Fog gun eagle eye intelligent linkage control system

Abstract

The invention discloses a fog gun eagle-eye intelligent linkage control system, and relates to the technical field of environment monitoring and governance, the intelligent linkage control system is composed of a high-magnification eagle-eye camera, an environment sensor, fog gun equipment, an NPU module, a fog gun equipment linkage control module, an Internet of Things gateway and a central control system, and the NPU is a neural network processing unit; wherein the central control system is in communication connection with the high-magnification eagle-eye camera, the environment sensor and the fog gun equipment by taking the Internet of Things gateway as a center. By combining the high-performance AI processor, the intelligent linkage control algorithm and the Internet of Things technology, efficient cooperation and automatic response among equipment are achieved, the system can recognize a pollution source in real time, the spraying strategy can be dynamically adjusted according to the environment condition, the treatment efficiency and the resource utilization rate are greatly improved, and the system is suitable for large-scale popularization and application. By means of the intelligent improvement, the defects of traditional equipment are effectively overcome, and refined and low-cost pollution treatment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring and control, in particular to air pollution control and dust control, and in particular to a fog cannon Hawkeye intelligent linkage control system. Background Art

[0002] With the advancement of science and technology, especially the rapid development of technologies such as machine vision, data analysis, and artificial intelligence, it has provided strong technical support for the research and development and application of the fog cannon Eagle Eye intelligent linkage control system.

[0003] For example, Chinese patent publication number: CN115780122A discloses a redundant control system and method for automatic rotation safety protection of a fog cannon, including: a hydraulic control module, a limit sensing module and a controller; the hydraulic control module includes: a rotary hydraulic reversing valve of the fog cannon rotary motor, a limit oil circuit cut-off valve, an oil circuit limit release valve, a one-way valve, and a hydraulically controlled one-way valve.

[0004] In the existing technology, system faults are judged through fault detection programs and time control; the system is redundantly protected from three aspects: control logic, limit sensors, and hydraulic systems, to solve the problem that there are certain visual blind spots in the control of fog cannons. The fog cannons can only be observed through cameras, and faults cannot be discovered in time, which easily causes fog cannon accidents. However, since traditional environmental monitoring equipment and fog cannon devices are often separated and lack intelligent linkage mechanisms, data exchange and control scheduling between various devices are not efficient enough, and automated linkage control cannot be achieved. In addition, there is a lack of computer vision and deep learning algorithms for pollution source identification and analysis, making automated identification and positioning difficult to achieve, and it is impossible to provide accurate pollution control strategies, and thus it is impossible to achieve real-time linkage and scheduling of different equipment and areas. Summary of the Invention

[0005] The purpose of the present invention is to provide a fog cannon Hawkeye intelligent linkage control system to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: A fog cannon Hawkeye intelligent linkage control system, the intelligent linkage control system consists of a high-magnification Hawkeye camera, environmental sensors, fog cannon equipment, NPU module, fog cannon equipment linkage control module, Internet of Things gateway and central control system, NPU is a neural network processing unit; The central control system is centered on the Internet of Things gateway and is connected to the high-magnification Hawkeye camera, environmental sensors and fog cannon equipment; The high-magnification eagle-eye camera is used to capture video stream data in real time; The environmental sensor is used to collect air quality data, which includes PM2.5 and PM10 concentrations and wind speed and wind direction environmental parameters. The environmental sensor includes a PM2.5 concentration sensor, a PM10 concentration sensor, a wind speed concentration sensor, and a wind direction sensor; The NPU module uses a deep learning algorithm to analyze the location and type of pollution sources in video stream data and combines the results with environmental sensor data to identify the intensity and distribution of pollution sources; The fog cannon equipment linkage control module automatically adjusts the spraying strategy of the fog cannon equipment according to the output of the NPU module, performs precise spraying, and realizes fixed-point and quantitative spraying operation; The IoT gateway is responsible for data transmission and remote control.

[0007] A further improvement of the technical solution of the present invention is that the process of collecting the video stream data and the air quality data includes: Install high-magnification Eagle Eye cameras in key monitoring areas. The high-magnification Eagle Eye cameras are activated to monitor pollution sources in the area in real time, collect high-resolution video streams, capture image details of the pollution sources, and connect the video stream data to the central control system through the IoT gateway. Utilize the deployed PM2.5 concentration sensor, PM10 concentration sensor, wind speed sensor, and wind direction sensor to collect air quality data including PM2.5 and PM10 concentrations as well as wind speed and wind direction environmental parameters in real time, and connect the air quality data to the central control system through the IoT gateway; In the central control system, the incoming video stream data and air quality data are verified and corrected to ensure the accuracy and integrity of the data, and the pollution source data set is preprocessed. The preprocessing operation includes data cleaning and data format conversion steps. Through data cleaning, invalid or abnormal data generated by equipment failure, transmission errors, etc. are removed, and obviously erroneous data are corrected. Through data format conversion, the video stream data collected by the camera is converted into a format suitable for storage, and the data collected by the environmental sensor is converted into a unified format for subsequent processing; The preprocessed video stream data is integrated with the air quality data to form a complete pollution source data set.

[0008] A further improvement of the technical solution of the present invention is that: the NPU module includes an edge computing unit, a data processing and analysis unit, and an intelligent decision control unit, wherein the electrical signals between the units are connected; The edge computing unit runs a deep learning model and performs local data processing based on video stream data, identifying pollution sources in real time, and quickly processing data locally, reducing data transmission delays and improving system response speed and processing efficiency. The data processing and analysis unit, based on the pollution source identification results of the edge computing unit, combines the results with the environmental sensor data to identify the intensity and distribution of the pollution source, achieve accurate identification and positioning of the pollution source, and provide accurate decision-making basis for intelligent control; The intelligent decision-making control unit dynamically adjusts the spray angle, intensity, and coverage of the fog cannon based on air quality data and pollution source analysis results, using fuzzy control and PID control algorithms. This module ensures the system's response speed and spraying accuracy, achieving precise pollution control.

[0009] A further improvement of the technical solution of the present invention is that in the edge computing unit, the real-time identification process of the pollution source includes: Extract environmental image data of the monitored area from a public database, use the image annotation tool (LabelImg) to annotate the pollution source images, including the pollution source types of smoke, dust, and emissions, and divide the pollution source image dataset into training and test sets; Use the training set data to train the deep learning model and the pollution source identification model to enable it to identify the pollution source types of smoke, dust, and emissions. Use the test set data to evaluate the pollution source identification model and calculate indicators such as accuracy, recall rate, and F1 score. Adjust the model parameters based on the evaluation results. Deploy the trained pollution source identification model to the NPU, load the pre-processed video stream data to the edge computing unit, and extract the color, shape, and texture features related to pollution source identification from the pre-processed video stream data; Based on the extracted features, the pollution source identification model is used for real-time analysis, the pollution source matching index is calculated, the location of the pollution source in the video stream is identified, and the identification results are output, classified, and the pollution type is determined.

[0010] A further improvement of the technical solution of the present invention is that the calculation expression of the pollution source matching index is: ; in, is the pollution source matching index, The color feature matching score is a value between 0 and 1. The higher the value, the more the color feature matches the color of the known pollution source. It is the shape feature matching score, which is a value between 0 and 1. The higher the value, the more the shape feature matches the known pollution source shape. It is the texture feature matching score, which is a value between 0 and 1. The higher the value, the more the texture feature matches the texture of the known pollution source. is a constant used to adjust the sensitivity of the matching index. is the benchmark score of the color feature, which is used to normalize the color feature matching degree. The value range of is between 0 and 1. When , it means no pollution source is detected. , it means that the detected pollution source characteristics completely match the known pollution source.

[0011] A further improvement of the technical solution of the present invention is that: in the data processing and analysis unit, the process of identifying the intensity and distribution of pollution sources includes: Receive the pollution source identification results sent by the edge computing unit, including the location, type, and pollution degree evaluation index of the pollution source, extract real-time PM2.5 concentration and PM10 concentration data from the air quality data, and traverse the historical database to determine the baseline concentration value and maximum concentration value of PM2.5 concentration and PM10 concentration; The pollution source matching index is combined with PM2.5 and PM10 concentration data to calculate the pollution degree evaluation index and further analyze the pollution degree of the pollution source; Extract real-time wind speed and direction data from air quality data, analyze the impact of wind speed and direction on pollutant diffusion, determine the possible transmission paths of pollutants, and calculate the pollution source distribution coefficient by combining the pollution degree evaluation index and wind speed and direction data; Based on the pollution source distribution coefficient, the diffusion direction and speed of pollutants are determined to reflect the spatial distribution trend of pollution sources, and geographic information system tools are used to visualize the diffusion path and potential impact areas of pollutants.

[0012] A further improvement of the technical solution of the present invention is that the calculation expression of the pollution degree evaluation index is: ; in, is the pollution degree evaluation index, is the pollution source matching index, For real-time monitoring of PM2.5 concentration, The PM10 concentration is monitored in real time. is the baseline concentration value of PM2.5, used to normalize the concentration value. is the baseline concentration value of PM10, used to normalize the concentration value. is the maximum concentration value of PM2.5, which is used to calculate the exponential decay term. is the maximum concentration value of PM10, which is used to calculate the exponential decay term. The value range of is between 0 and 1. When , it means that no pollution source is detected or the pollution concentration is very low. , it means that the detected pollution source characteristics completely match the known pollution source.

[0013] A further improvement of the technical solution of the present invention is that the calculation expression of the pollution source distribution coefficient is: ; in, is the pollution source distribution coefficient, is the pollution degree evaluation index, is the wind speed, which is used to measure the diffusion rate of pollutants. is the reference wind speed value, used to normalize the wind speed. It is the angle between wind direction and pollutant diffusion direction, and is used to measure the effect of wind direction on pollutant diffusion. is the maximum wind direction angle, which is used to limit the influence of the exponential attenuation term. It is the angle between the wind direction and the due north direction, which is used to calculate the trigonometric function influence of the pollutant diffusion direction. When the wind direction is due north (or due south), When the wind is 0 degrees or 180 degrees, the sine value is 0, indicating that the pollutants will not spread in the east-west direction; when the wind is due east or due west, When the angle is 90 degrees, the sine value reaches its maximum, indicating that the diffusion intensity of pollutants in the east-west direction is the largest. The value range of is between 0 and 1. When , it means that there is no pollution diffusion or the pollution level is very low. When , it means that the pollutant diffusion conditions are optimal, the pollution level is high, and the wind speed and direction are most conducive to the diffusion of pollutants.

[0014] A further improvement of the technical solution of the present invention is that: in the intelligent decision-making control unit, the process of dynamically adjusting the spray angle, intensity and coverage of the fog cannon includes: Integrate real-time air quality data and pollution source analysis results, including the location, type, pollution level, pollutant diffusion direction and speed of the pollution source, and then preset the initial values ​​of the spray angle, intensity and coverage of the fog cannon equipment according to environmental conditions and pollution level. The initial values ​​serve as the starting point of the control process and are used for preliminary response to pollution conditions. Among them, the spray angle is determined according to the location of the pollution source, wind direction and wind speed, the spray intensity is adjusted according to the pollution level and pollution source type, and the coverage ensures that the pollution source and its potential diffusion area are covered; Design fuzzy control rules to convert qualitative air quality data and pollution source analysis results into fuzzy logic inputs for fog cannon equipment operation. Calculate preliminary spray parameters for the fog cannon equipment's spray angle, intensity, and coverage based on real-time data. Combined with a PID control algorithm, fine-tune the preliminary spray parameters of the fog cannon equipment to achieve more precise pollution control. The fuzzy controller provides qualitative control decisions, while the PID controller provides quantitative adjustments. The output results of the intelligent decision-making control unit are converted into specific parameters of the fog cannon's spray angle, intensity and coverage range, and a control command is generated and sent to the fog cannon equipment linkage control module.

[0015] A further improvement of the technical solution of the present invention is that: in the fog cannon equipment linkage control module, the process of adjusting the spraying strategy of the fog cannon equipment includes: Receive specific parameters of the spray angle, intensity, and coverage of the fog cannon equipment from the intelligent decision-making control module, and parse the received control instructions and convert them into specific parameters that the fog cannon equipment can understand and execute; Based on the analyzed parameters, the fog cannon equipment linkage control module automatically adjusts the status of the fog cannon equipment, including starting or stopping the equipment, adjusting the spray angle, controlling the spray intensity, etc. After the adjustment is completed, the fog cannon equipment performs fixed-point and quantitative spray operations according to the preset spraying strategy; The fog cannon equipment linkage control module monitors the operating status and spraying effect of the fog cannon equipment in real time during the spraying process, and feeds back the relevant information to the intelligent decision-making control module for further optimization and adjustment.

[0016] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art: The present invention provides a fog cannon Eagle Eye intelligent linkage control system. By combining a high-performance AI processor, an intelligent linkage control algorithm and Internet of Things technology, it achieves efficient collaboration and automated response between devices. The system can not only identify pollution sources in real time, but also dynamically adjust the spraying strategy according to environmental conditions, greatly improving the control efficiency and resource utilization. This intelligent improvement effectively overcomes the shortcomings of traditional equipment and realizes refined and low-cost pollution control.

[0017] The present invention provides a fog cannon Eagle Eye intelligent linkage control system. Through deep learning and computer vision technology, the system can accurately identify and locate pollution sources in complex scenarios, rather than simply spraying in an area. By performing video stream analysis, it can extract high-dimensional features from the video, achieve accurate identification of smoke and dust, and accurately locate the pollution source so that the fog cannon equipment can concentrate on spraying the pollution source area, reducing water waste and insufficient spraying coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 This is a product structure diagram of the present invention; Figure 2 This is a flow chart for real-time identification of pollution sources of the present invention; Figure 3 This is a flow chart for identifying the intensity and distribution of pollution sources of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Example 1, as Figure 1 、 Figure 2 As shown, the present invention provides a fog cannon Hawkeye intelligent linkage control system, which consists of a high-magnification Hawkeye camera, an environmental sensor, a fog cannon device, an NPU module, a fog cannon device linkage control module, an Internet of Things gateway, and a central control system. The NPU is a neural network processing unit. The central control system is centered on the IoT gateway and communicates with high-magnification Hawkeye cameras, environmental sensors, and fog cannon equipment; High-magnification Eagle Eye cameras are used to capture video streaming data in real time; Environmental sensors are used to collect air quality data, including PM2.5 and PM10 concentrations, as well as wind speed and direction environmental parameters. Environmental sensors include PM2.5 concentration sensors, PM10 concentration sensors, wind speed and concentration sensors, and wind direction sensors. The NPU module uses deep learning algorithms to analyze the location and type of pollution sources in video stream data and combines the results with environmental sensor data to identify the intensity and distribution of pollution sources; The fog cannon equipment linkage control module automatically adjusts the spraying strategy of the fog cannon equipment according to the output of the NPU module, performs precise spraying, and realizes fixed-point and quantitative spraying operations; The IoT gateway is responsible for data transmission and remote control; Furthermore, the process of collecting video stream data and air quality data includes: High-magnification Eagle Eye cameras are installed in key monitoring areas. When activated, they monitor pollution sources in the area in real time, collect high-resolution video streams, and capture image details of the pollution sources. The video stream data is connected to the central control system through the Internet of Things gateway. Using the deployed PM2.5 concentration sensors, PM10 concentration sensors, wind speed sensors, and wind direction sensors, air quality data, including PM2.5 and PM10 concentrations, as well as wind speed and wind direction environmental parameters, is collected in real time. The air quality data is connected to the central control system through the Internet of Things gateway. The central control system verifies and corrects the received video stream data and air quality data to ensure data accuracy and completeness, and preprocesses the pollution source data set. The preprocessing operation includes data cleaning and data format conversion steps. Data cleaning removes invalid or abnormal data caused by equipment failure, transmission errors, etc., and corrects obviously erroneous data. Data format conversion converts the video stream data collected by the camera into a format suitable for storage, and the data collected by the environmental sensors into a unified format for subsequent processing. The preprocessed video stream data is integrated with the air quality data to form a complete pollution source data set.

[0022] Example 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: preferably, the NPU module includes an edge computing unit, a data processing and analysis unit, and an intelligent decision control unit, wherein the electrical signals between the units are connected; The edge computing unit, by running a deep learning model, performs local data processing based on video stream data, identifies pollution sources in real time, processes data quickly locally, reduces data transmission delays, and improves the system's response speed and processing efficiency. It extracts environmental image data of the monitored area from the public database and uses the image annotation tool (LabelImg) to annotate the pollution source images, including the pollution source types of smoke, dust, and emissions. It also divides the pollution source image data set into a training set and a test set, and uses the training set data to test the deep learning model (YOLO or Faster The pollution source identification model is trained using a neural network (NPU) with R-CNN (Recurrent Neural Network) to identify pollution source types such as smoke, dust, and emissions. The pollution source identification model is evaluated using test data, and metrics such as accuracy, recall, and F1 score are calculated. Model parameters are adjusted based on the evaluation results, and the trained pollution source identification model is deployed on the NPU. The preprocessed video stream data is loaded into the edge computing unit. Color, shape, and texture features related to pollution source identification are extracted from the preprocessed video stream data. Based on the extracted features, the pollution source identification model is used for real-time analysis, calculating the pollution source matching index, identifying the location of pollution sources in the video stream, and outputting the identification results for classification to determine the pollution type. Furthermore, the calculation expression of the pollution source matching index is: ; in, is the pollution source matching index, The color feature matching score is a value between 0 and 1. The higher the value, the more the color feature matches the color of the known pollution source. It is the shape feature matching score, which is a value between 0 and 1. The higher the value, the more the shape feature matches the known pollution source shape. It is the texture feature matching score, which is a value between 0 and 1. The higher the value, the more the texture feature matches the texture of the known pollution source. is a constant used to adjust the sensitivity of the matching index. is the benchmark score of the color feature, which is used to normalize the color feature matching degree. The value range of is between 0 and 1. When , it means no pollution source is detected. When , it means that the detected pollution source characteristics completely match the known pollution source. 、 and The increase in pollution source matching index Increase, indicating that the confidence in the identification of the pollution source is improved. The use of radical and logarithmic functions increases the nonlinearity of the formula, making the pollution source matching index more sensitive to changes in the eigenvalue; The data processing and analysis unit, based on the pollution source identification results of the edge computing unit, combines the results with environmental sensor data to identify the intensity and distribution of pollution sources, achieve accurate identification and positioning of pollution sources, and provide accurate decision-making basis for intelligent control. It receives the pollution source identification results sent by the edge computing unit, including the location, type, and pollution level evaluation index of the pollution source, and extracts real-time PM2.5 and PM10 concentration data from the air quality data. It traverses the historical database to determine the baseline and maximum concentration values ​​of PM2.5 and PM10 concentrations. It combines the pollution source matching index with the PM2.5 and PM10 concentration-related data to calculate the pollution level evaluation index, further analyze the pollution level of the pollution source, extract real-time wind speed and direction data from the air quality data, analyze the impact of wind speed and wind direction on pollutant diffusion, and determine the possible transmission path of pollutants. It combines the pollution level evaluation index with wind speed and direction data to calculate the pollution source distribution coefficient. Based on the pollution source distribution coefficient, it determines the diffusion direction and speed of pollutants, reflecting the spatial distribution trend of pollution sources. It also uses geographic information system tools to visualize the diffusion path of pollutants and potential impact areas. Furthermore, the calculation expression of the pollution degree evaluation index is: ; in, is the pollution degree evaluation index, is the pollution source matching index, For real-time monitoring of PM2.5 concentration, The PM10 concentration is monitored in real time. is the baseline concentration value of PM2.5, used to normalize the concentration value. is the baseline concentration value of PM10, used to normalize the concentration value. is the maximum concentration value of PM2.5, which is used to calculate the exponential decay term. is the maximum concentration value of PM10, which is used to calculate the exponential decay term. The value range of is between 0 and 1. When , it means that no pollution source is detected or the pollution concentration is very low. When , it means that the detected pollution source characteristics completely match the known pollution source, and and are very high, with The increase in pollution level evaluation index Increase, indicating that the confidence level of pollution source identification is improved. and As the square term in the denominator increases, Decrease, indicating that the pollution concentration has a negative impact on the evaluation index, and the exponential decay term decreases with and decreases with the increase of The value of indicates that high pollution concentration has a significant negative impact on the evaluation index; Furthermore, the calculation expression of the pollution source distribution coefficient is: ; in, is the pollution source distribution coefficient, is the pollution degree evaluation index, is the wind speed, which is used to measure the diffusion rate of pollutants. is the reference wind speed value, used to normalize the wind speed. It is the angle between wind direction and pollutant diffusion direction, and is used to measure the effect of wind direction on pollutant diffusion. is the maximum wind direction angle, which is used to limit the influence of the exponential attenuation term. It is the angle between the wind direction and the due north direction, which is used to calculate the trigonometric function influence of the pollutant diffusion direction. When the wind direction is due north (or due south), When the wind is 0 degrees or 180 degrees, the sine value is 0, indicating that the pollutants will not spread in the east-west direction; when the wind is due east or due west, When the angle is 90 degrees, the sine value reaches its maximum, indicating that the diffusion intensity of pollutants in the east-west direction is the largest. The value range of is between 0 and 1. When , it means that there is no pollution diffusion or the pollution level is very low. When , it means the pollutant diffusion conditions are optimal, the pollution level is high and the wind speed and direction are most conducive to the diffusion of pollutants. The increase in pollution source distribution coefficient Increase, indicating that the higher the pollution level, the greater the possibility of pollutant diffusion. The increase of the radical term increases, resulting in Increase, indicating that the higher the wind speed, the faster the diffusion of pollutants. As the exponential decay term decreases, Decrease, indicating that the greater the angle between wind direction and pollutant diffusion direction, the more restricted the diffusion of pollutants. Indicates the effect of wind direction on the diffusion direction of pollutants. When the wind direction is due north, this item is 1, indicating that the diffusion direction of pollutants is the best. The intelligent decision-making control unit dynamically adjusts the spray angle, intensity and coverage of the fog cannon based on air quality data and pollution source analysis results, using fuzzy control and PID control algorithms. This module ensures the system's response speed and spraying accuracy, achieves precise pollution control, and integrates real-time air quality data and pollution source analysis results, including the location, type, pollution level, pollutant diffusion direction and speed of the pollution source, and then presets the initial values ​​of the spray angle, intensity and coverage of the fog cannon equipment according to environmental conditions and pollution level. The initial value serves as the starting point of the control process and is used to initially respond to pollution conditions. The spray angle is determined according to the location, wind direction and wind speed of the pollution source, and the spray intensity is determined according to the pollution level and Adjust the pollution source type and ensure that the coverage covers the pollution source and its potential diffusion area. Design fuzzy control rules to convert qualitative air quality data and pollution source analysis results into fuzzy logic inputs for fog cannon equipment operation. Calculate the preliminary spray parameters of the fog cannon equipment's spray angle, intensity, and coverage based on real-time data. Combined with the PID control algorithm, fine-tune the preliminary spray parameters of the fog cannon equipment to achieve more accurate pollution control. The fuzzy controller provides qualitative control decisions, while the PID controller provides quantitative adjustments. Convert the output results of the intelligent decision-making control unit into specific parameters of the fog cannon's spray angle, intensity, and coverage, and generate control commands to be sent to the fog cannon equipment linkage control module. In the fog cannon equipment linkage control module, the process of adjusting the fog cannon equipment spraying strategy includes: The specific parameters of the spray angle, intensity and coverage range of the fog cannon equipment are received from the intelligent decision-making control module, and the received control instructions are parsed and converted into specific parameters that the fog cannon equipment understands and executes. According to the parsed parameters, the fog cannon equipment linkage control module automatically adjusts the status of the fog cannon equipment, including starting or stopping the equipment, adjusting the spray angle, controlling the spray intensity, etc. After the adjustment is completed, the fog cannon equipment performs fixed-point and quantitative spray operations according to the preset spraying strategy. The fog cannon equipment linkage control module monitors the operating status and spraying effect of the fog cannon equipment in real time during the spraying process, and feeds back the relevant information to the intelligent decision-making control module for further optimization and adjustment.

[0023] Example 3, based on Examples 1-2, the present invention provides the following practical application effects and test data examples: A. In a test at an industrial park, compared to traditional manually controlled fog cannon equipment, this system's response time to sudden pollution source events was shortened by approximately 50%, with spraying initiated in 1.5 minutes, down from 3 minutes. In a dust control test, the system reduced PM10 concentration from 300 micrograms per cubic meter to 120 micrograms per cubic meter within 10 minutes, achieving a dust reduction efficiency of over 60%. Through automated control and self-learning optimization, the system saved 30% of water usage and approximately 40 hours of manual operation time during the one-month test period. B. In a simulated test environment, compared to traditional fog cannon equipment, this system reduced PM2.5 and PM10 concentrations by an average of 60% within 10 minutes of activation at the same dust concentration, compared to a 40% reduction for traditional equipment. In construction site applications, the system reduced water usage by approximately 30% over a one-month period and increased spraying efficiency by 25%. In actual use at an industrial park, the system was able to initiate spraying measures within one minute during pollution outbreaks (such as increased smoke and dust emissions), doubling the response speed of traditional systems. In an urban road dust control project, the system implemented remote automated control, reducing manual maintenance time by over 70% and ensuring continued stable operation at night and in adverse weather conditions. Furthermore, IoT technology and edge computing enable real-time fusion and processing of multiple sensor data, significantly shortening the response time for pollution detection and control. Edge computing technology enables devices to process data and execute controls locally, without relying on cloud computing, reducing data transmission latency. In experimental scenarios, compared to traditional manual control methods, the system was able to initiate spraying within 1-2 minutes of a change in pollution concentration, over 50% faster than traditional methods. The system's automated operation and self-learning capabilities significantly reduce the need for manual operation and maintenance costs. The intelligent control algorithm, combined with automated control devices, allows for unattended operation, reducing the manpower required for long-term operations. Field application feedback indicates that the system's use on construction sites can reduce manual operation time by over 50 hours per month and save approximately 30% of water consumption. Traditional fog cannon equipment relies on timed spraying and manual operation and cannot be adjusted in real time according to pollution conditions. This system can dynamically adjust the spray angle and intensity through intelligent analysis and adaptive control to achieve more refined treatment. Under the same environment, the pollutant concentration of this system is reduced by more than 20% faster than traditional fog cannon equipment. Other smart devices usually need to rely on cloud processing, which has certain network delays, especially in areas with unstable networks. This system is based on edge computing and performs fast processing locally, ensuring the device's immediate responsiveness. In industrial area applications with unstable networks, the system's response delay is reduced by about 30% compared to other devices, greatly improving processing efficiency.

[0024] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A fog cannon Hawkeye intelligent linkage control system, characterized in that: The intelligent linkage control system is composed of a high-magnification Hawkeye camera, an environmental sensor, a fog cannon device, an NPU module, a fog cannon device linkage control module, an Internet of Things gateway and a central control system, and the NPU is a neural network processing unit; The central control system is centered on the Internet of Things gateway and is connected to the high-magnification Hawkeye camera, environmental sensor and fog cannon equipment in communication; The high-magnification eagle-eye camera is used to capture video stream data in real time; The environmental sensor is used to collect air quality data, which includes PM2.5, PM10 concentration and wind speed and wind direction environmental parameters; The NPU module analyzes the location and type of pollution sources in the video stream data through a deep learning algorithm, and combines the results with environmental sensor data to identify the intensity and distribution of pollution sources; The fog cannon equipment linkage control module automatically adjusts the spraying strategy of the fog cannon equipment according to the output of the NPU module; The IoT gateway is responsible for data transmission and remote control.

2. According to claim 1, the intelligent linkage control system of the fog cannon Hawkeye is characterized in that: The process of collecting the video stream data and the air quality data includes: A high-magnification Hawkeye camera is installed in the monitoring area, the high-magnification Hawkeye camera is started, real-time monitoring of pollution sources in the area, collecting high-resolution video streams, and connecting the video stream data to the central control system through the Internet of Things gateway; Using the deployed PM2.5 concentration sensor, PM10 concentration sensor, wind speed sensor and wind direction sensor, real-time air quality data including PM2.5 and PM10 concentrations and wind speed and wind direction environmental parameters are collected, and the air quality data is connected to the central control system through the IoT gateway; Verify and correct the accessed video stream data and air quality data in the central control system, and preprocess the data of the pollution source data set, wherein the preprocessing operation includes data cleaning and data format conversion steps; The preprocessed video stream data is integrated with the air quality data to form a complete pollution source data set.

3. According to claim 2, a fog cannon Hawkeye intelligent linkage control system is characterized in that: The NPU module includes an edge computing unit, a data processing and analysis unit, and an intelligent decision control unit, wherein the electrical signals between the units are connected; The edge computing unit performs local data processing based on video stream data by running a deep learning model to identify pollution sources in real time; The data processing and analysis unit identifies the intensity and distribution of pollution sources based on the pollution source identification results of the edge computing unit and combines the results with the environmental sensor data; The intelligent decision-making control unit dynamically adjusts the spray angle, intensity and coverage of the fog cannon based on air quality data and pollution source analysis results using fuzzy control and PID control algorithms.

4. The intelligent linkage control system of the fog cannon Hawkeye according to claim 3 is characterized in that: In the edge computing unit, the real-time identification process of the pollution source includes: Extract environmental image data of the monitored area from the public database, use image annotation tools to annotate the pollution source images, including the pollution source types of smoke, dust, and emissions, and divide the pollution source image data set into training and test sets; Use the training set data to train the deep learning model and the pollution source identification model, and use the test set data to evaluate the pollution source identification model, and adjust the model parameters according to the evaluation results; Deploy the trained pollution source identification model to the NPU, load the preprocessed video stream data to the edge computing unit, and extract the color, shape, and texture features related to pollution source identification from the preprocessed video stream data; Based on the extracted features, the pollution source identification model is used for real-time analysis, the pollution source matching index is calculated, the location of the pollution source in the video stream is identified, and the identification results are output, classified, and the pollution type is determined.

5. The intelligent linkage control system of the fog cannon Hawkeye according to claim 4 is characterized in that: The calculation expression of the pollution source matching index is: ； in, is the pollution source matching index, is the color feature matching score, is the shape feature matching score, is the texture feature matching score, is a constant used to adjust the sensitivity of the matching index. is the benchmark score of the color feature, which is used to normalize the color feature matching degree. The value range is between 0 and 1. When , it means no pollution source is detected. , it means that the detected pollution source characteristics completely match the known pollution source.

6. The fog cannon Hawkeye intelligent linkage control system according to claim 5 is characterized in that: In the data processing and analysis unit, the process of identifying the intensity and distribution of pollution sources includes: Receive the pollution source identification results sent by the edge computing unit, including the location, type and pollution degree evaluation index of the pollution source, extract the real-time PM2.5 concentration and PM10 concentration data from the air quality data, and traverse the historical database to determine the baseline concentration value and maximum concentration value of PM2.5 concentration and PM10 concentration; The pollution source matching index is combined with PM2.5 and PM10 concentration-related data to calculate the pollution level evaluation index and further analyze the pollution level of the pollution source; Extract real-time wind speed and wind direction data from air quality data, analyze the impact of wind speed and wind direction on pollutant diffusion, determine the propagation path of pollutants, and calculate the pollution source distribution coefficient by combining the pollution degree evaluation index and wind speed and wind direction data; According to the pollution source distribution coefficient, the diffusion direction and speed of pollutants are determined to reflect the spatial distribution trend of pollution sources, and geographic information system tools are used to visualize the diffusion path and potential impact areas of pollutants.

7. The intelligent linkage control system of the fog cannon Hawkeye according to claim 6 is characterized in that: The calculation expression of the pollution degree evaluation index is: ； in, is the pollution degree evaluation index, is the pollution source matching index, For real-time monitoring of PM2.5 concentration, For real-time monitoring of PM10 concentration, is the baseline concentration value of PM2.5, is the baseline concentration value of PM10, is the maximum concentration value of PM2.5, which is used to calculate the exponential decay term. is the maximum concentration value of PM10, which is used to calculate the exponential decay term. The value range is between 0 and 1. When , it means no pollution source is detected. , it means that the detected pollution source characteristics completely match the known pollution source.

8. The intelligent linkage control system of the fog cannon Hawkeye according to claim 7 is characterized in that: The calculation expression of the pollution source distribution coefficient is: ； in, is the pollution source distribution coefficient, is the pollution degree evaluation index, is the wind speed, which is used to measure the diffusion rate of pollutants. is the reference wind speed value, is the angle between wind direction and pollutant diffusion direction, is the maximum wind direction angle, is the angle between the wind direction and due north, The value range is between 0 and 1. When , it means that there is no pollution diffusion, when When , it means that the pollutant diffusion conditions are optimal, the pollution level is high, and the wind speed and direction are most conducive to the diffusion of pollutants.

9. The fog cannon Hawkeye intelligent linkage control system according to claim 8 is characterized in that: In the intelligent decision control unit, the process of dynamically adjusting the spray angle, intensity and coverage of the fog cannon includes: Integrate real-time air quality data and pollution source analysis results, including the location, type, pollution level, pollutant diffusion direction and speed of the pollution source, and then preset the initial values ​​of the spray angle, intensity and coverage of the fog cannon equipment according to environmental conditions and pollution level; Design fuzzy control rules to convert qualitative air quality data and pollution source analysis results into fuzzy logic inputs for fog cannon equipment operation, calculate preliminary spray parameters of fog cannon equipment such as spray angle, intensity and coverage based on real-time data, and fine-tune preliminary spray parameters of fog cannon equipment in combination with PID control algorithm; The output results of the intelligent decision-making control unit are converted into specific parameters of the spray angle, intensity and coverage range of the fog cannon, and a control command is generated and sent to the linkage control module of the fog cannon equipment.

10. The fog cannon Hawkeye intelligent linkage control system according to claim 9, characterized in that: In the fog cannon equipment linkage control module, the process of adjusting the spraying strategy of the fog cannon equipment includes: Receive specific parameters of the spray angle, intensity and coverage of the fog cannon equipment from the intelligent decision control module, and parse the received control instructions and convert them into specific parameters that the fog cannon equipment understands and executes; According to the analyzed parameters, the fog cannon equipment linkage control module automatically adjusts the state of the fog cannon equipment. After the adjustment is completed, the fog cannon equipment performs fixed-point and quantitative spraying operations according to the preset spraying strategy; The fog cannon equipment linkage control module monitors the operating status and spraying effect of the fog cannon equipment in real time during the spraying process, and feeds back the relevant information to the intelligent decision-making control module for further optimization and adjustment.

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