Dynamic monitoring method for carbon emission in coal mining working area

Abstract

The embodiment of the invention discloses a coal mining working area carbon emission dynamic monitoring method, and belongs to the field of gas monitoring, and the method comprises the steps: dividing a monitoring area into a real-time operation area and a peripheral monitoring area; planning a ground monitoring path of a ground platform in the real-time operation area, and planning an air monitoring path of an air platform in the peripheral monitoring area; respectively collecting monitoring data along a ground monitoring path and an air monitoring path by using a ground platform and an air platform; eliminating interference turbulence data introduced by vibration of equipment in the coal mining working area from the monitoring data to generate effective data; and calculating the carbon emission of the real-time operation area and the peripheral monitoring area based on the effective data. Through path planning and mobile cooperative data monitoring, the influence of strong airflow disturbance in a coal mining working area is effectively overcome, and accurate monitoring of carbon emission is realized.

Description

Technical Field

[0001] This invention relates to the field of gas monitoring technology, and in particular to a method for dynamic monitoring of carbon emissions in coal mining areas. Background Technology

[0002] Coal, as an important fossil fuel, has long occupied a central position in the global energy structure. Its centuries-long history of mining has provided a key driving force for the Industrial Revolution and the development of modern society.

[0003] In the coal production chain, the mining stage (encompassing core operations such as coal seam stripping, blasting, and loading) is not only a direct source of greenhouse gases such as methane within the mine pit, but also the main stage where large mining equipment operates intensively and consumes a large amount of energy. Therefore, accurate monitoring of carbon emissions during the mining stage is fundamental to understanding the overall total carbon emissions and dynamic changes in open-pit coal mines. Accurate monitoring not only provides crucial data support for scientific decision-making, but also directly improves the emission reduction efficiency of mining operations, powerfully promoting the green transformation of the coal industry.

[0004] However, existing technologies for monitoring dynamic carbon emissions during the mining process have significant limitations. Fixed monitoring stations are located in fixed positions, making it difficult to cover the moving coal mining area. Furthermore, their measurement range is limited, only obtaining single-point concentration information. In the presence of airflow disturbances that cause measurement errors, the reliability of the monitoring data is affected, making it difficult to accurately assess the precise carbon emissions of the mining area. Summary of the Invention

[0005] This invention provides a method for dynamic monitoring of carbon emissions in coal mining areas, addressing the technical problem that existing technologies cannot meet the requirements for accurate carbon emission monitoring due to insufficient accuracy of monitoring data.

[0006] This invention provides a method for dynamic monitoring of carbon emissions in coal mining areas, comprising: Real-time acquisition of operating parameters and equipment vibration frequency data of equipment in the coal mining area, including advance speed, current boundary and next area information; Based on the aforementioned operating parameters, the monitoring area is divided into a real-time operation area and a peripheral monitoring area. Based on the first monitoring weight corresponding to the real-time operation area, the ground monitoring path of the ground platform in the real-time operation area is planned; Based on the second monitoring weight corresponding to the outer monitoring area, the aerial monitoring path of the aerial platform in the outer monitoring area is planned within the outer monitoring area; Using the ground platform and the aerial platform, monitoring data are collected by moving along the ground monitoring path and the aerial monitoring path, respectively. The monitoring data includes wind speed data, gas concentration data and location data. Based on the equipment vibration frequency data, interference turbulence data introduced by the vibration of the equipment in the coal mining area is removed from the wind speed data and the gas concentration data to generate effective wind speed data and effective gas concentration data. The carbon emissions of the real-time operating area and the surrounding monitoring area are calculated based on the effective wind speed data, effective gas concentration data, and location data, respectively.

[0007] This invention provides a method for dynamic monitoring of carbon emissions in a coal mining area. The method involves acquiring real-time operating parameters and vibration frequency data of equipment in the coal mining area. The operating parameters include advance speed, current boundary, and next area information. Based on these parameters, the monitoring area is divided into a real-time operation area and a peripheral monitoring area. A ground monitoring path for a ground platform is planned within the real-time operation area according to a first monitoring weight. An aerial monitoring path for an aerial platform is planned within the peripheral monitoring area according to a second monitoring weight. The ground platform and aerial platform are used to collect monitoring data along the ground and aerial monitoring paths, respectively. The monitoring data includes wind speed data, gas concentration data, and location data. Based on the equipment vibration frequency data, interference turbulence data introduced by the vibration of the coal mining equipment is removed from the wind speed and gas concentration data to generate effective wind speed and effective gas concentration data. Finally, the carbon emissions of the real-time operation area and the peripheral monitoring area are calculated based on the effective wind speed data, effective gas concentration data, and location data. This invention plans the movement paths of both ground-based and aerial monitoring platforms within their respective areas, assigning corresponding coefficient weights to paths in different monitoring areas to optimize monitoring priority and execution order. The platforms move along the predetermined paths within their monitoring areas, synchronously collecting data and further eliminating interfering data to achieve carbon emission monitoring. Compared to existing technologies, in the complex environment of strong airflow disturbances in coal mining areas, this invention achieves high-precision data acquisition through a combination of dynamic weighted path planning and mobile collaborative monitoring. This lays a solid foundation for effectively identifying and eliminating interfering data, ultimately significantly reducing the impact of airflow disturbances and achieving accurate carbon emission monitoring. This method not only overcomes the bottleneck of limited coverage in traditional monitoring methods but also overcomes the severe impact of airflow disturbances caused by coal mining equipment on the monitoring process, thereby greatly improving data reliability and accuracy. It further enhances data usability, providing technical support for the accurate calculation and dynamic management of carbon emissions in coal mining areas. Attached Figure Description

[0008] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0009] Figure 1 This is a flowchart illustrating the dynamic monitoring method for carbon emissions in coal mining areas provided in Embodiment 1 of the present invention. Figure 2 This is a flowchart of the method for removing interfering turbulent flow data in the dynamic monitoring of carbon emissions in coal mining areas provided in Embodiment 1 of the present invention; Figure 3 This is a flowchart illustrating the calculation of carbon emissions in the dynamic monitoring method for carbon emissions in coal mining areas as described in Embodiment 1 of the present invention. Figure 4 This is a flowchart illustrating the dynamic monitoring method for carbon emissions in coal mining areas provided in Embodiment 2 of the present invention. Detailed Implementation

[0010] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0011] Example 1 Figure 1 This is a flowchart of a method for dynamic monitoring of carbon emissions in coal mining areas according to Embodiment 1 of the present invention. This embodiment is applicable to the dynamic monitoring of carbon emissions in coal mining areas with airflow disturbances, and specifically includes the following steps: Step 110: Real-time acquisition of the operating parameters and vibration frequency data of the equipment in the coal mining area. The operating parameters include the advance speed, current boundary, and next area information.

[0012] For example, a real-time communication link can be established with the existing production scheduling system of the coal mine through a dedicated industrial-grade data interface, directly extracting core parameters reflecting the dynamics of coal mining operations from the scheduling system. These parameters include, but are not limited to: advance speed, current boundary, and information on the next area. The advance speed can be regarded as the rate at which the coal mining machine advances along the working face, and the update frequency of the subsequent monitoring area division can be determined using the advance speed; the current boundary can be expressed in the form of latitude and longitude coordinates, which can accurately define the core range of the current drilling and blasting, mining and loading operations. After clarifying the subsequent advance direction and distance of the coal mining operation, the pre-planned range of the next working area can be calculated in advance by combining the current boundary.

[0013] For example, the industrial-grade data interface can be a RESTful API or OPCUA protocol interface that conforms to coal mine safety production standards. This interface has three major characteristics: encrypted transmission, low latency, and high compatibility. For example, the advancement speed can be 8 meters / day, the current boundary is 116°23′-116°25′ east longitude and 39°56′-39°58′ north latitude, and the next area information is to advance 50 meters northeast.

[0014] For example, equipment vibration can induce disruptive turbulence, and equipment vibration frequency data is a key indicator for identifying and quantifying this disruptive turbulence. For equipment vibration frequency data, an industrial-grade triaxial MEMS accelerometer is selected as the core component to collect the vibration spectrum. Optionally, the industrial-grade triaxial MEMS accelerometer can be the ADI ADXL355. To accommodate both the low-frequency vibrations of mining trucks (5-30Hz) and the high-frequency impacts of drilling rigs (greater than 100Hz), the sensor sampling rate is set to 1kHz. This ensures no aliasing in low-frequency vibrations and no missed detections in high-frequency impacts, while also reserving redundancy for subsequent downsampling, filtering, and other data processing.

[0015] Step 120: Based on the operating parameters, the monitoring area is divided into a real-time operation area and an external monitoring area.

[0016] Based on the real-time working parameters (advancement speed, current boundary, and next area information) obtained in step 110, the monitoring area can be divided into a real-time operation area and an outer monitoring area.

[0017] For example, the current boundary (e.g., 116°23′-116°25′ E, 39°56′-39°58′ N) is converted to Cartesian coordinates using a coordinate transformation tool to determine the precise range of the current operation. Then, combined with the advance speed (e.g., 8 meters / day) and the direction of the next area (e.g., advancing approximately 50 meters northeast), the dynamic extension trend of the operation is clarified. Subsequently, the areas of the real-time operation zone and the peripheral monitoring zone are defined. The real-time operation zone perfectly matches the core area of ​​the current operation, covering approximately 8,000 square meters, corresponding to the drilling and blasting, and mining / loading operation areas, where carbon emissions and equipment interference are most concentrated. The peripheral monitoring zone is based on the boundary of the real-time operation zone, including a ring-shaped area extending 20 meters outward from the real-time operation zone and the area outside the ring-shaped area to the next advance boundary. By defining the range of the real-time operation zone and the peripheral monitoring zone, different monitoring focuses can be assigned to different areas. For real-time operation zones with high gas emissions, more complex monitoring paths can be planned and more frequent monitoring intervals can be set for data monitoring in order to more accurately grasp gas emissions. For peripheral monitoring areas with low gas emissions, monitoring paths and monitoring intervals adapted to their emission conditions will be planned for data monitoring.

[0018] Step 130: Based on the first monitoring weight corresponding to the real-time operation area, plan the ground monitoring path of the ground platform in the real-time operation area.

[0019] Ground platforms serve as the carriers for performing monitoring tasks on the ground in coal mining areas. Monitoring weight can be considered an indicator of importance. Monitoring needs vary at different locations within a coal mining area. Real-time monitoring areas require ground platforms to conduct monitoring along denser paths, while peripheral monitoring areas, due to their different characteristics, can utilize aerial platforms to perform monitoring along relatively sparse paths. This is because real-time monitoring areas are not only concentrated areas of carbon emissions but are also frequently affected by equipment operation, thus making monitoring of these areas more important. Correspondingly, the first monitoring weight corresponding to real-time monitoring areas is higher; that is, the ground monitoring paths managed by the ground platform are assigned a higher weight, greater than the second monitoring weight corresponding to peripheral monitoring areas.

[0020] For example, the ground platform can be a customized tracked inspection vehicle equipped with a 15-meter optical path TDLAS device for... The monitoring wavelengths are 1.65μm and 3.3μm, with a monitoring range of 0-500ppm and an accuracy of ±1ppm. It is also equipped with a three-dimensional ultrasonic anemometer, with a measurement range of 0-30m / s and an accuracy of ±0.1m / s. Its optical path coverage can be automatically matched to the coal mining machine's operating unit. Since the real-time operating area is the core source of carbon emissions in the coal mining area, the corresponding area's first monitoring weight is set to 1.5. This weight is higher than that of the outer monitoring area and is used to guide the algorithm to prioritize the planning of the ground platform's path in this area. The planning of the ground platform's path in this area is achieved using the following formula: in, Let $n$ be the total cost of reaching the real-time job area from the starting node via node $n$. This represents the actual cost incurred from the starting node to node n. The first monitoring weight corresponds to the real-time work area, and the Euclidean distance is the Euclidean distance between node n and the center point of the real-time work area. This formula first calculates the actual cost incurred from the starting node to the current node, then combines this with the Euclidean distance between the node and the center point of the real-time work area, and adjusts the Euclidean distance term using the first monitoring weight to finally obtain the total cost from the starting node to the real-time work area. By selecting the node with the lowest total cost for advancement, the ground platform can travel along efficient, less redundant paths and densely cover the real-time work area, reducing the time and resources consumed by unnecessary paths, thereby improving the accuracy of real-time work area monitoring.

[0021] For example, for the ground platform of the real-time operation area, the path is designed as a parallel route with a horizontal spacing of 5 meters. It is updated every 30 minutes according to the progress of the operation. For example, the path planned at 8:00 am covers the western half of the real-time operation area, and at 8:30 am it is adjusted to cover the eastern half to match the scope of the operation after the progress.

[0022] Step 140: Based on the second monitoring weight corresponding to the outer monitoring area, plan the aerial monitoring path of the aerial platform in the outer monitoring area.

[0023] Aerial platforms can serve as carriers for performing tasks such as monitoring in the air within coal mining areas. The second monitoring weight corresponding to the peripheral monitoring area indicates that the aerial monitoring path managed by the aerial platform is assigned a lower weight, which is less than the first monitoring weight corresponding to the real-time operation area. This is because carbon emissions in the peripheral monitoring area are relatively dispersed, and equipment operation interference is weaker than in the real-time operation area, hence its smaller monitoring weight.

[0024] For example, the aerial platform uses a hexacopter UAV equipped with a 3-meter optical path open-circuit gas monitoring device, and maintains a stable flight altitude of 8 meters. This device monitors... The monitoring accuracy is ±2ppm. The second monitoring weight corresponding to the outer monitoring area is set to 1.0, which is lower than that of the real-time operation area, and is used to guide the algorithm to plan the path of the ground platform in this area with secondary priority. The aerial monitoring path of the aerial platform is planned in the following manner: in, The total cost of reaching the outer monitoring area from the starting node via node n. This represents the actual cost incurred from the starting node to node n. The second monitoring weight corresponds to the outer monitoring area, and the Euclidean distance is the Euclidean distance between node n and the center point of the outer monitoring area. This formula first calculates the actual cost incurred from the starting node to the current node, then combines this with the Euclidean distance between the node and the center point of the outer monitoring area, and adjusts the Euclidean distance term using the second monitoring weight to finally obtain the total cost from the starting node to the outer monitoring area. By selecting the node with the lowest total cost for advancement, the aerial platform can travel along less redundant paths, reducing the time and resources consumed by unnecessary paths, thereby improving the accuracy of monitoring the outer monitoring area.

[0025] For example, for an aerial platform in the outer monitoring area, the path is designed with a flight path interval of 30 meters, and an "S" shaped flight path is used to perform a single scan task; at the same time, the path is updated every 2 hours.

[0026] Step 150: Using the ground platform and the aerial platform, move along the ground monitoring path and the aerial monitoring path to collect monitoring data respectively. The monitoring data includes wind speed data, gas concentration data and location data.

[0027] The ground platform moves along the planned ground monitoring path at a predetermined speed, collecting real-time wind speed data, gas concentration data, and location data of the operating area at regular intervals; the aerial platform moves along the planned aerial monitoring path, collecting wind speed data, gas concentration data, and location data of the surrounding monitoring area at regular intervals. For example, a ground inspection vehicle equipped with a 15-meter optical path TDLAS device and a three-dimensional ultrasonic anemometer travels along a parallel route with lateral intervals of 5 meters at a speed of 0.7 m / s; it collects a set of data every hour, with the total data collection time being the same as the mining time in the coal mining area. For instance, the first data collection might show wind speed components ur=1.2 m / s, vr=0.8 m / s, and wr=0.3 m / s. Location data obtained from a concentration of 35 ppm and real-time positioning coordinates; wind speed components collected in the next hour: ur=1.1m / s, vr=0.9m / s, wr=0.2m / s. Concentration of 34 ppm and location coordinates were continuously generated as the vehicle traveled. The drone is equipped with a 3-meter optical path open-circuit gas monitoring device. It covers an area of ​​approximately 5000 square meters in a single operation using an "S"-shaped flight path, performing a scanning mission every two hours. Considering the drone's energy consumption, for example, the initial wind speed components were collected as us=1.5m / s, vs=0.6m / s, and ws=0.4m / s. The concentration was 28 ppm, and the location coordinates obtained from the drone's GPS trajectory were used to generate multiple new sets of data every two hours during the flight.

[0028] Collecting monitoring data by combining dynamic weighted path planning with mobile collaborative monitoring effectively compensates for the shortcomings of fixed monitoring stations and traditional point sensors, offering significant advantages in data acquisition. In terms of coverage and spatial representativeness, fixed monitoring stations have limited data coverage and poor spatial representativeness due to their fixed locations. However, mobile monitoring devices using ground and aerial platforms that travel along planned routes can adjust their position as the coal face moves. Overlaying dynamic weighted path planning optimizes the path based on the monitoring weight of real-time work areas, prioritizing areas with higher weights. This allows the ground platform to more accurately focus on the moving work face, avoiding blind spots and providing a more comprehensive reflection of the emissions situation across the entire work face. Furthermore, the combined use of ground and aerial platforms enables large-area coverage in terms of monitoring range and dynamic flux capture capability.

[0029] Step 160: Based on the equipment vibration frequency data, remove the interfering turbulence data introduced by the vibration of the equipment in the coal mining area from the wind speed data and the gas concentration data to generate effective wind speed data and effective gas concentration data.

[0030] In coal mining areas, the vibration of equipment during operation generates disruptive turbulence, which affects the accuracy of wind speed and gas concentration data. Equipment vibration frequency data reflects the characteristics of the vibration. Based on this, we can filter and remove data sets related to turbulence caused by equipment vibration from wind speed and gas concentration data previously collected from ground and aerial platforms. Ultimately, we retain wind speed data (i.e., effective wind speed data) and gas concentration data (i.e., effective gas concentration data) that are undisturbed or have negligible interference and accurately reflect the conditions of the working area.

[0031] The following is combined with Figure 2 The specific implementation of step 160 will be further described below. Figure 2 This is a flowchart illustrating the process of removing interfering turbulent flow data in the dynamic monitoring method for carbon emissions in coal mining areas according to Embodiment 1 of the present invention. Figure 2 As shown, step 160 may include the following steps: Step 1610: Based on the equipment vibration frequency data, perform coherent analysis on the wind speed data and the gas concentration data to determine the interfering turbulence data; Step 1620: Use an adaptive notch filter to suppress the interfering turbulence data and generate filtered wind speed data and filtered gas concentration data. Step 1630: Wiener filtering is used to reconstruct effective wind speed data and effective gas concentration data by frequency domain compensation of the filtered wind speed data and filtered gas concentration data.

[0032] For example, based on the equipment vibration frequency data obtained in the aforementioned steps, a coherence analysis is further performed on this data along with wind speed data and gas concentration data to calculate the coherence coefficient γ between them. 2 γ 2 The value ranges from 0 to 1. The closer the value is to 1, the stronger the correlation between the wind speed data or gas concentration data and the equipment vibration signal. 2 A value greater than 0.6 indicates that the corresponding data contains interference introduced by equipment vibration. In this case, the adaptive notch filter will accurately generate a zero-crossing band for the interference frequency determined by coherence analysis, significantly weakening or even eliminating the interference signal mixed in the original data. However, this process may inadvertently damage a small number of valid signals near the same frequency; therefore, frequency domain compensation and reconstruction are required through Wiener filtering to ultimately obtain the valid wind speed and valid gas concentration data.

[0033] By using the dynamic weighted path planning and mobile collaborative monitoring method described in the previous steps to collect monitoring data, the data quality can be greatly improved. Then, interference and turbulence removal is performed on the monitoring data to make the final monitoring data more accurate. Furthermore, the impact of airflow disturbance from coal mining equipment on the monitoring process is overcome, ensuring that the data can truly support subsequent work such as carbon emission calculation and environmental assessment of the work area.

[0034] Optionally, before step 160, the following can be added: using a filtering algorithm to remove abnormal data from the data; the abnormal data includes data collected during high water vapor and cold trap switching, data during periods when the rate of change of the first difference in gas concentration exceeds the threshold for three consecutive times, and data where the mean of the first data remains unchanged for two consecutive hours.

[0035] High water vapor data refers to water vapor content detected by the sensor exceeding a predefined threshold. For example, the normal water vapor content in a coal mining area is typically 5%-15%. If data shows a water vapor content of 30% at a certain time, it is considered high water vapor data. This is because excessive water vapor will adhere to the surface of the gas sensor, leading to… The monitoring results for gas concentrations are distorted and do not reflect the true gas emission status, so they must be discarded. Data collected during cold trap switching is related to the operation of the equipment itself. The cold trap is a component in the monitoring instrument used to remove water vapor and purify the gas to be measured. When the instrument switches cold traps, the airflow and temperature inside the instrument will fluctuate temporarily. At this time, the data collected by the sensor will show abnormal jumps, and such data must also be classified as abnormal and discarded. The first difference is the difference between the gas concentrations of two adjacent measurements.

[0036] For example, by using sensor data from ground / airborne platforms and API interfaces with existing coal mine production scheduling systems, real-time environmental conditions (such as gas concentration, water vapor content, wind speed, etc.) and the system's own status (such as the cold trap switching status of instruments) can be acquired. This provides the initial basis for identifying abnormal data, such as real-time capture of water vapor values ​​in the work area, the time point when the cold trap starts switching, and gas concentration data over a continuous period. Next, the acquired data is compared with thresholds set based on physical knowledge and expert experience. If the detected water vapor content is higher than the set standard, or if the data acquisition period falls during a cold trap switching period, the corresponding data will be judged as abnormal. If the rate of change of the first difference in gas concentration exceeds the threshold for three consecutive times (e.g., abnormal fluctuations in the magnitude of the change in the concentration difference between two adjacent values), the data for that period will also be marked as abnormal. Furthermore, if the first mean of a certain type of data remains unchanged for two consecutive hours, such as within two hours... If the average concentration remains consistently at a certain value, deviating from the normal fluctuation pattern under normal conditions, it will also be classified as outlier data. Finally, once the above-mentioned abnormal conditions are met, a filtering algorithm is used to remove these outlier data groups.

[0037] Step 170: Calculate the carbon emissions of the real-time operating area and the surrounding monitoring area based on the effective wind speed data, effective gas concentration data, and location data, respectively.

[0038] Wind speed data quantifies the speed and direction of gas movement, gas concentration data quantitatively measures the content of specific greenhouse gases in the air of a target area, and location data precisely anchors all measurements in time and space. These three types of data together form the basis for monitoring carbon emissions.

[0039] The following is combined with Figure 3 The specific implementation of step 170 will be further described below. Figure 3 This is a flowchart illustrating the calculation of carbon emissions in the dynamic monitoring method for carbon emissions in coal mining areas according to Embodiment 1 of the present invention. Figure 3 As shown, step 170 may include the following steps: Step 1710: Obtain the terrain dip angle and wind direction information of the coal mining area; Step 1720: Based on the terrain inclination angle, the effective wind speed data is processed using the cubic coordinate rotation method to obtain the rotated wind speed components. Step 1730: Calculate the turbulent flow rate of the real-time operating area and the peripheral monitoring area based on the wind speed component and the effective gas concentration data; Step 1740: Correct the turbulent flux to obtain the corrected turbulent flux; Step 1750: Perform density correction on the corrected turbulent flux to obtain the corrected turbulent flux. Step 1760: Calculate the regional flux of the real-time operating area and the surrounding monitoring area based on the corrected turbulent flux, the location data, and the wind direction information; Step 1770: Calculate the carbon emissions of the real-time operation area and the peripheral monitoring area based on the regional flux of the real-time operation area and the peripheral monitoring area.

[0040] It should be noted that both the ground platform and the aerial platform collect multiple sets of data from the real-time operating area and the surrounding monitoring area every hour. For ease of explanation, only one set of data collected in the real-time operating area is used as an example here. For instance, the terrain dip angle of the coal mining area is first measured to be 3°. Based on this dip angle, the effective wind speed data after interference removal is rotated three times to obtain the rotated wind speed components ur=1.18m / s, vr=0.79m / s, and wr=0.29m / s. Subsequently, the rotated vertical wind speed component wr=0.29m / s, the effective CH4 concentration data of 35ppm, and the air density are combined. =1.225kg / m 3 Calculate the initial value of the turbulent flux using the following formula: in, For turbulent flux, air density, The covariance calculation function, This represents the vertical wind speed component after rotation. This provides effective gas concentration data. Furthermore, based on the obtained initial value of the turbulent flux and the sensor height of the inspection vehicle being 1m above the ground, the turbulent flux is corrected using the following formula: in, This is the corrected turbulent flux. Let be the von Kármán constant (taken as 0.4). The altitude is the flight altitude / the height of the inspection vehicle's sensor above the ground (1m). The length of the Monin-Obukhov line is taken as 80m. The corrected turbulent flux is calculated to be 118 kg / (m²). 2 ·h). Furthermore, considering the sensible heat flux of the coal mining area as =50W / m 2 Temperature flux is =0.8W / m 2 and average temperature is =298K, and with correction coefficients μ=0.6 and β=0.4, the corrected turbulent flux is corrected using the following formula: in, This is the corrected turbulent flux. This represents the ratio of the molecular weights of air to water vapor. This is the ratio of the density of dry air to the density of water vapor. For sensible heat flux, For temperature flux, The average temperature. air density, This represents the corrected turbulent flux. Subsequently, using location data obtained from real-time positioning coordinates and wind direction (north wind, 2 m / s), a footprint weighting function is constructed, as shown in the following formula: in, Let be the coordinates of any point in the upwind region. For the coordinates of the measurement point, For wind direction during measurement, For footprint weight function, This defines the area of ​​the real-time operating zone or the surrounding monitoring zone. The regional flux is obtained by integrating the corrected turbulent flux with the footprint weight function. The integration formula is as follows: in, For regional flux, For footprint weight function, This is the corrected turbulent flux. Finally, based on the visible light and infrared imaging equipment mounted on the aerial platform, the length L of the real-time working area and the dynamic coal mining area at time t are obtained. =10000m 2 Given a time interval Δt = 1 hour, calculate the carbon emissions corresponding to this example data set (i.e., this monitoring period). The calculation formula is as follows: Among them, spatial average flux Calculated as Substituting these values ​​into the formula, the carbon emissions for the example data set (i.e., the monitoring period) are calculated to be 120,000 kg. Finally, the carbon emissions for all valid data sets (i.e., each monitoring period) in the real-time operating area are summed to obtain the total carbon emissions for that area. Additionally, the carbon emission intensity of the valid data set is calculated based on the spatial average flux and length, using the following formula: in, Where L is the spatial average flux, and L is the length of the real-time operating area or the peripheral monitoring area. The time interval is defined as the sum of the carbon emission intensities of all valid data sets in the outer monitoring area, which is the total carbon emission intensity of that area.

[0041] Additionally, it should be noted that due to the energy consumption of drones, the total data collection time for drones along their aerial monitoring path to collect data from the outer monitoring area differs from the mining operation time in the coal mining area. Therefore, when calculating the carbon emissions for all valid data sets (i.e., each monitoring time period) in the outer monitoring area, the total data collection time will vary. Then, the total carbon emissions of the outer monitoring area should be calculated using the following formula: in, Let T represent the total carbon emissions of the outer monitoring area, and T be the number of valid data sets (monitoring time periods). Similarly, the carbon emission intensity of all valid data sets (i.e., each monitoring time period) in the outer monitoring area should be calculated. Then, the total carbon emission intensity of the outer monitoring area should be calculated using the following formula: in, T represents the total carbon emission intensity of the outer monitoring area, and T represents the number of valid data sets (monitoring time periods).

[0042] This embodiment acquires real-time operating parameters and equipment vibration frequency data of the coal mining area equipment. The operating parameters include advance speed, current boundary, and next area information. Based on these parameters, the monitoring area is divided into a real-time operation area and a peripheral monitoring area. According to a first monitoring weight corresponding to the real-time operation area, a ground monitoring path is planned within the real-time operation area. According to a second monitoring weight corresponding to the peripheral monitoring area, an aerial monitoring path is planned within the peripheral monitoring area. Using the ground and aerial platforms, monitoring data is collected along the ground and aerial monitoring paths, respectively. The monitoring data includes wind speed data, gas concentration data, and location data. Based on the equipment vibration frequency data, interference turbulence data introduced by the vibration of the coal mining area equipment is removed from the wind speed and gas concentration data to generate effective wind speed and effective gas concentration data. Carbon emissions are calculated for the real-time operation area and the peripheral monitoring area based on the effective wind speed, effective gas concentration, and location data, respectively. The ground and aerial monitoring platforms are planned for movement within each area, and corresponding coefficient weights are assigned to the paths in different monitoring areas to optimize monitoring priority and execution order. The platform moves along a predetermined path within the monitoring area, synchronously collecting data and further filtering out interfering data to monitor carbon emissions. Compared to existing technologies, in the complex environment of strong airflow disturbances in coal mining areas, this invention combines dynamic weighted path planning with mobile collaborative monitoring to achieve high-precision data acquisition. This lays a solid foundation for effectively identifying and eliminating interfering data, ultimately significantly reducing the impact of airflow disturbances and achieving accurate carbon emission monitoring. This method not only overcomes the bottleneck of limited coverage in traditional monitoring methods but also overcomes the severe impact of airflow disturbances caused by coal mining equipment on the monitoring process, thereby greatly improving the reliability and accuracy of the data. The enhanced data availability provides technical support for the accurate calculation and dynamic management of carbon emissions in coal mining areas.

[0043] Example 2 Figure 4 This is a flowchart of a dynamic monitoring method for carbon emissions in a coal mining area provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiment. The step of planning the ground monitoring path of the ground platform in the real-time operation area according to the first monitoring weight corresponding to the real-time operation area and the step of planning the aerial monitoring path of the aerial platform in the outer monitoring area according to the second monitoring weight corresponding to the outer monitoring area further include: acquiring real-time parameter data of the coal mining area environment and coal mining area equipment; comparing the real-time parameter data with pre-set prior knowledge to obtain a trigger event; and updating the path in the monitoring area by adjusting the parameter of the actual cost incurred from the starting node to node n based on the trigger event.

[0044] See Figure 4 The method for dynamic monitoring of carbon emissions in the coal mining area includes: Step 210: Real-time acquisition of the operating parameters and vibration frequency data of the equipment in the coal mining area. The operating parameters include the advance speed, current boundary, and next area information.

[0045] Step 220: Based on the operating parameters, the monitoring area is divided into a real-time operation area and an external monitoring area.

[0046] Step 230: Based on the first monitoring weight corresponding to the real-time operation area, plan the ground monitoring path of the ground platform in the real-time operation area; based on the second monitoring weight corresponding to the outer monitoring area, plan the aerial monitoring path of the aerial platform in the outer monitoring area.

[0047] Step 240: Obtain real-time parameter data of the coal mining area environment and equipment; compare the real-time parameter data with pre-set prior knowledge to obtain trigger events; based on the trigger events, adjust the parameters of the actual cost incurred from the starting node to node n to update the path in the monitoring area.

[0048] Prior knowledge refers to pre-set rules, thresholds, or baseline information based on coal mine operation patterns, monitoring needs, safety standards, or historical experience, used to determine whether real-time data is normal or whether path adjustments need to be triggered. Triggering events refer to specific situations where real-time collected environmental or equipment parameter data, after being compared with pre-set prior knowledge, is determined to exceed the normal range or meet specific adjustment conditions. These events include three categories: sudden changes in operation speed, equipment movement, and the appearance of high-emission zones.

[0049] For example, the prior knowledge is specifically set as follows: the normal range of operation speed is 0.8-1.2 m / h, and the mutation threshold is ±20%, that is, speeds below 0.64 m / h or above 1.44 m / h are considered abnormal, triggering an event; the criterion for high-emission areas is that the flux value exceeds a preset threshold, such as 150 kg / (m³). 2 •h). First, environmental data (such as gas flux) and real-time equipment parameters (such as operation speed and equipment position) of the coal mining area are acquired through sensors, GPS and other devices. The real-time parameter data is then compared with the prior knowledge mentioned above to determine whether a triggering event has occurred and the type of triggering event.

[0050] For example, if the detected increase in the working face advancement speed exceeds 20%, the triggering event is determined to be a sudden change in the working face advancement speed. At this time, the basic cost parameters of the nodes need to be adjusted, reducing the path node cost in the area of ​​abnormal advancement speed by 30%, so that the ground inspection vehicle slows down its travel speed in this area, increases the data collection density, and ensures coverage of the newly advanced working area. If the detected entry of a new large piece of equipment into the monitoring area, i.e., forming a moving obstacle, the triggering event is determined to be equipment movement. At this time, in the cost function of path planning, a penalty term is added to the nodes around the obstacle. The closer to the obstacle, the greater the penalty, guiding the inspection vehicle or drone to automatically avoid the equipment. If the detected gas flux value in a certain area exceeds 150 kg / (m³), the triggering event is determined to be equipment movement. 2 If the threshold for determining a high emission zone is reached (h), then the event is determined to be the occurrence of a high emission zone. At this time, the passage cost of the path nodes in the area needs to be reduced, such as the original passage cost × 0.5, to prioritize guiding the monitoring platform to cover the high emission zone, obtain more dense emission data, and support subsequent emission reduction analysis.

[0051] Step 250: Using the ground platform and the aerial platform, move along the ground monitoring path and the aerial monitoring path to collect monitoring data respectively. The monitoring data includes wind speed data, gas concentration data and location data.

[0052] Step 260: Based on the equipment vibration frequency data, remove the interfering turbulence data introduced by the vibration of the equipment in the coal mining area from the wind speed data and the gas concentration data to generate effective wind speed data and effective gas concentration data.

[0053] Step 270: Calculate the carbon emissions of the real-time operating area and the surrounding monitoring area based on the effective wind speed data, effective gas concentration data, and location data, respectively.

[0054] This embodiment optimizes the planning of ground monitoring paths for ground platforms within the real-time operation area based on a first monitoring weight corresponding to the real-time operation area, and the planning of aerial monitoring paths for aerial platforms within the outer monitoring area based on a second monitoring weight corresponding to the outer monitoring area. Specifically, it involves: acquiring real-time parameter data of the coal mining area environment and equipment; comparing the real-time parameter data with pre-set prior knowledge to obtain trigger events; and updating the paths in the monitoring area by adjusting the parameters of the actual costs incurred from the starting node to node n based on the trigger events. Using this method, on the one hand, by comparing real-time parameters with prior knowledge, key changes can be accurately captured, avoiding monitoring blind spots caused by the path being out of sync with the actual scenario; on the other hand, by specifically adjusting path parameters (such as node costs and penalties) based on trigger events, the path can densely cover key areas such as high-emission zones to enhance monitoring value and lay the groundwork for subsequent data removal from interference. Furthermore, it allows for local path updates through parameter fine-tuning, improving monitoring efficiency.

[0055] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for dynamic monitoring of carbon emissions in coal mining areas, characterized in that, include: Real-time acquisition of operating parameters and equipment vibration frequency data of equipment in the coal mining area, including advance speed, current boundary and next area information; Based on the aforementioned operating parameters, the monitoring area is divided into a real-time operation area and a peripheral monitoring area. Based on the first monitoring weight corresponding to the real-time operation area, the ground monitoring path of the ground platform in the real-time operation area is planned; Based on the second monitoring weight corresponding to the outer monitoring area, the aerial monitoring path of the aerial platform in the outer monitoring area is planned within the outer monitoring area; Using the ground platform and the aerial platform, monitoring data are collected by moving along the ground monitoring path and the aerial monitoring path, respectively. The monitoring data includes wind speed data, gas concentration data and location data. Based on the equipment vibration frequency data, interference turbulence data introduced by the vibration of the equipment in the coal mining area is removed from the wind speed data and the gas concentration data to generate effective wind speed data and effective gas concentration data. The carbon emissions of the real-time operating area and the surrounding monitoring area are calculated based on the effective wind speed data, effective gas concentration data, and location data, respectively.

2. The method according to claim 1, characterized in that, The step of planning the ground monitoring path of the ground platform within the real-time operation area according to the first monitoring weight corresponding to the real-time operation area includes: Within the real-time operation area, the ground monitoring path of the ground platform is planned as follows: ， in, Let $n$ be the total cost of reaching the real-time job area from the starting node via node $n$. This represents the actual cost incurred from the starting node to node n. The first monitoring weight corresponding to the real-time work area is denoted as n, and the Euclidean distance is the Euclidean distance between node n and the center point of the real-time work area. Accordingly, the step of planning the aerial monitoring path of the aerial platform within the outer monitoring area based on the second monitoring weight corresponding to the outer monitoring area includes: Within the aforementioned peripheral monitoring area, the aerial monitoring path of the aerial platform is planned as follows: ， in, The total cost of reaching the outer monitoring area from the starting node via node n. This represents the actual cost incurred from the starting node to node n. The second monitoring weight corresponding to the outer monitoring area is denoted by , and the Euclidean distance is the Euclidean distance between node n and the center point of the outer monitoring area.

3. The method according to claim 2, characterized in that, The planning of ground monitoring paths for ground platforms within the real-time operation area and the planning of aerial monitoring paths for aerial platforms within the peripheral monitoring area both further include: Acquire real-time parameter data of the coal mining area environment and equipment; The real-time parameter data is compared with pre-set prior knowledge to obtain the trigger event; Based on the triggering event, the parameters of the actual cost incurred from the starting node to node n are adjusted to update the path in the monitoring area; The triggering events include sudden changes in the operation progress speed, equipment movement, and the appearance of high-emission zones; When the triggering event is a sudden change in the operation's progress speed, the parameter is the node's basic cost parameter; When the triggering event is that the device moves, the parameter is a penalty parameter; When the triggering event is the occurrence of the high-emission zone, the parameter is the passage cost parameter.

4. The method according to claim 3, characterized in that, The process of removing interfering turbulence data introduced by the vibration of the equipment in the coal mining area from the wind speed data and the gas concentration data based on the equipment vibration frequency data to generate effective wind speed data and effective gas concentration data includes: Based on the equipment vibration frequency data, coherent analysis is performed on the wind speed data and the gas concentration data to determine the interfering turbulence data. The interfering turbulence data is suppressed by an adaptive notch filter method, and filtered wind speed data and filtered gas concentration data are generated. Wiener filtering is used to reconstruct effective wind speed data and effective gas concentration data by frequency domain compensation of the filtered wind speed data and filtered gas concentration data.

5. The method according to claim 4, characterized in that, The calculation of carbon emissions for the real-time operating area and the surrounding monitoring area based on the effective wind speed data, effective gas concentration data, and location data includes: Obtain the topographic dip angle and wind direction information of the coal mining area; Based on the terrain slope, the effective wind speed data is processed using a cubic coordinate rotation method to obtain the rotated wind speed components. Calculate the turbulent flow rate of the real-time operating area and the surrounding monitoring area based on the wind speed component and the effective gas concentration data; The turbulent flux is corrected to obtain the corrected turbulent flux; The corrected turbulent flux is then subjected to density correction to obtain the corrected turbulent flux. Based on the corrected turbulent flux, the location data, and the wind direction information, the regional flux of the real-time work area and the surrounding monitoring area is calculated. The carbon emissions of the real-time operation area and the peripheral monitoring area are calculated based on the regional flux of the real-time operation area and the peripheral monitoring area.

6. The method according to claim 5, characterized in that, The calculation of turbulent flux in the real-time operating area and the surrounding monitoring area based on the wind speed component and the effective gas concentration data includes: The wind speed components include zonal, meridional and vertical wind speed components; Based on the vertical wind speed component and the effective gas concentration data, the turbulent flow rate of the real-time operating area and the surrounding monitoring area is calculated; The calculation of the turbulent flow rate in the real-time operating area and the surrounding monitoring area is achieved in the following manner: ， in, For turbulent flux, air density, The covariance calculation function, This represents the vertical wind speed component after rotation. This is valid gas concentration data.

7. The method according to claim 6, characterized in that, The step of correcting the turbulent flux to obtain the corrected turbulent flux includes: The flight altitude of the aerial platform and the altitude of the ground platform sensors above the ground are obtained; The turbulence flux is corrected based on the flight altitude and the ground platform sensor's distance from the ground. The correction of the turbulent flux is achieved in the following manner: ， in, This is the corrected turbulent flux. For von Kármán's constant, The altitude of the flight platform sensor is the distance from the ground. The length of the Moning-Obukhov pair; Accordingly, the step of performing density correction on the corrected turbulent flux to obtain the corrected turbulent flux includes: The sensible heat flux, temperature flux, and average temperature of the coal mining area are obtained. The corrected turbulent flux is corrected based on sensible heat flux, temperature flux, and average temperature. The correction of the modified turbulent flux is achieved in the following manner: ， in, This is the corrected turbulent flux. This represents the ratio of the molecular weights of air to water vapor. This is the ratio of the density of dry air to the density of water vapor. For sensible heat flux, For temperature flux, The average temperature. air density, This is the corrected turbulent flux.

8. The method according to claim 7, characterized in that, The calculation of the regional flux of the real-time operating area and the surrounding monitoring area based on the corrected turbulent flux, the location data, and the wind direction information includes: Calculate the footprint weight function for the real-time work area and the surrounding monitoring area based on the location data and the wind direction information; The corrected turbulent flux and the footprint weight function are integrated to obtain the regional flux of the real-time operating area and the peripheral monitoring area. The step weighting function for calculating the real-time work area and the surrounding monitoring area based on the location data and the wind direction information is implemented in the following manner: ， in, Let be the coordinates of any point in the upwind region. For the coordinates of the measurement point, For wind direction during measurement, For footprint weight function, The scope of the real-time operation area or the surrounding monitoring area; The integration operation is implemented in the following way: ， in, For regional flux, For footprint weight function, This is the corrected turbulent flux.

9. The method according to claim 8, characterized in that, The calculation of carbon emissions for the real-time operating area and the surrounding monitoring area based on the regional flux of the real-time operating area and the surrounding monitoring area includes: Using visible light and infrared imaging equipment mounted on an aerial platform, the length of the real-time operating area and the surrounding monitoring area, as well as the dynamic coal mining area at a certain moment, are obtained. Based on the regional flux of the real-time operation area and the peripheral monitoring area, the length of the real-time operation area and the peripheral monitoring area, and the dynamic coal mining area, calculate the carbon emissions of the real-time operation area and the peripheral monitoring area; The calculation of carbon emissions in the real-time operating area and the surrounding monitoring area is achieved in the following manner: ， in, Where L is the spatial average flux, and L is the length of the real-time operating area or the peripheral monitoring area. Let be the mining area at time t. For time intervals, Carbon emissions; Also includes: The emission intensity of the real-time operation area and the peripheral monitoring area is calculated based on the regional flux and length of the real-time operation area and the peripheral monitoring area. The calculation of the emission intensity of the real-time operating area and the peripheral monitoring area based on the regional flux and length of the real-time operating area and the peripheral monitoring area is achieved in the following manner: ， in, The emission intensity per meter of the working unit. Where L is the spatial average flux, and L is the length of the real-time operating area or the peripheral monitoring area. For time intervals.

10. The method according to claim 1, characterized in that, Before the step of removing interfering turbulence data introduced by the vibration of the equipment in the coal mining area from the wind speed data and the gas concentration data based on the equipment vibration frequency data to generate effective wind speed data and effective gas concentration data, the method further includes: Filtering algorithms are used to remove abnormal data from the data; The abnormal data includes data collected during high water vapor and cold trap switching periods, data during periods when the rate of change of the first difference in gas concentration exceeded the threshold for three consecutive times, and data where the mean of the first data remained unchanged for two consecutive hours.

Images