Atmospheric environment monitoring and early warning method and device

Abstract

The invention discloses an atmospheric environment monitoring and early warning method and device, and relates to the technical field of atmospheric environment monitoring and early warning, and the method specifically comprises the following steps: obtaining the traffic environment state information on a main road in real time, and evaluating the urgency degree of adjusting the data collection time interval of sensor equipment on the main road; based on evaluation and analysis results, an early warning and acquisition interval adjustment mechanism is constructed and applied, and corresponding early warning and acquisition interval adjustment measures are taken for different urgency conditions; and response feedback information of the sensor equipment on the main road is obtained in real time and analyzed after being obtained, and whether the adjustment degree of the mechanism on the data acquisition time interval of the sensor equipment on the main road meets the preset response requirement or not is evaluated. According to the invention, the problem of monitoring lag of rapid accumulation of pollutants in the traffic peak period is solved, and the technical effects of dynamically adjusting the acquisition interval and accurately triggering pollution early warning are realized.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric environment monitoring and early warning technology, and in particular to an atmospheric environment monitoring and early warning method and device. Background Art

[0002] Atmospheric environment refers to the composition and state of air from the surface of the earth to outer space, including various gases, particulate matter, water vapor and suspended pollutants in the atmosphere. It not only directly affects the quality of life and health of human beings, but also has a profound impact on ecosystems, climate change and economic development. However, with the acceleration of industrialization and the improvement of urbanization, the types and concentrations of pollutants in the air have increased significantly, leading to the deterioration of air quality and posing a serious threat to human health and environmental safety. In particular, the excessive concentrations of pollutants such as particulate matter, sulfur dioxide, nitrogen oxides, and volatile organic compounds may cause respiratory diseases, cardiovascular diseases, and even affect the balance of the entire ecosystem. Therefore, it is particularly important to monitor and warn the atmospheric environment. Through real-time monitoring of the atmospheric environment, pollution sources and pollution diffusion trends can be discovered in a timely manner, and the potential harm of pollution to the environment and human body can be assessed. The monitoring results can not only provide scientific basis for relevant departments to take emission reduction or governance measures in a timely manner, but also remind the public to pay attention to changes in air quality through the early warning system, avoid going out when pollution is serious, and reduce health risks. At the same time, early warning measures can help enterprises and governments plan and formulate emergency plans in advance to reduce the economic and social impacts of sudden pollution incidents. Therefore, monitoring and early warning of the atmospheric environment are important means to protect public health, maintain the ecological environment, and promote sustainable development.

[0003] Existing atmospheric environment monitoring and early warning technologies achieve early warning by comprehensively using sensor networks, big data analysis and meteorological models. First, sensor devices distributed in the monitoring area collect multiple types of environmental data such as particulate matter (such as PM2.5, PM10), gaseous pollutants (such as sulfur dioxide, nitrogen oxides, volatile organic compounds), temperature, humidity, wind speed and direction in the air in real time. These data are transmitted to the central processing system via wireless networks or wired networks. The system preprocesses and analyzes the data to remove noise and outliers. Next, based on meteorological models and pollutant diffusion models, the monitoring system uses complex mathematical formulas to calculate the pollution diffusion coefficient and cumulative risk index, and combines meteorological conditions (such as wind direction and temperature changes) to predict the diffusion trend of pollutants. By comparing with the preset risk threshold, the system generates an early warning assessment coefficient. If the assessment coefficient exceeds the threshold, the system will automatically issue a pollution warning signal to alert relevant departments and the public, and recommend appropriate measures, such as reducing outdoor activities, turning on air purification equipment or starting pollution source control measures. The entire early warning process relies on real-time data collection, calculation of complex models, and rapid response of multi-party collaboration to ensure that changes in the atmospheric environment can be monitored and warned in a timely and effective manner.

[0004] The prior art has the following deficiencies: On the main roads of some cities, especially during peak traffic hours and on roads with dense traffic lights, due to frequent vehicle starts and stops, pollutants emitted by a large number of vehicles accumulate rapidly in a short period of time, causing a sharp increase in pollutant concentrations in local areas. Since this accumulation phenomenon occurs in a short period of time, the existing atmospheric monitoring system relies on collecting data at fixed time intervals and cannot track the instantaneous changes of these pollutants in real time. Therefore, the existing technology fails to capture the short-term pollution peak during peak traffic hours in a timely manner, resulting in a lag in the system's recognition of pollution concentrations, and thus the inability to issue warnings in a timely manner. The public may be exposed to high concentrations of pollution without being warned, increasing the risk of respiratory diseases and health. At the same time, the environmental protection department's emergency response to peak traffic pollution will also lag, and it will be unable to take effective regulatory measures in a timely manner, which may lead to a continuous deterioration of air quality and affect the overall effect of urban environmental management.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an atmospheric environment monitoring and early warning method and device to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: an atmospheric environment monitoring and early warning method, specifically comprising the following steps: During peak traffic hours, real-time traffic status information on trunk roads is obtained and analyzed to identify whether there are frequent vehicle starts and stops during peak traffic hours; When it is identified that vehicles frequently start and stop, the traffic environment status information on the trunk roads is obtained in real time, and analyzed after acquisition to evaluate the urgency of adjusting the data collection time interval of the sensor equipment on the trunk roads, and the urgency is divided into three situations: high urgency, medium urgency and low urgency according to the urgency; Based on the evaluation and analysis results, construct and apply the warning and collection interval adjustment mechanism, and take corresponding warning and collection interval adjustment measures for different urgency situations; During the application of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the main road is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the main road by the mechanism meets the preset response requirements, and the early warning and collection interval adjustment mechanism is optimized according to the evaluation results; During long-term application, the warning and collection interval adjustment mechanism will be continuously optimized based on traffic environment status information, collection interval adjustment effects and warning trigger feedback to ensure that the system can flexibly adjust the collection interval and accurately trigger warnings under different traffic and pollutant changes.

[0008] Preferably, when it is identified that there is frequent starting and stopping of vehicles, the traffic environment status information on the trunk road is obtained in real time, and analyzed after acquisition, and the urgency of adjusting the data collection time interval of the sensor equipment on the trunk road is evaluated, and the urgency is divided into three situations of high urgency, medium urgency and low urgency according to the urgency, which specifically includes the following steps: When it is identified that vehicles frequently start and stop, the traffic environment status information on the main road is obtained in real time and pre-processed after acquisition; Extracting pollutant accumulation information and traffic fluctuation information from the pre-processed traffic environment status information on the trunk roads, and analyzing them after extraction to generate an instantaneous pollutant concentration change rate index and a traffic fluctuation intensity coefficient respectively; An urgency assessment model is constructed for the generated instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient to generate an urgency coefficient. The generated urgency coefficient is compared with the pre-set urgency coefficient threshold range. The urgency of adjusting the data collection time interval of sensor equipment on the main roads is evaluated based on the comparison results, and the urgency is divided into three situations: high urgency, medium urgency and low urgency.

[0009] Preferably, the acquisition logic of the instantaneous pollutant concentration change rate index and the traffic fluctuation intensity coefficient is as follows: Extract the pollutant accumulation information from the pre-processed traffic environment status information on the trunk road, including the actual wind speed on the trunk road at different times over a period of time, the average concentration of pollutants collected by the sensor equipment installed on the trunk road, and the pollutant emissions generated by all vehicles on the trunk road, and calibrate them respectively. , and , Indicates that within a period of time The actual wind speed on the main road at the moment, Indicates that within a period of time The average concentration of pollutants collected by sensors installed on the main roads at all times. Indicates that within a period of time The amount of pollutant emissions generated by all vehicles on the main roads at any given moment, , is a positive integer; Calculate the instantaneous pollutant concentration change rate index. The specific calculation formula is as follows: In the formula, is the instantaneous pollutant concentration change rate index; The traffic fluctuation information is extracted from the pre-processed traffic environment status information on the trunk road, including the rate of change of the average acceleration of all vehicles on the trunk road at different times within a period of time, the standard deviation of the average speed of all vehicles on the trunk road, the total number of starts and stops of all vehicles on the trunk road, and the ratio of vehicle flow to road design capacity, and they are calibrated as , , and , Indicates that within a period of time The rate of change of the average acceleration of all vehicles on the main road at the moment, Indicates that within a period of time The standard deviation of the average speed of all vehicles on the main road at the moment, Indicates that within a period of time The total number of starts and stops of all vehicles on the main road at the moment, Indicates that within a period of time The ratio of vehicle flow at any given moment to the road design capacity; Calculate the traffic fluctuation intensity coefficient. The specific calculation formula is as follows: In the formula, is the traffic fluctuation intensity coefficient.

[0010] Preferably, the generated instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient Construct an urgency assessment model and generate an urgency coefficient through weighted summation , and the generated urgency coefficient The pre-set urgency threshold interval The comparison is conducted, and the urgency of adjusting the data collection time interval of the sensor equipment on the trunk roads is evaluated based on the comparison results. The urgency is divided into three situations: high urgency, medium urgency and low urgency. The specific comparison analysis is as follows: like ,The urgency of adjusting the data collection time interval of sensor equipment on the trunk road is low urgency; like ,The urgency of adjusting the data collection time interval of sensor equipment on the trunk roads is medium urgency; like ,The urgency of adjusting the data collection time interval of sensor equipment on the main roads is high urgency.

[0011] Preferably, based on the evaluation and analysis results, a warning and collection interval adjustment mechanism is constructed and applied, and corresponding warning and collection interval adjustment measures are taken for different urgency situations, specifically: In high-urgency situations, early warnings are issued immediately and the data collection interval of sensor equipment is shortened; In cases of medium urgency, continuously monitor changes in pollutant concentrations and adjust the collection interval of sensor equipment according to a predetermined collection frequency range; In low-urgency situations, the data collection frequency of the current sensor equipment is maintained, and no warning is issued.

[0012] Preferably, during the application of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the trunk road is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road by the mechanism meets the preset response requirements, and the early warning and collection interval adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps: In the application process of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the trunk road is obtained in real time and pre-processed after acquisition; Extracting the collection response information and the collection accuracy information from the pre-processed response feedback information of the sensor equipment on the trunk road, and analyzing them after extraction to generate a data collection response time index and a data collection accuracy index respectively; An adjustment degree evaluation model is constructed for the generated data collection response time index and data collection accuracy index to generate an adjustment coefficient. The generated adjustment coefficient is compared with the pre-set adjustment coefficient threshold. Based on the comparison results, it is evaluated whether the adjustment degree of the data collection time interval of sensor equipment on the main road meets the preset response requirements. Based on the evaluation results, the early warning and collection interval adjustment mechanism is optimized.

[0013] Preferably, the acquisition logic of the data acquisition response time index and the data acquisition accuracy index is as follows: The collection response information in the response feedback information of the sensor equipment on the trunk road that has been preprocessed is extracted, including the response time from the sensor equipment receiving the instruction to adjust the data collection interval to adjusting the data collection interval, the time point when the condition that requires the collection interval adjustment is detected and the command is issued, and the time spent by the sensor equipment in transmitting the collected data to the central control system, and they are calibrated as , and , It indicates the time it takes for the sensor device to respond to the instruction to adjust the data collection interval and adjust the data collection interval. Indicates the time point when the condition that requires adjustment of the collection interval is detected and a command is issued. It indicates the time it takes for the sensor device to transmit the collected data to the central control system; Calculating Data Collection Response Time Index , according to the formula: ; The collection accuracy information in the response feedback information of the sensor equipment on the trunk road after preprocessing is extracted, including the change rate of the data collection frequency of the sensor equipment after adjusting the data collection time interval, the change rate of the pollutant concentration collected by the sensor equipment at different times within a period of time, and the change rate of the pollutant concentration collected by the sensor equipment at different times within a period of time, and the change rate of the pollutant concentration collected by the sensor equipment. , and , It indicates the rate of change of the data collection frequency after the sensor device adjusts the data collection time interval. Indicates that the sensor device adjusts the data collection time interval within a period of time The rate of change of pollutant concentration collected by the sensor equipment at all times, Indicates that the sensor device adjusts the data collection time interval within a period of time The rate of change of pollutant concentration collected by the sensor equipment at each moment, , is a positive integer; Calculate the data collection accuracy index. The specific calculation formula is as follows: In the formula, It is the data collection accuracy index.

[0014] Preferably, the generated data collection response time index and data acquisition accuracy index Construct an adjustment degree assessment model and generate adjustment coefficients through weighted summation , and the resulting adjustment factor With the preset adjustment factor threshold Compare and evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road meets the preset response requirements according to the comparison results, and optimize the warning and collection interval adjustment mechanism according to the evaluation results. The specific comparison and analysis are as follows: like ,The degree of adjustment of the data collection time interval of the sensor equipment on the main roads by this mechanism does not meet the preset response requirements, and the early warning and collection interval adjustment mechanism needs to be optimized, including: adjusting the weight ratio of the data collection response time index and the data collection accuracy index, optimizing the data transmission path to reduce delay, improving the dynamic adjustment algorithm of the collection interval to enhance the sensitivity to the urgency changes, and optimizing the prediction model of the system through machine learning to improve the accuracy of the collection interval adjustment; like ,The degree of adjustment of the data collection time interval of the sensor equipment on the main roads meets the preset response requirements, and there is no need to optimize the early warning and collection interval adjustment mechanism.

[0015] Preferably, the atmospheric environment monitoring and early warning device comprises a traffic status analysis module, an urgency assessment module, an early warning and collection interval adjustment module, a response feedback assessment module and a dynamic optimization module; Traffic status analysis module, which obtains traffic status information on trunk roads in real time during peak traffic hours, and analyzes it after acquisition to identify whether there are frequent vehicle starts and stops during peak traffic hours; The urgency assessment module, when identifying the frequent starting and stopping of vehicles, obtains the traffic environment status information on the trunk road in real time, analyzes it after acquisition, and assesses the urgency of adjusting the data collection time interval of the sensor equipment on the trunk road, and divides it into three situations according to the urgency: high urgency, medium urgency and low urgency; The warning and collection interval adjustment module builds and applies the warning and collection interval adjustment mechanism based on the evaluation and analysis results, and takes corresponding warning and collection interval adjustment measures for different urgency situations; The response feedback evaluation module obtains the response feedback information of the sensor equipment on the trunk road in real time during the application of the early warning and collection interval adjustment mechanism, and analyzes it after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road by the mechanism meets the preset response requirements, and optimizes the early warning and collection interval adjustment mechanism according to the evaluation results; During long-term application, the dynamic optimization module continuously optimizes the warning and collection interval adjustment mechanism based on traffic environment status information, collection interval adjustment effects and warning trigger feedback, ensuring that the system can flexibly adjust the collection interval and accurately trigger warnings under different traffic and pollutant changes.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention can effectively solve the problem of rapid accumulation of pollutants on trunk roads during peak traffic hours. By acquiring and analyzing traffic status information in real time, especially identifying the frequent starting and stopping of vehicles, the present invention can capture the changes in pollutant peaks in a short period of time, avoiding the lag that occurs when existing monitoring systems collect data at fixed time intervals. This dynamic pollutant monitoring method ensures that pollutant concentrations in traffic-intensive areas can be captured in a timely manner, effectively reducing the health risks of the public exposed to high-concentration pollutant environments, and providing timely and accurate pollution status information to environmental protection departments.

[0017] 2. Another major advantage of the present invention is its dynamic adjustment mechanism for data collection intervals under different urgency situations. Based on traffic status information and pollutant accumulation information, the system can automatically evaluate the urgency of the sensor data collection time interval and adjust it according to the degree of urgency. This refined adjustment mechanism not only improves the sensitivity and real-time performance of pollutant monitoring, but also greatly reduces unnecessary frequent data collection and optimizes the efficiency of data transmission. Especially in high-urgency situations, the system can immediately shorten the collection interval and issue an early warning, making the response of pollutant monitoring faster and more accurate. This flexible collection mechanism improves the adaptability of the system under complex traffic conditions and effectively avoids the problem of inaccurate capture of pollutant peaks due to too long collection time intervals.

[0018] 3. The present invention further improves the overall performance and accuracy of the system by continuously optimizing the early warning and collection interval adjustment mechanism. As time goes by, the system can continuously adjust and optimize the collection mechanism based on multiple feedback information (such as traffic environment status, collection interval adjustment effect, early warning trigger feedback), ensuring that the system can flexibly adjust the collection frequency and accurately trigger early warnings under different traffic and pollutant changes. Through this long-term optimization mechanism, the system can adapt to the changing urban traffic and pollution environment, effectively improving the emergency response capabilities of the environmental protection department and the efficiency of urban pollution control. This dynamic optimization strategy ensures the robustness and adaptability of the system, enabling it to maintain efficient and accurate monitoring and early warning functions under complex and changing urban traffic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 The figure is a flow chart of the atmospheric environment monitoring and early warning method of the present invention.

[0021] Figure 2 It is a module schematic diagram of the atmospheric environment monitoring and early warning device of the present invention. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0023] Example 1 The present invention provides Figure 1 The atmospheric environment monitoring and early warning method shown specifically includes the following steps: During peak traffic hours, real-time traffic status information on trunk roads is obtained and analyzed to identify whether there are frequent vehicle starts and stops during peak traffic hours; To achieve "real-time acquisition of traffic status information on trunk road sections", the following methods can be used: First, use traffic monitoring cameras and traffic flow sensors installed on trunk road sections to collect data such as vehicle flow and speed in real time. The camera can identify the vehicle's movement trajectory, parking, starting and other behaviors, and the traffic flow sensor can record the number of vehicles passing through the road section and the change in speed. In addition, the system can also obtain signal control information of specific sections through traffic signal data (such as traffic light status and signal cycle) in the urban traffic management system. Another way is to combine vehicle-to-everything (V2X) technology to receive vehicle start, stop, acceleration and deceleration status information in real time through communication between vehicles and infrastructure. These data can be transmitted to the central control system through wireless sensor networks or Internet of Things platforms to ensure real-time acquisition and update of traffic status information.

[0024] After obtaining traffic status information, the data can be analyzed by the software system. First, the system uses big data analysis algorithms to analyze the traffic flow, speed, stop and start data collected by cameras, sensors, traffic lights and vehicle communication equipment in real time. The software can apply pattern recognition algorithms or machine learning models to detect traffic congestion by identifying the frequent start and stop patterns of vehicles during peak hours (for example, vehicles decelerate and restart sharply multiple times in a short period of time). At the same time, the software can also combine historical traffic data and traffic flow prediction models to analyze whether there are abnormal changes in vehicle behavior on the road section, such as frequent start and stop patterns. By comparing the preset vehicle start and stop thresholds, the system can quickly identify whether there are frequent vehicle starts and stops during peak traffic hours.

[0025] By acquiring and analyzing traffic status information in real time, it is possible to timely identify the frequent start and stop of vehicles, which is crucial to the entire early warning system. During peak traffic hours, especially on main roads with dense traffic lights, frequent starts and stops will lead to an instantaneous increase in pollutant emissions, especially the accumulation of harmful gases such as NOx and CO. By accurately identifying this situation, the system can more effectively determine the instantaneous accumulation trend of pollutants, thereby providing support for subsequent pollutant data collection and early warning mechanisms. If the system can quickly identify the frequent start and stop of vehicles during peak hours, it can adjust the pollutant data collection frequency in real time according to this dynamic change, avoiding the problem of existing technologies relying on fixed time intervals to collect data, thereby solving the problem of untimely early warnings caused by delayed collection. This process is the basic part of the entire technical solution, providing accurate input for dynamic adjustment and early warning response, ensuring that early warnings can be issued in a timely manner when pollutant concentrations rise rapidly, protecting public health and helping environmental protection departments to respond quickly.

[0026] When it is identified that vehicles frequently start and stop, the traffic environment status information on the trunk roads is obtained in real time, and analyzed after acquisition to evaluate the urgency of adjusting the data collection time interval of the sensor equipment on the trunk roads, and the urgency is divided into three situations: high urgency, medium urgency and low urgency according to the urgency; In this embodiment, when it is identified that there are frequent vehicle starts and stops, the traffic environment status information on the trunk road is obtained in real time, and after the acquisition, it is analyzed to evaluate the urgency of adjusting the data collection time interval of the sensor equipment on the trunk road, and the urgency is divided into three situations according to the urgency: high urgency, medium urgency and low urgency, which specifically includes the following steps: When it is identified that vehicles frequently start and stop, the traffic environment status information on the main road is obtained in real time and pre-processed after acquisition; Real-time acquisition of traffic environment status information on trunk roads can be achieved in a variety of ways, mainly relying on the synergy of sensor networks, traffic monitoring equipment and vehicle-to-everything (V2X) technology. First, traffic flow sensors installed on trunk roads can collect real-time traffic information such as the speed, acceleration, and number of stops of vehicles on the road; at the same time, pollutant sensors can monitor the concentration of pollutants in the air (such as NOx, CO, etc.) in real time. In addition, through the communication between vehicles and infrastructure (V2X), vehicle-to-everything technology can directly obtain data such as vehicle emissions, start-stop status, and driving status, and upload these data to the central monitoring system through wireless networks. After the system integrates data from these multi-dimensional sensors, it can update the traffic status and pollutant emission information of vehicles on the road in real time. These data are transmitted to the software system through wireless sensor networks or Internet of Things platforms to ensure the real-time and accuracy of traffic environment status information.

[0027] Preprocessing is an essential step in the data processing process. Its main purpose is to clean, filter and standardize the raw data to ensure the accuracy and stability of subsequent analysis. First, traffic environment status information usually contains noise and incomplete data, such as missing data caused by sensor failure or outliers in vehicle emission data. Therefore, the preprocessing process first needs to perform missing value filling and outlier detection, and use interpolation or models based on historical data to make up for missing values ​​and delete or correct abnormal data. Secondly, data from different sensors may be inconsistent in sampling frequency and format, so it is necessary to unify the data format and synchronize data timestamps to ensure that data from different sources can be accurately integrated. Finally, in order to optimize the efficiency of data analysis, it is also necessary to reduce the dimension or extract features of traffic environment status information, such as extracting key features from data such as vehicle acceleration and parking frequency, reducing data redundancy, and ensuring more efficient analysis. The entire preprocessing process is automatically completed by the data processing module in the software, and the functions of data cleaning, format standardization and feature extraction are realized through algorithms to ensure that the processed data can support subsequent accurate analysis.

[0028] Extracting pollutant accumulation information and traffic fluctuation information from the pre-processed traffic environment status information on the trunk roads, and analyzing them after extraction to generate an instantaneous pollutant concentration change rate index and a traffic fluctuation intensity coefficient respectively; An urgency assessment model is constructed for the generated instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient to generate an urgency coefficient. The generated urgency coefficient is compared with the pre-set urgency coefficient threshold range. The urgency of adjusting the data collection time interval of sensor equipment on the main roads is evaluated based on the comparison results, and the urgency is divided into three situations: high urgency, medium urgency and low urgency.

[0029] Determining the pre-set threshold interval of the urgency coefficient can be achieved by combining historical data analysis, model training and dynamic adjustment. First, the system can perform big data analysis based on historically collected traffic environment status information, including pollutant accumulation rate, traffic fluctuation intensity, and corresponding environmental response (such as pollutant peak and warning lag). By using machine learning algorithms (such as decision trees, support vector machines or neural networks), the system can automatically establish an association model to identify the change pattern of the urgency coefficient under different traffic and pollutant conditions, and determine a reasonable urgency coefficient interval through cluster analysis or regression analysis. After determining the threshold, the system will also perform dynamic optimization in actual operation, using adaptive algorithms (such as adaptive threshold adjustment algorithms) to automatically adjust the threshold range based on real-time feedback data to ensure that the urgency coefficient threshold can adapt to changes in different scenarios. This data-driven approach can not only set the initial threshold, but also dynamically modify the threshold interval according to the long-term operation of the system to ensure the accuracy and flexibility of the early warning mechanism.

[0030] In this embodiment, the logic for obtaining the instantaneous pollutant concentration change rate index and the traffic fluctuation intensity coefficient is as follows: Extract the pollutant accumulation information from the pre-processed traffic environment status information on the trunk road, including the actual wind speed on the trunk road at different times over a period of time, the average concentration of pollutants collected by the sensor equipment installed on the trunk road, and the pollutant emissions generated by all vehicles on the trunk road, and calibrate them respectively. , and , Indicates that within a period of time The actual wind speed on the main road at the moment, Indicates that within a period of time The average concentration of pollutants collected by sensors installed on the main roads at all times. Indicates that within a period of time The amount of pollutant emissions generated by all vehicles on the main roads at any given moment, , is a positive integer; Extracting the pollutant accumulation information on the pre-processed trunk roads can be achieved in the following ways. First, the actual wind speed data can be obtained in real time through meteorological monitoring equipment (such as wind speed sensors) installed on the trunk roads. These sensors transmit the wind speed data to the system's central data platform through wireless transmission. Secondly, the average concentration of pollutants on the trunk roads is collected in real time by air quality sensors (such as NOx and CO sensors) distributed throughout the roads. The sensors automatically collect pollutant concentration data in the air at different time points and average these collected data through the data integration module. Finally, vehicle emission data can be obtained through the vehicle-to-everything (V2X) system. Vehicles communicate with smart devices on the roadside and collect data from the vehicle emission monitoring system (such as NOx and CO emissions of each vehicle) to aggregate vehicle emissions to the central data platform. All of these data are transmitted in real time through the IoT platform and sensor network, and data extraction and processing are performed in the central system. The extraction process relies on the data integration module in the software to automatically integrate, process, and prepare these quantitative data from different sources for further analysis.

[0031] Calculate the instantaneous pollutant concentration change rate index. The specific calculation formula is as follows: In the formula, is the instantaneous pollutant concentration change rate index; This instantaneous pollutant concentration change rate index The calculation formula is based on the average concentration of pollutants , vehicle emissions and wind speed The weighted processing is performed to obtain the rate of change of pollutant concentration. Used to measure the accumulation of pollutants in each time period, wind speed The diffusion capacity of pollutants is adjusted to ensure that the concentration changes less when the wind speed is high and changes faster when the wind speed is low. By weighted averaging multiple moments, the dynamic change trend of pollutant concentration over the entire time period can be reflected. This calculation method integrates key environmental factors and provides an accurate index for measuring the rate of change of pollutant concentration over time.

[0032] Instantaneous pollutant concentration change rate index The size of is directly related to the urgency of the data collection time interval of the sensor device. When the index is large, it means that the concentration of pollutants changes rapidly in a short period of time. Especially when the vehicle is frequently started and stopped, the accumulation speed of pollutants is fast. At this time, the system needs to shorten the sensor collection time interval to obtain data more frequently, accurately capture the dynamic changes of pollutants and issue timely warnings. On the contrary, when When the index is small, it means that the pollutant concentration changes slowly and the pollutant diffuses smoothly. The system can extend the data collection time interval and reduce unnecessary collection frequency, thereby optimizing resource utilization.

[0033] The traffic fluctuation information is extracted from the pre-processed traffic environment status information on the trunk road, including the rate of change of the average acceleration of all vehicles on the trunk road at different times within a period of time, the standard deviation of the average speed of all vehicles on the trunk road, the total number of starts and stops of all vehicles on the trunk road, and the ratio of vehicle flow to road design capacity, and they are calibrated as , , and , Indicates that within a period of time The rate of change of the average acceleration of all vehicles on the main road at the moment, Indicates that within a period of time The standard deviation of the average speed of all vehicles on the main road at the moment, Indicates that within a period of time The total number of starts and stops of all vehicles on the main road at the moment, Indicates that within a period of time The ratio of vehicle flow at any given moment to the road design capacity; Extracting the pre-processed traffic fluctuation information on the main road can be achieved through the traffic monitoring system and vehicle-to-everything (V2X) technology installed on the road. First, the rate of change of the average acceleration of the vehicle can be obtained in real time through the vehicle-to-everything system to obtain the acceleration data of each vehicle, and the rate of change of the acceleration of all vehicles in a specific time period can be calculated in the central system. The standard deviation of the average speed of the vehicle can be obtained through the traffic flow sensors and vehicle-to-everything devices installed on both sides of the road to obtain the real-time speed data of all vehicles. The software calculates the standard deviation of the speed of all vehicles in a specific time period through statistical analysis. The total number of vehicle starts and stops is also captured by the vehicle-to-everything and traffic flow sensors. Each start and stop is identified and accumulated through acceleration and deceleration data. The ratio of vehicle flow to road design capacity is obtained by obtaining the real-time flow of vehicles on the road through traffic flow sensors, and compared with the pre-set road design capacity to generate flow occupancy. All these quantitative data are transmitted to the central control system through the sensor network and vehicle-to-everything system. After transmission, the data is pre-processed and analyzed by the software to ensure the integrity and accuracy of the information, providing basic data for the subsequent calculation of the traffic fluctuation intensity coefficient.

[0034] Calculate the traffic fluctuation intensity coefficient. The specific calculation formula is as follows: In the formula, is the traffic fluctuation intensity coefficient.

[0035] Traffic fluctuation intensity coefficient The calculation formula of is a weighted combination of multiple variables, with the aim of comprehensively measuring the intensity of traffic flow fluctuations on the main roads. It represents the product of the standard deviation of all vehicle speeds and the acceleration change rate, reflecting the contribution of vehicle speed fluctuations and acceleration changes to traffic fluctuations within a certain period of time. It represents the number of vehicle starts and stops. By dividing it by the number of starts and stops, the impact of frequent starts and stops on traffic fluctuations can be highlighted. When the number of starts and stops is high, the traffic fluctuation is more significant. It is a nonlinear processing of the ratio of vehicle flow to road design capacity. The logarithmic function can capture the nonlinear changes in road congestion, especially during peak hours when vehicles are congested. A small change in the proportion of flow may lead to large-scale fluctuations. By weighted averaging multiple moments, it ensures that the formula can integrate data at different moments and reflect the overall trend of traffic fluctuations during the entire period, rather than being limited to a certain point in time. This calculation can more comprehensively and dynamically evaluate traffic fluctuations, providing an accurate quantitative basis for optimizing traffic management and dynamic adjustments.

[0036] Traffic fluctuation intensity coefficient The size of is closely related to the urgency of adjusting the data collection time interval of the sensor equipment. When the coefficient is large, it means that the vehicle acceleration on the main road changes frequently, the speed fluctuates greatly, the number of starts and stops is large, and the traffic volume is close to or exceeds the road design capacity, reflecting that the traffic fluctuation intensity is high. At this time, it is necessary to shorten the data collection time interval of the sensor, monitor the traffic conditions at a higher frequency, capture subtle traffic changes, and ensure that the warning is issued in time; on the contrary, when When the coefficient is small, it indicates that the traffic flow is relatively stable with small fluctuations. At this time, the time interval for data collection can be extended to reduce unnecessary collection frequency, thereby optimizing the use of system resources.

[0037] In this embodiment, the generated instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient Construct an urgency assessment model and generate an urgency coefficient through weighted summation , and the generated urgency coefficient The pre-set urgency threshold interval The comparison is conducted, and the urgency of adjusting the data collection time interval of the sensor equipment on the trunk roads is evaluated based on the comparison results. The urgency is divided into three situations: high urgency, medium urgency and low urgency. The specific comparison analysis is as follows: like ,The urgency of adjusting the data collection time interval of sensor equipment on the trunk road is low urgency; This situation means that the current traffic fluctuations and pollutant concentration change rates on the road are relatively stable, the traffic flow matches the road design capacity, and the vehicle acceleration changes, start-stop frequency, and pollutant accumulation rate are all at a low level. In this case, the system can extend the sensor data collection time interval because frequent monitoring is not necessary, the data changes are small, and reducing the collection frequency can save resources and reduce the system's energy consumption.

[0038] like ,The urgency of adjusting the data collection time interval of sensor equipment on the trunk roads is medium urgency; This situation indicates that the traffic fluctuations and pollutant concentration change rates on the road are at a moderate level, and the traffic flow has some fluctuations, but has not yet reached a serious congestion or a large accumulation of pollutants. At this time, the system needs to maintain the current data collection frequency, or appropriately adjust the sensor collection time interval according to the actual situation. This ensures that the system can capture the dynamic changes in traffic and pollutants while still effectively optimizing resources and ensuring the real-time nature of information updates.

[0039] like ,The urgency of adjusting the data collection time interval of sensor equipment on the main roads is high urgency.

[0040] This situation means that traffic on the main roads fluctuates strongly, vehicles start and stop frequently, acceleration changes dramatically, and pollutant concentrations accumulate rapidly in a short period of time. At this time, the system needs to significantly shorten the data collection time interval of the sensor and monitor the changes in traffic and pollutants in real time to detect problems in time and issue warnings. High urgency indicates that environmental conditions may deteriorate, and sensors need more frequent data updates to support the implementation of emergency measures and avoid potential health and environmental risks.

[0041] The core of building the urgency assessment model is to use the instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient Combined, the urgency coefficient is generated by weighted summation First, according to the actual application scenario, the weight coefficients of the two coefficients in different situations are determined. For example, when the accumulation of pollutants is more important to health risks, Higher weight is given to traffic congestion when its impact on the environment is more obvious. A higher weight. Specifically, the urgency factor The calculation formula is: ,in and They are and The weight coefficient of By adjusting the weight coefficient, the system can flexibly adapt to different traffic and environmental change scenarios. The urgency coefficient generated in this way can better reflect real-time traffic fluctuations and changes in pollutant concentrations, thereby accurately evaluating the need to adjust the data collection frequency.

[0042] Based on the evaluation and analysis results, construct and apply the warning and collection interval adjustment mechanism, and take corresponding warning and collection interval adjustment measures for different urgency situations; In this embodiment, based on the evaluation and analysis results, a warning and collection interval adjustment mechanism is constructed and applied, and corresponding warning and collection interval adjustment measures are taken for different urgency situations, specifically: In high-urgency situations, early warnings are issued immediately and the data collection interval of sensor equipment is shortened to increase the frequency of data collection and ensure that changes in pollutant concentrations and traffic fluctuations are captured in a timely manner; In high-urgency situations, the system needs to quickly capture significant changes in the environment through real-time monitoring data from sensor devices (such as pollutant concentrations and traffic fluctuation intensity). Through the early warning and collection interval adjustment mechanism, the software can immediately shorten the sensor's data collection time interval. The specific implementation method is that the system automatically adjusts the collection frequency parameters and increases the collection density after the urgency coefficient exceeds the preset threshold to ensure that the latest environmental status data is obtained at a higher frequency. This adjustment can be set through a dynamic collection frequency range through the software, and the system will adaptively speed up data collection based on the real-time feedback of urgency information. This is done to ensure that the system can promptly identify high pollution and traffic congestion, and quickly take corresponding early warning and response measures to protect public health and optimize traffic management.

[0043] In the case of medium urgency, continuously monitor the changes in pollutant concentrations and adjust the collection interval of the sensor equipment according to the predetermined collection frequency range to ensure that the data collection frequency is adapted to environmental changes; In medium urgency situations, the system needs to continuously monitor pollutant concentrations and traffic fluctuations, but does not need to collect data as frequently as in high urgency situations. The software can automatically adjust the data collection interval of the sensor according to the predetermined collection frequency range. The specific implementation method is that the system evaluates the urgency coefficient in the medium range and calls the control module in the software to set a moderate data collection frequency. The system will dynamically adjust the collection interval of the sensor in combination with the traffic environment status information, for example, it can obtain the latest data at regular intervals. This is done to ensure that the system can continue to track environmental changes, but will not occupy too many resources, thereby maintaining good monitoring efficiency, while ensuring the accuracy of the system when dealing with medium urgency.

[0044] In low-urgency situations, the data collection frequency of the current sensor equipment is maintained and no warning is issued to save system resources and optimize collection efficiency.

[0045] In low-urgency situations, the software will maintain the current collection frequency and does not need to be adjusted frequently. The specific implementation method is that when the urgency coefficient is in a low range, the system will automatically determine that there is no need to issue an early warning, and the data collection time interval of the sensor equipment will also be maintained at the default setting. This can be achieved through the automatic judgment mechanism within the software. When the urgency is at a low level, the software will maintain the existing collection interval frequency and will not issue additional instructions. This is done to reduce the system load and save unnecessary data processing work when resources are not tight, while still maintaining basic monitoring capabilities to ensure that the system can still operate effectively in low pollution or low traffic fluctuations.

[0046] The process of building an early warning and data collection interval adjustment mechanism can be achieved through the logical judgment and feedback adjustment mechanism in the software. First, the system needs to establish an urgency assessment model, which performs weighted calculation based on the collected traffic fluctuation intensity coefficient and the instantaneous pollutant concentration change rate index to generate an urgency coefficient. Next, the system sets the threshold interval for early warning and data collection interval adjustment (such as high urgency, medium urgency, and low urgency), and defines different collection frequency adjustment strategies for each urgency interval. When the urgency coefficient triggers a certain threshold, the system dynamically adjusts the sensor collection interval through the internal control module to shorten or extend the collection frequency. At the same time, the system will use an automated feedback mechanism to close the loop of urgency assessment and sensor response information to ensure the real-time and effectiveness of data collection.

[0047] During the application of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the main road is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the main road by the mechanism meets the preset response requirements, and the early warning and collection interval adjustment mechanism is optimized according to the evaluation results; In this embodiment, during the application of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the trunk road is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road by the mechanism meets the preset response requirements, and the early warning and collection interval adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps: In the application process of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the trunk road is obtained in real time and pre-processed after acquisition; Real-time acquisition of response feedback information from sensor devices on trunk roads can be achieved through the integration of sensor networks and central control systems. Each sensor device is equipped with a communication module, which can send the collected data to the central system through wireless transmission (such as 5G or LoRa, etc.). The specific implementation method is that the sensor device will regularly transmit information such as the device status, data collection response time, collection frequency, transmission delay, etc. to the central data platform in real time. Through the information fed back by these sensors, the central system can obtain the working status of each device in real time, including its response to the collection interval adjustment command. This method ensures that the system can quickly capture the response information of the sensor device and provide timely data support for subsequent analysis.

[0048] Preprocessing is performed to ensure that the acquired feedback data can be effectively used for analysis, remove noise data, and standardize data from different sources. Feedback information may contain erroneous or invalid data (such as information loss caused by transmission delay or sensor abnormality). Preprocessing can filter out accurate and valid data. Specific preprocessing operations include: first, checking the integrity of the acquired feedback information and eliminating incomplete or erroneous data; second, using data smoothing algorithms to eliminate outliers to ensure data continuity and consistency; finally, uniformly standardizing the feedback data from different sensors to ensure that data from different devices are analyzed under the same dimension. These preprocessing steps are completed automatically through software to ensure data quality and provide a reliable data basis for the subsequent calculation of response time index and acquisition accuracy index.

[0049] Extracting the collection response information and the collection accuracy information from the pre-processed response feedback information of the sensor equipment on the trunk road, and analyzing them after extraction to generate a data collection response time index and a data collection accuracy index respectively; An adjustment degree evaluation model is constructed for the generated data collection response time index and data collection accuracy index to generate an adjustment coefficient. The generated adjustment coefficient is compared with the pre-set adjustment coefficient threshold. Based on the comparison results, it is evaluated whether the adjustment degree of the data collection time interval of sensor equipment on the main road meets the preset response requirements. Based on the evaluation results, the early warning and collection interval adjustment mechanism is optimized.

[0050] The preset adjustment coefficient threshold can be determined by analyzing historical data and simulating the system operating environment. First, the software can collect historical feedback information from sensor devices on trunk roads for a long time, including data collection response time, collection accuracy and other related data, and perform statistical analysis. Through these historical data, the system can identify the normal range and abnormal conditions of sensor device response and collection frequency under different urgency conditions. Next, the software will perform simulation tests to simulate the response of sensors based on different collection frequencies and urgency conditions to find the optimal adjustment coefficient for different situations. Finally, based on the statistical analysis results and simulation test data, the software uses advanced statistical or machine learning algorithms (such as regression analysis or clustering algorithms) to generate a reasonable adjustment coefficient threshold range. This ensures that the adjustment coefficient threshold can accurately reflect the actual equipment response and collection interval adjustment requirements in actual operation, thereby achieving more intelligent dynamic adjustment.

[0051] In this embodiment, the acquisition logic of the data collection response time index and the data collection accuracy index is as follows: The collection response information in the response feedback information of the sensor equipment on the trunk road that has been preprocessed is extracted, including the response time from the sensor equipment receiving the instruction to adjust the data collection interval to adjusting the data collection interval, the time point when the condition that requires the collection interval adjustment is detected and the command is issued, and the time spent by the sensor equipment in transmitting the collected data to the central control system, and they are calibrated as , and , It indicates the time it takes for the sensor device to respond to the instruction to adjust the data collection interval and adjust the data collection interval. Indicates the time point when the condition that requires adjustment of the collection interval is detected and a command is issued. It indicates the time it takes for the sensor device to transmit the collected data to the central control system; Extracting the collection response information from the response feedback information of the sensor equipment on the trunk road after preprocessing can be achieved through the communication module, system log recording and network monitoring module of the sensor equipment. The specific implementation method is that the sensor equipment will record the time from receiving the instruction to adjust the collection interval to the actual adjustment completion. This data can be extracted through the operation log or state change log inside the equipment. When the system detects the conditions that require adjustment of the collection interval, it will automatically generate a timestamp in the central control system to record the specific time point when the system issues the adjustment command. This time can be extracted from the operation log of the central system. After the sensor equipment collects the data, it transmits it to the central control system. The time spent in the transmission process is automatically monitored and recorded by the network monitoring module, and calculated by combining the upload time of the sensor equipment and the time when the central control system receives the data. These data are extracted through the software automation process. After preprocessing, the integrity and accuracy of the data are ensured, thereby providing a basis for the subsequent calculation of the data collection response time index.

[0052] Calculating Data Collection Response Time Index , according to the formula: ; The calculation formula for this data collection response time index is The purpose of is to evaluate the speed and efficiency of the sensor device in responding to the adjustment of the data collection interval. In the formula, Represents the time (response time) from when the sensor device receives the adjustment instruction to when the adjustment is actually completed. This is the core indicator for evaluating the response speed of the sensor. Represents the point in time when the system issues an adjustment command, which measures the system's reaction speed when an urgent condition is detected; It is the delay time for the sensor device to transmit data to the central control system after collecting it, which reflects the network transmission efficiency. Through this formula, the sensor response time is combined with the system trigger and data transmission delay for calculation, which can measure the efficiency of the entire data collection and response process as a whole.

[0053] Data Collection Response Time Index The size of directly reflects the response speed of the sensor device to the data collection interval adjustment command, thereby evaluating whether the system adjustment effect meets the preset response requirements. Specifically, if the data collection response time index is large, it means that the sensor device can respond quickly after receiving the adjustment command, and the data transmission delay is low, the system adjustment efficiency is high, and the preset response requirements are met; on the contrary, if the index is small, it means that the sensor device has a large delay in responding to the command, and the real-time adjustment is poor, and it may not be able to reflect the pollution changes or traffic conditions of the main roads in time, resulting in the system failing to meet the preset response requirements. Therefore, the larger the index, the more timely and effective the system's adjustment of the collection interval is, and the more it meets the expected adjustment goals.

[0054] The collection accuracy information in the response feedback information of the sensor equipment on the trunk road after preprocessing is extracted, including the change rate of the data collection frequency of the sensor equipment after adjusting the data collection time interval, the change rate of the pollutant concentration collected by the sensor equipment at different times within a period of time, and the change rate of the pollutant concentration collected by the sensor equipment at different times within a period of time, and the change rate of the pollutant concentration collected by the sensor equipment. , and , It indicates the rate of change of the data collection frequency after the sensor device adjusts the data collection time interval. Indicates that the sensor device adjusts the data collection time interval within a period of time The rate of change of pollutant concentration collected by the sensor equipment at all times, Indicates that the sensor device adjusts the data collection time interval within a period of time The rate of change of pollutant concentration collected by the sensor equipment at each moment, , is a positive integer; Extracting the collection accuracy information from the response feedback information of the sensor equipment on the trunk road after preprocessing can be achieved through the built-in data recording module of the sensor equipment and the data analysis module of the central monitoring system. The specific implementation method is that after adjusting the data collection time interval, the sensor equipment will automatically record the changes in the collection frequency of each collection cycle (the rate of change of the data collection frequency), which can be directly extracted through the internal collection log of the sensor. At the same time, the sensor equipment will record the actual change rate of the pollutant concentration (actual pollutant concentration change rate) at each collection time point. These data are collected in real time by the monitoring module of the sensor and stored in the database of the system. The expected pollutant concentration change rate is generated by the expected model in the early warning and collection interval adjustment mechanism, calculated based on historical data and environmental prediction models, and the system will estimate the changes in pollutant concentration that should be collected at a specific collection time interval based on current environmental conditions. The software can judge the gap between the actual effect and the expected effect of data collection by automatically extracting, comparing and analyzing these data, thereby generating a data collection accuracy index. The extraction and analysis of these data are automatically performed by the integrated software system, ensuring the efficiency and accuracy of the accuracy evaluation process.

[0055] Calculate the data collection accuracy index. The specific calculation formula is as follows: In the formula, It is the data collection accuracy index.

[0056] The calculation formula for this data collection accuracy index is The purpose of the calculation formula is to evaluate the data collection accuracy of the sensor device after adjusting the collection frequency. The purpose of the calculation formula is to compare the actual collection data change rate of the sensor device after adjusting the collection interval. The rate of change expected from the system , to evaluate whether the adjusted collection is accurate. The collection frequency change rate in the denominator It plays a standardization role, uniformly measuring the errors under different acquisition frequencies, so that the influence of acquisition frequency is taken into consideration. Finally, the entire formula averages the data differences in each time period to reflect the overall accuracy of the entire acquisition cycle. The calculation step ensures that the positive and negative directions of the error do not affect the accuracy evaluation through the absolute value. The smaller the difference, the higher the accuracy of the sensor device and the better the adjustment effect; if the difference is large, it means that the adjustment effect is not good and the acquisition accuracy does not meet expectations.

[0057] Data collection accuracy index The size of directly reflects the difference between the actual collection effect and the expected collection effect of the sensor device after adjusting the collection time interval. Therefore, it is closely related to evaluating whether the mechanism meets the preset response requirements. Specifically, a lower DCAI value indicates that the collected data of the sensor device after adjustment is very close to the expected data, indicating that the accuracy of data collection is high, the collection interval adjustment mechanism works well, and meets the preset response requirements; while a higher DCAI value indicates that there is a large deviation between the actual collected data and the expected data, and the data collection accuracy is not ideal, indicating that the adjustment mechanism fails to effectively control the collection interval and may need to be further optimized or readjusted to meet the preset requirements of the system.

[0058] In this embodiment, the generated data collection response time index and data acquisition accuracy index Construct an adjustment degree assessment model and generate adjustment coefficients through weighted summation , and the resulting adjustment factor With the preset adjustment factor threshold Compare and evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road meets the preset response requirements according to the comparison results, and optimize the warning and collection interval adjustment mechanism according to the evaluation results. The specific comparison and analysis are as follows: like ,The degree of adjustment of the data collection time interval of the sensor equipment on the main roads by this mechanism does not meet the preset response requirements, and the early warning and collection interval adjustment mechanism needs to be optimized, including: adjusting the weight ratio of the data collection response time index and the data collection accuracy index, optimizing the data transmission path to reduce delay, improving the dynamic adjustment algorithm of the collection interval to enhance the sensitivity to the urgency changes, and optimizing the prediction model of the system through machine learning to improve the accuracy of the collection interval adjustment; This situation means that the adjustment mechanism has not met the preset response requirements, the collection time interval adjustment of the sensor equipment is not ideal, the system's response speed or collection accuracy may be biased, and the changes in pollutants on the road cannot be effectively captured. This will lead to untimely warnings or insufficient data collection accuracy, affecting the real-time monitoring of air quality. In order to optimize this situation, the weight ratio of the data collection response time index and the data collection accuracy index can be adjusted to dynamically adjust the balance between response speed and collection accuracy according to the actual needs of the system. At the same time, optimize the data transmission path, reduce the delay problem in data transmission, and ensure that the sensor equipment can respond to instructions quickly. Furthermore, the dynamic adjustment algorithm of the collection interval can be improved to make it more sensitive to changes in traffic and pollutants and improve the flexibility of the collection frequency adjustment. Through machine learning technology, historical data and feedback are analyzed, the system's prediction model is continuously optimized, and the accuracy of the entire adjustment mechanism is improved.

[0059] like ,The degree of adjustment of the data collection time interval of the sensor equipment on the main roads meets the preset response requirements, and there is no need to optimize the early warning and collection interval adjustment mechanism.

[0060] At this point, the system adjustment mechanism has met the preset response requirements, indicating that the sensor equipment can effectively adjust the collection time interval, and the system can respond to environmental changes in a timely manner while maintaining good collection accuracy. In this system state, no further optimization is required. The system can accurately monitor changes in pollutant concentrations on trunk roads, and the early warning mechanism is effective. This means that the optimal balance between data collection frequency and system response has been achieved, and the system can maintain efficient and stable operation, continuing to ensure the accuracy of real-time monitoring and pollution warnings.

[0061] “Response time index for data collection generated and data acquisition accuracy index The purpose of building an adjustment degree evaluation model is to comprehensively evaluate the performance of the system in terms of response speed and acquisition accuracy. and Different weight coefficients are assigned to each parameter to determine which parameter has a greater impact on system adjustment based on the actual application scenario. For example, in an emergency, the system may pay more attention to response speed, so The weight coefficient is higher, while On the contrary, if the current scene requires more acquisition accuracy, then The weight coefficient is higher. Then, a weighted summation method is used to calculate an adjustment coefficient , the formula is ,in and They are and The weight coefficients of the two parameters are combined reasonably to generate a comprehensive adjustment coefficient. By adjusting these two weights, different scene requirements can be flexibly responded to, ensuring that the system achieves the optimal balance between response and accuracy.

[0062] During long-term application, the warning and collection interval adjustment mechanism will be continuously optimized based on traffic environment status information, collection interval adjustment effects and warning trigger feedback to ensure that the system can flexibly adjust the collection interval and accurately trigger warnings under different traffic and pollutant changes.

[0063] During long-term application, the system will continuously collect traffic environment status information on trunk roads, including vehicle flow, acceleration changes, start-stop times, and other data. Through software algorithms, the system can analyze the changing trends of these data in real time, and evaluate the applicability of the current strategy in combination with the effect of adjusting the collection interval of sensor equipment. At the same time, the system will record the feedback information of each warning trigger, including the warning response time and warning accuracy, and further optimize the warning trigger mechanism by analyzing these feedback information to ensure that the system can respond to different emergency situations in a timely manner.

[0064] The system will use historical traffic flow, pollutant concentration changes and other data to build an intelligent prediction model, and through multivariate time series analysis, predict traffic fluctuations and pollutant concentration changes in the future. Based on these prediction results, the system can optimize the collection interval in advance. For example, if the system predicts an upcoming traffic rush or a sharp change in pollutant concentration, it can proactively shorten the collection time interval to ensure that pollutant changes can be accurately monitored at critical moments and timely warnings can be issued.

[0065] Through machine learning algorithms, the system can dynamically optimize the collection interval adjustment mechanism based on real-time and historical data feedback. Under different traffic and pollutant change conditions, the system will adaptively adjust the sensor's collection frequency. In emergency situations, the system will increase the frequency of data collection to ensure that the rapid accumulation of pollutants can be captured in time; while in stable traffic conditions, the system will reduce the collection frequency to reduce the system burden. Through this dynamic optimization, the system can maintain efficient and accurate collection and early warning capabilities for a long time.

[0066] Example 2 like Figure 2 The atmospheric environment monitoring and early warning device shown includes a traffic status analysis module, an urgency assessment module, an early warning and collection interval adjustment module, a response feedback assessment module, and a dynamic optimization module; Traffic status analysis module, which obtains traffic status information on trunk roads in real time during peak traffic hours, and analyzes it after acquisition to identify whether there are frequent vehicle starts and stops during peak traffic hours; The urgency assessment module, when identifying the frequent starting and stopping of vehicles, obtains the traffic environment status information on the trunk road in real time, analyzes it after acquisition, and assesses the urgency of adjusting the data collection time interval of the sensor equipment on the trunk road, and divides it into three situations according to the urgency: high urgency, medium urgency and low urgency; The warning and collection interval adjustment module builds and applies the warning and collection interval adjustment mechanism based on the evaluation and analysis results, and takes corresponding warning and collection interval adjustment measures for different urgency situations; The response feedback evaluation module obtains the response feedback information of the sensor equipment on the trunk road in real time during the application of the early warning and collection interval adjustment mechanism, and analyzes it after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road by the mechanism meets the preset response requirements, and optimizes the early warning and collection interval adjustment mechanism according to the evaluation results; During long-term application, the dynamic optimization module continuously optimizes the warning and collection interval adjustment mechanism based on traffic environment status information, collection interval adjustment effects and warning trigger feedback, ensuring that the system can flexibly adjust the collection interval and accurately trigger warnings under different traffic and pollutant changes.

[0067] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0068] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0069] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0070] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0071] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0072] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An atmospheric environment monitoring and early warning method, characterized in that: The specific steps include: During peak traffic hours, real-time traffic status information on trunk roads is obtained and analyzed to identify whether there are frequent vehicle starts and stops during peak traffic hours; When it is identified that vehicles frequently start and stop, the traffic environment status information on the trunk roads is obtained in real time, and analyzed after acquisition to evaluate the urgency of adjusting the data collection time interval of the sensor equipment on the trunk roads, and the urgency is divided into three situations: high urgency, medium urgency and low urgency according to the urgency; Based on the evaluation and analysis results, construct and apply the warning and collection interval adjustment mechanism, and take corresponding warning and collection interval adjustment measures for different urgency situations; During the application of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the main road is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the main road by the mechanism meets the preset response requirements, and the early warning and collection interval adjustment mechanism is optimized according to the evaluation results; During long-term application, the warning and collection interval adjustment mechanism will be continuously optimized based on traffic environment status information, collection interval adjustment effects and warning trigger feedback to ensure that the system can flexibly adjust the collection interval and accurately trigger warnings under different traffic and pollutant changes.

2. The atmospheric environment monitoring and early warning method according to claim 1, characterized in that: When it is identified that vehicles frequently start and stop, the traffic environment status information on the trunk road is obtained in real time, and analyzed after acquisition to evaluate the urgency of adjusting the data collection time interval of the sensor equipment on the trunk road. The urgency is divided into three situations: high urgency, medium urgency and low urgency according to the urgency, which specifically includes the following steps: When it is identified that vehicles frequently start and stop, the traffic environment status information on the main road is obtained in real time and pre-processed after acquisition; Extracting pollutant accumulation information and traffic fluctuation information from the pre-processed traffic environment status information on the trunk roads, and analyzing them after extraction to generate an instantaneous pollutant concentration change rate index and a traffic fluctuation intensity coefficient respectively; An urgency assessment model is constructed for the generated instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient to generate an urgency coefficient. The generated urgency coefficient is compared with the pre-set urgency coefficient threshold range. The urgency of adjusting the data collection time interval of sensor equipment on the main roads is evaluated based on the comparison results, and the urgency is divided into three situations: high urgency, medium urgency and low urgency.

3. The atmospheric environment monitoring and early warning method according to claim 2, characterized in that: The logic for obtaining the instantaneous pollutant concentration change rate index and the traffic fluctuation intensity coefficient is as follows: Extract the pollutant accumulation information from the pre-processed traffic environment status information on the trunk road, including the actual wind speed on the trunk road at different times over a period of time, the average concentration of pollutants collected by the sensor equipment installed on the trunk road, and the pollutant emissions generated by all vehicles on the trunk road, and calibrate them respectively. , and , Indicates that within a period of time The actual wind speed on the main road at the moment, Indicates that within a period of time The average concentration of pollutants collected by sensors installed on the main roads at all times. Indicates that within a period of time The amount of pollutant emissions generated by all vehicles on the main roads at any given moment, , is a positive integer; Calculate the instantaneous pollutant concentration change rate index. The specific calculation formula is as follows: In the formula, is the instantaneous pollutant concentration change rate index; The traffic fluctuation information is extracted from the pre-processed traffic environment status information on the trunk road, including the rate of change of the average acceleration of all vehicles on the trunk road at different times within a period of time, the standard deviation of the average speed of all vehicles on the trunk road, the total number of starts and stops of all vehicles on the trunk road, and the ratio of vehicle flow to road design capacity, and they are calibrated as , , and , Indicates that within a period of time The rate of change of the average acceleration of all vehicles on the main road at the moment, Indicates that within a period of time The standard deviation of the average speed of all vehicles on the main road at the moment, Indicates that within a period of time The total number of starts and stops of all vehicles on the main road at the moment, Indicates that within a period of time The ratio of vehicle flow at any given moment to the road design capacity; Calculate the traffic fluctuation intensity coefficient. The specific calculation formula is as follows: In the formula, is the traffic fluctuation intensity coefficient.

4. The atmospheric environment monitoring and early warning method according to claim 3, characterized in that: The generated instantaneous pollutant concentration change rate index and traffic fluctuation intensity coefficient Construct an urgency assessment model and generate an urgency coefficient through weighted summation , and the generated urgency coefficient The pre-set urgency threshold interval The comparison is conducted, and the urgency of adjusting the data collection time interval of the sensor equipment on the trunk roads is evaluated based on the comparison results. The urgency is divided into three situations: high urgency, medium urgency and low urgency. The specific comparison analysis is as follows: like ,The urgency of adjusting the data collection time interval of sensor equipment on the trunk road is low urgency; like ,The urgency of adjusting the data collection time interval of sensor equipment on the trunk roads is medium urgency; like ,The urgency of adjusting the data collection time interval of sensor equipment on the main roads is high urgency.

5. The atmospheric environment monitoring and early warning method according to claim 4, characterized in that: Based on the evaluation and analysis results, a warning and collection interval adjustment mechanism is constructed and applied, and corresponding warning and collection interval adjustment measures are taken for different urgency situations, specifically: In high-urgency situations, early warnings are issued immediately and the data collection interval of sensor equipment is shortened; In cases of medium urgency, continuously monitor changes in pollutant concentrations and adjust the collection interval of sensor equipment according to a predetermined collection frequency range; In low-urgency situations, the data collection frequency of the current sensor equipment is maintained, and no warning is issued.

6. The atmospheric environment monitoring and early warning method according to claim 5, characterized in that: During the application of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the trunk road is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road by the mechanism meets the preset response requirements, and the early warning and collection interval adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps: In the application process of the early warning and collection interval adjustment mechanism, the response feedback information of the sensor equipment on the trunk road is obtained in real time and pre-processed after acquisition; Extracting the collection response information and the collection accuracy information from the pre-processed response feedback information of the sensor equipment on the trunk road, and analyzing them after extraction to generate a data collection response time index and a data collection accuracy index respectively; An adjustment degree evaluation model is constructed for the generated data collection response time index and data collection accuracy index to generate an adjustment coefficient. The generated adjustment coefficient is compared with the pre-set adjustment coefficient threshold. Based on the comparison results, it is evaluated whether the adjustment degree of the data collection time interval of sensor equipment on the main road meets the preset response requirements. Based on the evaluation results, the early warning and collection interval adjustment mechanism is optimized.

7. The atmospheric environment monitoring and early warning method according to claim 6, characterized in that: The acquisition logic of the data acquisition response time index and the data acquisition accuracy index is as follows: The collection response information in the response feedback information of the sensor equipment on the trunk road that has been preprocessed is extracted, including the response time from the sensor equipment receiving the instruction to adjust the data collection interval to adjusting the data collection interval, the time point when the condition that requires the collection interval adjustment is detected and the command is issued, and the time spent by the sensor equipment in transmitting the collected data to the central control system, and they are calibrated as , and , Indicates the time it takes for the sensor device to respond to the instruction to adjust the data collection interval and adjust the data collection interval. Indicates the time point when the condition that requires adjustment of the collection interval is detected and a command is issued. It indicates the time it takes for the sensor device to transmit the collected data to the central control system; Calculating Data Collection Response Time Index , according to the formula: ; The collection accuracy information in the response feedback information of the sensor equipment on the trunk road after preprocessing is extracted, including the change rate of the data collection frequency of the sensor equipment after adjusting the data collection time interval, the change rate of the pollutant concentration collected by the sensor equipment at different times within a period of time, and the change rate of the pollutant concentration collected by the sensor equipment at different times within a period of time, and the change rate of the pollutant concentration collected by the sensor equipment. , and , It indicates the rate of change of the data collection frequency after the sensor device adjusts the data collection time interval. Indicates that the sensor device adjusts the data collection time interval within a period of time The rate of change of pollutant concentration collected by the sensor equipment at all times, Indicates that the sensor device adjusts the data collection time interval within a period of time The rate of change of pollutant concentration collected by the sensor equipment at each moment, , is a positive integer; Calculate the data collection accuracy index. The specific calculation formula is as follows: In the formula, It is the data collection accuracy index.

8. The atmospheric environment monitoring and early warning method according to claim 7, characterized in that: Response time index for generated data collection and data acquisition accuracy index Construct an adjustment degree assessment model and generate adjustment coefficients through weighted summation , and the resulting adjustment factor With the preset adjustment factor threshold Compare and evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road meets the preset response requirements according to the comparison results, and optimize the warning and collection interval adjustment mechanism according to the evaluation results. The specific comparison and analysis are as follows: like ,The degree of adjustment of the data collection time interval of the sensor equipment on the main roads by this mechanism does not meet the preset response requirements, and the early warning and collection interval adjustment mechanism needs to be optimized, including: adjusting the weight ratio of the data collection response time index and the data collection accuracy index, optimizing the data transmission path to reduce delay, improving the dynamic adjustment algorithm of the collection interval to enhance the sensitivity to the urgency changes, and optimizing the prediction model of the system through machine learning to improve the accuracy of the collection interval adjustment; like ,The degree of adjustment of the data collection time interval of the sensor equipment on the main roads meets the preset response requirements, and there is no need to optimize the early warning and collection interval adjustment mechanism.

9. An atmospheric environment monitoring and early warning device, used to implement the atmospheric environment monitoring and early warning method according to any one of claims 1 to 8, characterized in that: It includes traffic status analysis module, urgency assessment module, warning and collection interval adjustment module, response feedback assessment module and dynamic optimization module; Traffic status analysis module, which obtains traffic status information on trunk roads in real time during peak traffic hours, and analyzes it after acquisition to identify whether there are frequent vehicle starts and stops during peak traffic hours; The urgency assessment module, when identifying the frequent starting and stopping of vehicles, obtains the traffic environment status information on the trunk road in real time, analyzes it after acquisition, and assesses the urgency of adjusting the data collection time interval of the sensor equipment on the trunk road, and divides it into three situations according to the urgency: high urgency, medium urgency and low urgency; The warning and collection interval adjustment module builds and applies the warning and collection interval adjustment mechanism based on the evaluation and analysis results, and takes corresponding warning and collection interval adjustment measures for different urgency situations; The response feedback evaluation module obtains the response feedback information of the sensor equipment on the trunk road in real time during the application of the early warning and collection interval adjustment mechanism, and analyzes it after acquisition to evaluate whether the adjustment degree of the data collection time interval of the sensor equipment on the trunk road by the mechanism meets the preset response requirements, and optimizes the early warning and collection interval adjustment mechanism according to the evaluation results; During long-term application, the dynamic optimization module continuously optimizes the warning and collection interval adjustment mechanism based on traffic environment status information, collection interval adjustment effects and warning trigger feedback, ensuring that the system can flexibly adjust the collection interval and accurately trigger warnings under different traffic and pollutant changes.

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