in the calculation process. When the target diffusion weight is large, its influence is amplified, causing larger diffusion weights to occupy a more significant position in the normalized result. When the weights are smaller, their influence is weakened, thus reducing the impact of the differences between the weights on the normalization result.

[0039] The intensity calculation module 103 is used to analyze the pollution gradient in the spatial grid and calculate the causal strength between the pollution source location and the air control equipment in the air monitoring area.

[0040] In one embodiment of the present invention, analyzing the pollution gradient in the spatial grid includes: obtaining a phase offset; calculating the total pollution concentration of each spatial grid using the phase offset; and calculating the pollution gradient in the spatial grid based on the total pollution concentration using the following formula: ; in, Indicates the pollution gradient. This represents the total pollution concentration in the i-th spatial grid. This represents the total pollution concentration in the j-th spatial grid. This represents the x-coordinate of the center of the i-th spatial grid. This represents the x-coordinate of the center of the j-th spatial grid. This represents the pollution diffusion weight between the center of the i-th spatial grid and the center of the j-th spatial grid. Represents the set of indices of neighboring grids. This represents the center coordinates of the neighboring grid with index j in the neighboring grid. This represents the center coordinates of the neighboring grid with index i in the neighboring grid.

[0041] Furthermore, in this embodiment of the invention, the pollution gradient reflects the direction and rate of pollution diffusion, the hyperbolic distance factor integrates spatial attenuation characteristics, and the nonlinear mapping converts physical distance into control priority, thereby establishing a quantitative control relationship between pollution source and equipment and avoiding ineffective energy consumption.

[0042] In one embodiment of the present invention, calculating the causal strength between the pollution source location and the air control equipment in the air monitoring area includes: calculating the hyperbolic distance factor between the pollution source location and the air control equipment using the following formula: ; in, Represents the hyperbolic distance factor. Indicates the location of the pollution source. Indicates the location of the air conditioning equipment. express and Euclidean distance between them Represents normalization , Represents normalization ; Based on the hyperbolic distance factor, the hyperbolic distance factor is nonlinearly mapped to causal strength using the following formula: ; in, Indicates the strength of causality. Indicates the environmental degradation coefficient. This represents the inverse hyperbolic cosine function.

[0043] in, Obtained by fitting real-time data from weather stations, Divide by the maximum radius of the monitoring area to obtain ,Will Divide by the maximum radius of the monitoring area to obtain .

[0044] The instruction generation module 104 is used to generate air control instructions for the air control device using the pollution gradient and the causal intensity.

[0045] The embodiments of the present invention adapt to the degree of pollution through a graded strategy, that is, the greater the pollution gradient, the higher the power, the traffic speed limit dynamically alleviates the spread of pollution, low-reliability sensors are turned off, and the control commands are dynamically matched with the severity of pollution, thereby reducing energy consumption.

[0046] In one embodiment of the present invention, generating air control commands for the air control device using the pollution gradient and the causal intensity includes: dividing the causal intensity into a first intensity range, a second intensity range, and a third intensity range; when the causal intensity belongs to the first intensity range, calculating the fan power of the air purifier fan in the air control device according to the pollution gradient using the following formula: ; in, Indicates the power of the fan. Indicates the pollution gradient. Indicates the power conversion factor. Indicates the effective cross-sectional area; When the causal intensity falls within the second intensity range, the speed limit value of the road intelligent transportation system in the air control device is calculated using the following formula based on the causal intensity: ; in, Indicates the speed limit value. Indicates the strength of causality; When the causal intensity falls within the third intensity range, the historical reliability of the monitoring equipment in the air control device is analyzed; the fan power, the speed limit value, and the historical reliability are used as air control commands.

[0047] The monitoring equipment refers to devices that monitor pollution levels, such as sensors. The effective cross-sectional area refers to the cross-sectional area of ​​the effective airflow channel that actually participates in air purification when the air purifier fan is working. The formula for calculating historical reliability is: ; in, Indicates historical credibility.

[0048] The air control module 105 is used to convert the air control command into a power supply signal for the air control device, and to realize air monitoring and control processing of the air monitoring area through the air control device according to the power supply signal.

[0049] This invention improves system reliability by converting control commands into 100% device actions.

[0050] In one embodiment of the present invention, converting the air conditioning command into a power supply signal for the air conditioning device includes: acquiring the fan power, speed limit value, and historical reliability; calculating the target current value of the air purifier fan in the air conditioning device using the fan power; calculating the traffic light frequency of the intelligent transportation system in the air conditioning device using the speed limit value; calculating the power-off delay of the monitoring device in the air conditioning device using the historical reliability; and using the target current value, the traffic light frequency, and the power-off delay as the power supply signal.

[0051] Please refer to Table 1 below for a schematic diagram of the power supply signals:

[0052] See Figure 2 The diagram shown is a schematic representation of air monitoring and control processing in an embodiment of the intelligent air quality monitoring and control method based on a mesh network provided by the present invention. Figure 2 In this process, the output current magnitude and waveform are adjusted by controlling parameters such as the switching frequency and duty cycle of the IGBT power module (adjusted to the target current value, i.e., the fan power supply signal). This output current is input into the air purifier fan. After calculating the traffic light frequency (traffic power supply signal), the reciprocal of the traffic light frequency is taken to obtain the period. The alternation duration of the traffic light is calculated using the period. For example, the red light duration T1 = 0.6T, and the green light duration T2 = 0.4T. Low-reliability devices refer to devices with historical reliability below the threshold. For these devices, after calculating the power-off delay (sensor power supply signal), wait... After a few seconds (a safe shutdown buffer period), these low-trust devices will be shut down.

[0053] In contrast to the shortcomings of the prior art, this invention achieves refined mapping of pollution distribution through grid segmentation. The central sensor node covers the entire grid, avoiding monitoring blind spots and providing a high-precision, high-resolution real-time data foundation for pollution source localization. Furthermore, this invention excites pollutant molecules to resonate using acoustic vortex signals, enhancing signal strength. The phase offset accurately reflects the molecular vibration state, with sensitivity reaching the micrometer level. Further, this invention filters the grid with the highest pollution intensity and uses hyperbolic mapping compression deformation to compress far-field interference, improving near-field localization accuracy. Attention weight aggregation strengthens the contribution of highly correlated neighboring grids, reducing pollution source localization errors and achieving precise localization. Furthermore, this invention uses pollution gradients to reflect the direction and rate of pollution diffusion, integrates spatial attenuation characteristics using hyperbolic distance factors, and converts physical distance into control priorities using nonlinear mapping, establishing a quantitative control relationship between pollution sources and equipment to avoid ineffective energy consumption. This invention adapts to pollution levels through a graded strategy; the greater the pollution gradient, the higher the power. Traffic speed limits dynamically mitigate pollution diffusion, low-reliability sensors are shut down, and control commands are dynamically matched with pollution severity, thereby reducing energy consumption. This invention improves system reliability by converting control commands 100% into equipment actions. This invention can reduce the defects of both timeliness and spatial accuracy failure in pollution control response.

[0054] like Figure 3 The diagram shown is a flowchart illustrating a mesh network-based intelligent air quality monitoring and control method according to an embodiment of the present invention. In this embodiment, the mesh network-based intelligent air quality monitoring and control method includes: The air monitoring area is divided into various spatial grids, and pollutant molecules in the spatial grids are made to vibrate. The phase shift of the scattered sound waves when the pollutant molecules vibrate is extracted using the sensor node at the center of the spatial grid. The estimated pollution source location and neighboring grids in the spatial grid are identified using the phase offset. Based on the estimated pollution source location and the neighboring grids, the pollution source is precisely located in the spatial grid to obtain the pollution source location. Analyze the pollution gradient in the spatial grid and calculate the causal strength between the pollution source location and the air control equipment in the air monitoring area; The air control command for the air control device is generated using the pollution gradient and the causal intensity. The air control command is converted into a power supply signal for the air control device, and the air monitoring and control processing of the air monitoring area is realized through the air control device according to the power supply signal.

[0055] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0056] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A Mesh network-based intelligent air quality monitoring and regulation system, characterized in that, The intelligent air quality monitoring and regulation system based on a Mesh network comprises a phase extraction module, a pollution positioning module, an intensity calculation module, an instruction generation module and an air regulation module. The phase extraction module is configured to divide an air monitoring area into spatial grids, make pollutant molecules in the spatial grids vibrate, and extract phase offsets of scattered sound waves when the pollutant molecules vibrate by using sensor nodes at the centers of the spatial grids. The pollution positioning module is configured to identify an estimated pollution source position in the spatial grids and neighbor grids by using the phase offsets, perform fine positioning of a pollution source in the spatial grids according to the estimated pollution source position and the neighbor grids, and obtain a pollution source position. The intensity calculation module is configured to analyze a pollution gradient in the spatial grids, and calculate a causal intensity between the pollution source position and an air regulation device in the air monitoring area. The instruction generation module is configured to generate air regulation instructions of the air regulation device by using the pollution gradient and the causal intensity. The air regulation module is configured to convert the air regulation instructions into energy supply signals of the air regulation device, and perform air monitoring and regulation processing of the air monitoring area by the air regulation device according to the energy supply signals. 2.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The making of the pollutant molecules in the spatial grids vibrate comprises: The sensor nodes at the centers of each spatial grid synchronously emit sound vortex signals belonging to a target frequency band to the spatial grids through a Mesh network; When the sound vortex signals reach the pollutant molecules, the pollutant molecules vibrate. 3.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The extraction of the phase offsets of the scattered sound waves when the pollutant molecules vibrate comprises: In the sensor nodes, the phase offsets between the sound vortex signals emitted by the sensor nodes and the scattered sound waves reflected by the pollutant molecules are calculated. 4.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The identification of the estimated pollution source position in the spatial grids and the neighbor grids by using the phase offsets comprises: According to the phase offsets, a pollution diffusion weight in the spatial grids is calculated by using the following formula: ； wherein, represents a pollution diffusion weight between the center of the i-th spatial grid and the center of the j-th spatial grid, represents a k-th phase offset of the resonance frequency at the center of the i-th spatial grid, represents a k-th phase offset of the resonance frequency at the center of the j-th spatial grid, represents a spatial coordinate of the center of the i-th spatial grid, represents a spatial coordinate of the center of the j-th spatial grid, represents a weight coefficient that exponentially decays with an increase in the Euclidean distance . According to the phase offsets, the fine positioning of the pollution source in the spatial grids is performed by using the following formula to obtain the estimated pollution source position: ； wherein, represents an estimated pollution source position, represents an index of the center of the spatial grid, represents a phase offset amount of the i-th spatial grid center belonging to the l-th pollutant molecule and satisfying the r-th frequency, represents the number of pollutant molecules, represents the number of frequencies in the target frequency band, selects the grid index i with the maximum pollution intensity sum; A target diffusion weight conforming to the estimated pollution source position is selected from the pollution diffusion weights; When the target diffusion weight is greater than a preset weight threshold, a non-pollution source grid corresponding to the target diffusion weight is taken as a neighbor grid. 5.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The fine positioning of the pollution source in the spatial grids according to the estimated pollution source position and the neighbor grids to obtain the pollution source position comprises: According to the estimated pollution source position, a center coordinate of the neighbor grid is compressed and deformed by using the following formula to obtain a compressed and deformed position: ； wherein, denotes the position of the compression deformation, denotes the position of the estimated pollution source, denotes the center coordinate of the neighbor grid with index j in the neighbor grid, denotes the index set of the neighbor grids; According to the compressed and deformed position, the fine positioning of the pollution source in the spatial grids is performed by using the following formula to obtain the pollution source position. ； wherein, represents a pollution source location, represents a normalized attention weight converted from a target diffusion weight. 6.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The analysis of the pollution gradient in the spatial grids comprises: The phase offsets are obtained; The total pollution concentration of each spatial grid is calculated by using the phase offsets; Based on the total concentration of pollution, the pollution gradient in the spatial grid is calculated using the following formula: ； wherein, represents the pollution gradient, represents the total concentration of pollution of the i-th spatial grid, represents the total concentration of pollution of the j-th spatial grid, represents the abscissa of the center of the i-th spatial grid, represents the abscissa of the center of the j-th spatial grid, represents the pollution diffusion weight between the center of the i-th spatial grid and the center of the j-th spatial grid, represents the index set of the neighbor grids, represents the center coordinate of the neighbor grid with index j in the neighbor grids, represents the center coordinate of the neighbor grid with index i in the neighbor grids. 7.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The calculation of the causal strength between the pollution source position and the air conditioning equipment in the air monitoring area includes: The hyperbolic distance factor between the pollution source position and the air conditioning equipment is calculated using the following formula: ； wherein denotes a hyperbolic distance factor, denotes a pollution source location, denotes a location of an air conditioning device, denotes the Euclidean distance between , denotes a normalized , denotes a normalized ; Based on the hyperbolic distance factor, the hyperbolic distance factor is nonlinearly mapped to the causal strength using the following formula: ； wherein denotes the causal strength, denotes the environmental decay coefficient, denotes the inverse hyperbolic cosine function. 8.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The air conditioning instructions for the air conditioning equipment are generated using the pollution gradient and the causal strength, including: Divide the first, second and third strength intervals of the causal strength; When the causal strength belongs to the first strength interval, the fan power of the air purification fan in the air conditioning equipment is calculated according to the pollution gradient using the following formula: ； wherein, represents the fan power, represents the pollution gradient, represents the power conversion factor, represents the effective cross-sectional area; When the causal strength belongs to the second strength interval, the speed limit value of the intelligent road traffic system in the air conditioning equipment is calculated according to the causal strength using the following formula: ； wherein represents a speed limit value, represents a causal strength; When the causal strength belongs to the third strength interval, the historical reliability of the monitoring equipment in the air conditioning equipment is analyzed; The fan power, speed limit value and historical reliability are used as air conditioning instructions. 9.The Mesh network based intelligent air quality monitoring and regulating system according to claim 1, wherein, The air conditioning instructions are converted into energy supply signals for the air conditioning equipment, including: Obtain the fan power, speed limit value and historical reliability in the air conditioning equipment; The target current value of the air purification fan in the air conditioning equipment is calculated using the fan power; The traffic signal light frequency of the intelligent road traffic system in the air conditioning equipment is calculated using the speed limit value; The historical reliability is used to calculate the power-off delay of the monitoring equipment in the air conditioning equipment; The target current value, traffic signal light frequency and power-off delay are used as energy supply signals. 10.A method for intelligent air quality monitoring and regulation based on a Mesh network, the method being applied in the intelligent air quality monitoring and regulation system based on a Mesh network according to any one of claims 1-9, characterized in that, The method includes: Divide the air monitoring area into spatial grids, make the pollutant molecules in the spatial grid vibrate, and use the sensor nodes at the center of the spatial grid to extract the phase shift of the scattered sound waves when the pollutant molecules vibrate; The initial estimated pollution source position and the neighbor grid in the spatial grid are identified using the phase shift, and the spatial grid is precisely positioned according to the initial estimated pollution source position and the neighbor grid to obtain the pollution source position; Analyze the pollution gradient in the spatial grid, calculate the causal strength between the pollution source position and the air conditioning equipment in the air monitoring area; Air conditioning instructions for the air conditioning equipment are generated using the pollution gradient and the causal strength; The air conditioning instructions are converted into energy supply signals for the air conditioning equipment, and the air monitoring and control processing of the air monitoring area is realized through the air conditioning equipment according to the energy supply signals.

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