Ozone oxidation method and system for multi-stage slow-release exhaust gas co of mine underground automobile

Abstract

The present application relates to the field of tail gas CO processing, in particular to a mine underground automobile multi-stage slow-release type tail gas CO ozone oxidation method and system, the method comprising: obtaining real-time running state data of the transport automobile and real-time air flow data of the roadway to obtain a current working condition parameter set; predicting the tail gas CO emission intensity based on the parameter set to determine the theoretical total ozone demand; obtaining the predicted spatio-temporal distribution information of the transport automobile driving along the roadway, splitting the theoretical total ozone demand into multiple sub-release doses, and assigning each sub-release dose a release time node and a release spatial position; according to the corresponding release time node and spatial position, sequentially controlling the ozone release device to perform release, so that the ozone axially contacts and oxidizes the CO in the tail gas of the moving automobile along the roadway. The present application realizes the active matching of the ozone release rhythm and the spatio-temporal evolution law of the moving emission source, greatly improves the effective collision probability of ozone molecules and CO and the ozone utilization efficiency, and reduces the tail gas CO concentration in the roadway.

Description

Technical Field

[0001] This invention relates to the field of exhaust CO treatment, specifically to a method and system for ozone oxidation of multi-stage slow-release exhaust CO from underground mining vehicles. Background Technology

[0002] In underground mining transportation, trackless rubber-tired vehicles and explosion-proof diesel-powered transport vehicles are the main equipment for transporting ore, materials, and personnel. Due to the relatively enclosed space and limited ventilation in underground tunnels, the exhaust gases continuously emitted by these vehicles easily accumulate in the work area. Carbon monoxide (CO), being colorless and odorless and possessing a strong binding affinity to hemoglobin, is a typical pollutant threatening the health and safety of miners. To control CO concentration in tunnels, the industry has successively adopted various methods, including in-vehicle purification, ventilation dilution, catalytic conversion, and ozone oxidation. Among these, ozone oxidation technology, with its advantages of rapid reaction, no reliance on precious metal catalysts, and the ability to directly convert CO into carbon dioxide at room temperature, is receiving increasing attention in the field of underground exhaust gas after-treatment.

[0003] Currently, most ozone oxidation solutions used in underground mines follow the approach of centralized release at fixed points. This involves setting up an ozone release terminal at a specific cross-section or chamber in the mine roadway, and then releasing ozone into the roadway space based on real-time monitoring of the CO concentration at that location or at predetermined time intervals. This centralized release model has fundamental flaws when dealing with mobile emission sources. Underground transport vehicles are in continuous motion, and the location, intensity, and duration of their exhaust emissions change in real time with engine operating conditions and vehicle speed, forming a dynamically evolving pollution zone. Large amounts of ozone released at fixed points often fail to effectively follow and synchronize with the exhaust gases from moving vehicles in space and time; most of the ozone is rapidly carried away by the axial airflow in the roadway and diluted before it can fully mix with the exhaust gases. The proportion that actually participates in the CO oxidation reaction is low, resulting in poor ozone utilization and unstable purification effects. Even if some systems attempt to trigger ozone release at the location where a vehicle passes, the lack of prediction of the vehicle's future trajectory and arrival time means that the determination of release timing and dosage still heavily relies on experience or simplified threshold logic, making it difficult to accurately match the spatiotemporal distribution of vehicles throughout the tunnel. When multiple transport vehicles pass densely, existing methods cannot predict the cumulative emission effect and cannot reasonably distribute the ozone dosage across multiple road sections the vehicles will travel, easily leading to excessively high local ozone concentrations, incomplete oxidation, or waste of reagents. In addition, the speed and direction of airflow in underground tunnels fluctuate with ventilation system adjustments and vehicle movement disturbances, further reducing the effective contact probability between ozone and exhaust plumes when released at a fixed location. The root of these problems lies in the fact that existing ozone oxidation methods fail to finely decompose the ozone release process in both temporal and spatial dimensions. They do not convert the required total ozone amount into a multi-stage, progressive, and route-based slow-release strategy based on the expected spatiotemporal distribution of transport vehicles, thus failing to achieve step-by-step contact and gradual deep oxidation of ozone with the exhaust gases of moving vehicles. Therefore, how to dynamically predict CO emission demand based on the real-time operating status of transport vehicles and the airflow data in the tunnel, and how to break down the total ozone demand into multiple sub-release doses according to the expected spatiotemporal distribution of vehicles traveling along the tunnel, and control ozone release sequentially at the corresponding release time nodes and release spatial locations, so that ozone forms a multi-stage slow release and step-by-step reaction efficient oxidation process with vehicle exhaust along the tunnel axis, has become an urgent problem to be solved in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for ozone oxidation of CO from multi-stage slow-release exhaust gas from underground mining vehicles. This addresses the problem of how to dynamically predict CO emission demand based on the real-time operating status of the transport vehicles and the airflow data in the tunnel, and how to break down the total ozone demand into multiple sub-release doses according to the expected spatiotemporal distribution of the vehicles traveling along the tunnel, and sequentially control ozone release at corresponding release time nodes and release spatial locations, so that ozone forms a highly efficient oxidation process of multi-stage slow release and step-by-step reaction with vehicle exhaust gas along the tunnel axis.

[0005] To achieve the above objectives, the following technical solution is adopted.

[0006] An ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles includes the following steps:

[0007] The real-time operating status data of underground transport vehicles and real-time airflow data of roadways are obtained to obtain the current operating condition parameter set; based on the current operating condition parameter set, the exhaust CO emission intensity under the current transport conditions is predicted, and the theoretical total ozone demand required to oxidize the exhaust CO is determined according to the exhaust CO emission intensity.

[0008] The expected spatiotemporal distribution information of the transport vehicle traveling along the alleyway is obtained. Based on the expected spatiotemporal distribution information and the theoretical total ozone demand, the theoretical total ozone demand is divided into multiple sub-release doses, and a corresponding release time node and release spatial location are assigned to each sub-release dose.

[0009] According to the release time nodes and release spatial locations corresponding to each sub-release dose, the ozone release devices at the corresponding locations are controlled to release ozone in sequence, so that the released ozone comes into contact with the exhaust gas of the moving vehicle along the roadway axis and oxidizes the exhaust gas CO.

[0010] Optionally, the step of obtaining real-time operating status data of underground transport vehicles and real-time airflow data of roadways to obtain the current operating condition parameter set specifically includes:

[0011] The engine speed signal, engine load rate signal and exhaust emission temperature signal are read in real time from the on-board diagnostic terminal installed on the transport vehicle. At the same time, the real-time position coordinates and real-time driving speed of the transport vehicle are read from the on-board positioning module to obtain the real-time operating status data of each transport vehicle.

[0012] Real-time airflow data of the tunnel is obtained by reading the current cross-sectional average wind speed data, cross-sectional average wind direction data, and tunnel ambient temperature data from ultrasonic anemometers and temperature sensors installed on the tunnel sidewalls.

[0013] The real-time operating status data and the real-time airflow data of the roadway are associated at the same acquisition time to form a working condition parameter tuple for the current time, and the working condition parameter tuple is stored in the time-series buffer.

[0014] Median filtering is performed on the tuples of operating condition parameters at multiple consecutive sampling times in the time-series buffer to remove abnormal jump values ​​caused by transient interference from sensors. The filtered tuples of operating condition parameters are then arranged in chronological order to form the current set of operating condition parameters.

[0015] Optionally, the step of predicting the exhaust CO emission intensity under the current transportation conditions based on the current operating condition parameter set, and determining the theoretical total ozone demand required to oxidize the exhaust CO based on the exhaust CO emission intensity, specifically includes:

[0016] The engine speed data and engine load rate data of each transport vehicle are extracted from the current working condition parameter set. The engine speed of each transport vehicle is multiplied by the engine load rate to obtain the effective working intensity index of the engine of that transport vehicle. The effective working intensity indexes of all transport vehicles are summed to obtain the total working intensity value at the current moment.

[0017] The total workload value is input into the pre-built CO emission prediction model. The CO emission prediction model takes the total workload value as the input variable and the CO emission mass flow rate per unit time as the output variable. It adopts a multiple linear regression model structure based on the measured data in the mine to calculate the exhaust gas CO emission intensity at the current moment.

[0018] Read the stoichiometric coefficient of ozone and CO pre-stored in the local database, multiply the CO emission intensity of the exhaust gas by the stoichiometric coefficient, and obtain the baseline ozone requirement required for complete reaction.

[0019] Obtain the current tunnel ambient temperature data, query the pre-stored temperature influence coefficient on the reaction rate based on the tunnel ambient temperature data, multiply the baseline ozone demand by the temperature influence coefficient on the reaction rate, and obtain the theoretical total ozone demand.

[0020] Optionally, the step of obtaining the expected spatiotemporal distribution information of the transport vehicle traveling along the alleyway, and based on the expected spatiotemporal distribution information and the theoretical total ozone demand, dividing the theoretical total ozone demand into multiple sub-release doses, and assigning a corresponding release time node and release spatial location to each sub-release dose, specifically includes:

[0021] The current real-time location and current real-time speed of each transport vehicle are obtained from the vehicle positioning module. Based on the current real-time location, current real-time speed and the roadway topology, the estimated arrival time of each transport vehicle to each spatial location point in the roadway within a future preset time window is predicted, and the estimated spatiotemporal trajectory of each transport vehicle is obtained.

[0022] The expected spatiotemporal trajectories of all transport vehicles are superimposed on the time axis and the spatial axis. The number of transport vehicles expected to pass through each spatial location point in the alley within each time interval in the future preset time window is calculated to obtain the spatiotemporal distribution matrix.

[0023] Using the number of transport vehicles expected to pass through each spatial location point in the spatiotemporal distribution matrix as the allocation weight, the theoretical total ozone demand is allocated according to the weight ratio of each spatial location point, so that the spatial location point with more expected transport vehicles receives a larger ozone allocation dose, thus obtaining the initial sub-release dose corresponding to each spatial location point.

[0024] The initial sub-release dose corresponding to each spatial location point is normalized so that the sum of the sub-release doses of all spatial location points is equal to the theoretical total ozone demand, thus obtaining the multiple sub-release doses;

[0025] For each spatial location point, the number of transport vehicles expected to pass through the spatial location point in each time interval is extracted from the spatiotemporal distribution matrix. The time interval in which the number of transport vehicles expected to pass through reaches its peak is taken as the release time node of the spatial location point, and the actual roadway coordinates of each spatial location point are taken as the release spatial location corresponding to the sub-release dose.

[0026] The release time point and release spatial location of each spatial location are associated with the corresponding sub-release dose and stored to form a multi-level release scheduling table.

[0027] Optionally, the step of obtaining the expected spatiotemporal distribution information of the transport vehicle traveling along the alleyway, and based on the expected spatiotemporal distribution information and the theoretical total ozone demand, dividing the theoretical total ozone demand into multiple sub-release doses, and assigning a corresponding release time node and release spatial location to each sub-release dose, further includes a step of conflict resolution for multiple release spatial locations under the same release time node, specifically including:

[0028] Extract all sub-release doses with the same release time node and their corresponding release spatial locations from the multi-level release scheduling table to form a set of releases at the same time.

[0029] Calculate the straight-line distance along the roadway between any two release spatial locations in the same-time release set, and determine the sub-release doses corresponding to the two release spatial locations whose straight-line distance along the roadway is less than a preset spatial conflict threshold as having spatial conflict;

[0030] For sub-release doses that are determined to have spatial conflicts, the sub-release doses corresponding to the release spatial positions with smaller straight-line distances along the roadway are merged into the sub-release doses corresponding to the release spatial positions with larger straight-line distances along the roadway. The merged sub-release doses replace the original two sub-release doses, and the release task at the release spatial position with smaller straight-line distances along the roadway is cancelled.

[0031] Repeat the merging operation until the straight-line distance along the roadway between any two release spatial locations in the simultaneous release set is not less than the preset spatial conflict threshold. Then update the simultaneous release set after conflict resolution to the multi-level release scheduling table.

[0032] Optionally, the step of sequentially controlling the ozone release devices at corresponding locations to release ozone according to the release time nodes and release spatial locations corresponding to each sub-release dose, so that the released ozone comes into contact with and oxidizes the CO in the exhaust gas of moving vehicles along the axial direction of the roadway, specifically includes:

[0033] Extract the sub-release doses and their release spatial locations corresponding to all release time nodes that are about to arrive at the current moment from the multi-level release schedule table;

[0034] For each sub-release dose to be executed at the current moment, the dose value of the sub-release dose is converted into a control signal for the valve opening duration of the corresponding ozone release device;

[0035] The valve opening duration control signal is sent to the solenoid valve controller of the ozone release device at the corresponding release space location, so that the solenoid valve remains open during the valve opening duration to release the corresponding dose of ozone.

[0036] During the opening of the solenoid valve, the airflow-assisted injection unit at the ozone release device is activated simultaneously to ensure that the released ozone is evenly distributed along the transverse cross section of the roadway.

[0037] After executing all the sub-release doses to be executed at the current moment, the corresponding release time node in the multi-level release scheduling table is marked as executed, and an execution confirmation signal is returned to the management terminal.

[0038] Optionally, it also includes online monitoring of CO concentration after multi-stage release and dynamic dose adjustment between stages, specifically including:

[0039] CO concentration monitoring points are set between the release space locations of every two adjacent ozone release devices. Within a preset monitoring time window after the ozone release device completes the release of the sub-release dose, the CO concentration values ​​of each CO concentration monitoring point are continuously collected to obtain the actual CO concentration decay curve of each monitoring point.

[0040] The peak decrease amplitude and decrease rate are extracted from the actual CO concentration decay curves at each monitoring point. The peak decrease amplitude and decrease rate are compared with the theoretical expected decrease amplitude and theoretical decrease rate corresponding to the pre-stored sub-release dose to calculate the actual oxidation efficiency of the ozone release device.

[0041] If the actual oxidation efficiency is lower than the preset efficiency threshold, the compensation coefficient of the adjacent ozone releasing device downstream of the ozone releasing device that has not yet performed release is calculated based on the ratio of the actual oxidation efficiency to the preset efficiency threshold. The compensation coefficient is multiplied by the original sub-release dose of the adjacent ozone releasing device to obtain the compensated sub-release dose of the adjacent ozone releasing device. The compensated sub-release dose is used to replace the original sub-release dose of the adjacent ozone releasing device in the multi-level release scheduling table.

[0042] Optionally, it also includes a multi-level release strategy dynamic adjustment step based on changes in vehicle driving status, specifically including:

[0043] During the ozone release process according to the multi-level release schedule, the real-time driving speed of each transport vehicle is continuously monitored. If the absolute value of the deviation between the real-time driving speed of a transport vehicle and the current real-time driving speed used in the prediction exceeds the preset speed deviation threshold, the dynamic adjustment process of the release strategy is triggered.

[0044] The real-time position and direction of the speed deviation of the transport vehicle that has a speed deviation are obtained from the current working condition parameter set. If the direction of the speed deviation is the deceleration direction, it is determined that the residence time of the exhaust emission source of the transport vehicle in the roadway is extended, and the residence increment time of the exhaust emission source at the release space location caused by the speed deviation is calculated.

[0045] Based on the residence increment time, the value of the corresponding sub-release dose at the release spatial location is increased, and the release time node of at least one release spatial location downstream of the release spatial location is adjusted forward by a preset time step in the multi-level release scheduling table.

[0046] The adjusted sub-release dose and release time node are updated in the multi-level release scheduling table, and the corresponding ozone release device is controlled to release ozone according to the updated multi-level release scheduling table.

[0047] The ozone oxidation system for CO in the exhaust gas of underground mining vehicles includes: a working condition data acquisition module, an exhaust gas emission prediction and demand determination module, a time and space allocation and scheduling module, and a multi-stage release execution control module.

[0048] The working condition data acquisition module is configured to acquire real-time operating status data of underground transport vehicles and real-time airflow data of roadways, obtain the current working condition parameter set, and send the current working condition parameter set to the exhaust gas emission prediction and demand determination module and the spatiotemporal allocation and scheduling module.

[0049] The exhaust emission prediction and demand determination module is configured to receive the current operating condition parameter set, predict the exhaust CO emission intensity under the current transportation condition based on the current operating condition parameter set, determine the theoretical total ozone demand required to oxidize the exhaust CO according to the exhaust CO emission intensity, and send the theoretical total ozone demand to the spatiotemporal allocation and scheduling module.

[0050] The spatiotemporal allocation and scheduling module is configured to receive the theoretical total ozone demand and the real-time location data and real-time driving speed data from the current operating condition parameter set, obtain the expected spatiotemporal distribution information of the transport vehicle traveling along the roadway, divide the theoretical total ozone demand into multiple sub-release doses according to the expected spatiotemporal distribution information and the theoretical total ozone demand, assign a corresponding release time node and release spatial location to each sub-release dose, generate a multi-level release scheduling table, and send the multi-level release scheduling table to the multi-level release execution control module;

[0051] The multi-level release execution control module is configured to receive the multi-level release schedule table and, according to the release time node and release spatial position corresponding to each sub-release dose in the multi-level release schedule table, sequentially control the ozone release device at the corresponding position to perform ozone release.

[0052] Optionally, the spatiotemporal allocation and scheduling module includes a spatiotemporal trajectory prediction unit, a spatiotemporal distribution matrix generation unit, a dose allocation unit, and a release schedule generation unit;

[0053] The spatiotemporal trajectory prediction unit is configured to predict the estimated arrival time of each transport vehicle to each spatial location point in the lane within a future preset time window, based on the current real-time location and current real-time driving speed of each transport vehicle and the lane path topology, thereby obtaining the estimated spatiotemporal trajectory of each transport vehicle.

[0054] The spatiotemporal distribution matrix generation unit is connected to the spatiotemporal trajectory prediction unit and is configured to receive the expected spatiotemporal trajectories of each transport vehicle, superimpose all the expected spatiotemporal trajectories on the time axis and the spatial axis, calculate the number of transport vehicles expected to pass through each spatial location point in the alley within each time interval in the future preset time window, and generate a spatiotemporal distribution matrix.

[0055] The dose allocation unit is connected to the spatiotemporal distribution matrix generation unit and is configured to receive the spatiotemporal distribution matrix and the theoretical total ozone demand. Using the number of transport vehicles expected to pass through each spatial location point in the spatiotemporal distribution matrix as the allocation weight, the theoretical total ozone demand is allocated according to the weight ratio of each spatial location point. The initial sub-release doses after allocation are normalized so that the sum of the normalized sub-release doses is equal to the theoretical total ozone demand, and the sub-release doses corresponding to each spatial location point are output.

[0056] The release schedule generation unit is connected to the dose allocation unit and is configured to receive sub-release doses corresponding to each spatial location point. For each spatial location point, the number of transport vehicles expected to pass through the spatial location point in each time interval is extracted from the spatiotemporal distribution matrix. The time interval in which the number of transport vehicles expected to pass through the spatial location point reaches its peak is taken as the release time node of the spatial location point. The actual roadway coordinates of each spatial location point are taken as the release spatial location corresponding to the sub-release dose. The release time node and release spatial location of each spatial location point are associated and stored with the corresponding sub-release dose to generate a multi-level release schedule table.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The proposed method for ozone oxidation of CO from underground mine vehicle exhaust gas utilizes a multi-stage, slow-release ozone oxidation system. This method simultaneously acquires real-time operating data of the transport vehicles and real-time airflow data from the tunnel to create a parameter set accurately reflecting the current operating conditions. Based on this parameter set, the CO emission intensity under the current transport conditions is predicted, and the required theoretical total ozone demand is determined. Then, combining the predicted spatiotemporal distribution information of each transport vehicle, the total demand is broken down into multiple sub-release doses. Each sub-release dose is assigned a specific release time node and spatial location. Finally, the ozone release device is sequentially controlled to release ozone along the tunnel axis at different locations and times, allowing ozone to gradually contact the exhaust gas and undergo oxidation reactions as the vehicles travel. This method fundamentally changes the passive mode of fixed-point centralized release, unfolding the ozone release process in time and space. It achieves active matching of the ozone release rhythm with the spatiotemporal evolution of mobile emission sources, significantly increasing the effective collision probability of ozone molecules with CO. While ensuring efficient CO conversion, it significantly improves ozone utilization efficiency, effectively reducing the CO concentration in the tunnel exhaust gas and providing a safer breathing environment for underground workers.

[0059] Based on this, multivariate data such as engine speed, load rate, exhaust gas temperature, real-time coordinates, driving speed, and cross-sectional average wind speed, wind direction, and ambient temperature are acquired from the vehicle diagnostic terminal and ultrasonic anemometers and temperature sensors deployed on the tunnel sidewalls. Median filtering is applied to the continuously collected operating parameters to eliminate abnormal jumps caused by sensor transient interference, ensuring the stability and authenticity of the operating parameter set. The total working intensity is obtained by multiplying and summing the engine speed and load rate of each transport vehicle, and CO emission intensity is calculated using a multivariate linear regression model fitted based on underground measured data. Combined with the chemical reaction stoichiometric coefficient and the influence coefficient of tunnel ambient temperature on the reaction rate, a theoretical total ozone demand that is closer to actual needs is obtained, thus providing a scientific basis for subsequent dose splitting. During the ozone allocation phase, a spatiotemporal distribution matrix is ​​generated by predicting the estimated arrival times of each transport vehicle at various spatial locations within a preset time window and superimposing these predictions. Using the estimated number of transport vehicles passing through each location as the allocation weight, the theoretical total ozone demand is proportionally allocated and normalized. This ensures that the ozone dosage automatically tilts towards sections with dense vehicle traffic, and the release timing aligns with the peak arrival time of vehicles at that location, achieving optimal allocation of ozone resources in both spatiotemporal dimensions. Simultaneously, conflict resolution and merging of release locations that are too close together at the same release time node avoids ozone overlap and waste, as well as the risk of localized overconcentration caused by repeated releases at close range. In the actual release control process, each sub-release dose is converted into the valve opening duration of the corresponding ozone release device, and the airflow-assisted injection unit is simultaneously activated to uniformly distribute ozone along the transverse cross-section of the roadway, enhancing the spatial mixing of ozone and exhaust gas. Furthermore, online CO monitoring points are deployed between adjacent ozone release locations. Oxidation efficiency is assessed by comparing actual concentration decay curves with theoretically expected values. If the efficiency falls short of expectations, automatic dose compensation is applied to adjacent downstream points that have not yet released ozone, forming a closed-loop feedback regulation. When it detects that a transport vehicle's dwell time at a release location is prolonged due to deceleration or other reasons, the system can dynamically increase the release dose at that location and advance the downstream release time, allowing the release strategy to quickly adapt to real-time changes in driving conditions. The corresponding ozone oxidation system integrates operational data acquisition, exhaust emission prediction and demand determination, spatiotemporal allocation and scheduling, and multi-level release execution control into a cohesive whole. In particular, the spatiotemporal allocation and scheduling module includes a spatiotemporal trajectory prediction unit, a spatiotemporal distribution matrix generation unit, a dose allocation unit, and a release scheduling table generation unit, enabling efficient and reliable engineering implementation of the aforementioned multi-level slow-release decision-making process and possessing good adaptability for downhole deployment. Attached Figure Description

[0060] Figure 1 This is a schematic flowchart illustrating the steps of an embodiment of the ozone oxidation method for CO in the multi-stage slow-release exhaust gas of a vehicle in a mine, according to the present invention.

[0061] Figure 2This is a schematic diagram of a module of an embodiment of the ozone oxidation system for CO in the exhaust gas of underground vehicles in mines according to the present invention. Detailed Implementation

[0062] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0063] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0064] Example 1

[0065] like Figure 1 As shown in this embodiment, a multi-stage slow-release ozone oxidation method for CO in the exhaust gas of underground mining vehicles is described in detail. This method can be deployed in a system consisting of an underground sensor network, an on-board terminal, an edge computing server, and ozone release actuators distributed along the roadway.

[0066] At the outset of implementing this method, a basic data sensing environment needs to be established. For each transport vehicle operating underground, an on-board diagnostic terminal is installed at the electronic control unit interface in its engine compartment. This terminal supports reading controller area network (CAN) bus data conforming to standards such as SAE J1939 or ISO 15765, parsing and reading engine speed signals in real time at a frequency of at least 1Hz, for example, 2Hz; reading engine load rate signals, i.e., the percentage of current torque to the maximum available torque at the same speed; and reading exhaust gas temperature signals, obtained from temperature sensors installed in the exhaust manifold or before the catalytic converter, in degrees Celsius. Simultaneously, an on-board positioning module is installed in a stable location on the top of the transport vehicle or inside the driver's cab. Considering the lack of satellite navigation signals underground, this positioning module employs a positioning scheme based on ultra-wideband (UWB) technology. The module contains an UWB tag that emits nanosecond-level pulse signals at a specific frequency, which are received by multiple positioning base stations pre-deployed in a mesh pattern on the tunnel roof. Edge computing servers or positioning engines calculate the time difference of signals arriving at different base stations. Combining this with the known 3D coordinates of the base stations, they use the Chan algorithm or Taylor series expansion algorithm to calculate the tag, thus obtaining the real-time location coordinates of the transport vehicle. These real-time location coordinates are represented in a unified 3D Cartesian coordinate system used in the mine, including axial mileage along the tunnel, horizontal offset, and elevation. By dividing the position difference between two consecutive location calculations by the time interval, the real-time speed of the transport vehicle under tunnel constraints can be obtained, in m / s. Five types of data—engine speed signal, engine load rate signal, exhaust emission temperature signal, real-time location coordinates, and real-time speed—are packaged into a single real-time operating status data message, with an attached vehicle identifier and timestamp, and transmitted via the vehicle's onboard Wi-Fi 6 or 5G private network wireless communication module.

[0067] Concurrently, wind flow sensing devices were deployed at several key cross-sections of the roadway. The selection criteria for key cross-sections included locations where the roadway cross-section changed, before and after airlocks, major intersections, and areas near where ozone release devices were planned. At each selected monitoring cross-section, an ultrasonic anemometer was installed in the middle of the roadway sidewall, avoiding equipment obstruction and eddy current zones. This ultrasonic anemometer integrates at least two pairs of orthogonal ultrasonic transducers. By measuring the time difference of ultrasonic wave propagation along the downwind and upwind paths, the wind speed vector component along that path is calculated, and then vector synthesis is used to obtain the cross-sectional average wind speed and cross-sectional average wind direction data. The unit for the cross-sectional average wind speed data is m / s, and the cross-sectional average wind direction data is expressed as the angle or azimuth with the roadway axis. A temperature sensor, using a Class A precision platinum resistance Pt100 element and equipped with an explosion-proof protective cover, is also integrated on the same mounting bracket to collect roadway ambient temperature data, expressed in °C. These sensors are connected to a nearby data acquisition substation via a shielded RS-485 bus. The data acquisition substation timestamps the data and encapsulates it into real-time airflow data packets for the tunnel, which are then uploaded via an industrial Ethernet ring network.

[0068] On the edge computing server, which serves as the data processing and control hub, a real-time data aggregation and preprocessing service runs. This service maintains a time-series buffer with a set maximum length, for example, caching data from the last 60 seconds. When a message is received from a different data source, the service first retrieves or creates a tuple of operating parameters associated with that acquisition time in the time-series buffer based on the timestamp carried in the message. A tuple of operating parameters is a structured data object whose fields include, but are not limited to: acquisition time timestamp, vehicle identifier list, corresponding engine speed list, engine load rate list, exhaust emission temperature list, real-time location coordinate list, real-time driving speed list, monitoring section identifier list, section average wind speed list, section average wind direction list, and tunnel ambient temperature list. Through this association operation, the vehicle operating status and the environmental wind flow status at the same moment are coupled. Considering the complex electromagnetic environment downhole, sensors may generate abnormal jump values ​​due to the start-up and shutdown of large equipment, inverter interference, or momentary packet loss in communication. For example, the engine speed may suddenly jump from 1500 r / min to 9000 r / min or 0 r / min. To avoid such abnormal data contaminating subsequent predictions and decisions, median filtering is required on the tuples of operating condition parameters from multiple consecutive sampling times in the time-series buffer. Specifically, a sliding window is set with an odd number of widths, such as 5 consecutive sampling points. The five consecutive numerical sequences from the same sensor within the window are sorted, and the median is taken as the filtered value at the center of the window. For the engine speed signal, if the five original values ​​within the window are 1480, 1520, 1510, 9000, and 1490, and the sorted values ​​are 1480, 1490, 1510, 1520, and 9000, with a median of 1510, then 1510 is used to replace the potentially abnormal value of 9000. For multidimensional data such as real-time location coordinates, median filtering is performed independently on each coordinate component. After median filtering, the tuples of operating condition parameters are freed from the influence of short-term strong interference, thus more accurately reflecting the continuous changing trend of the operating conditions. Arranging these filtered operating condition parameter tuples in chronological order constitutes the current operating condition parameter set.

[0069] After obtaining the current operating condition parameter set, the next step is to predict the CO emission intensity of the exhaust gas under the current transportation conditions and determine the theoretical total ozone demand accordingly. First, engine speed and load rate data for each transport vehicle are extracted from the current operating condition parameter set. The engine speed of each transport vehicle is multiplied by the engine load rate to obtain a comprehensive index, defined as the engine effective working intensity index. For example, an engine with a speed of 2000 r / min and a load rate of 60% has an engine effective working intensity index of 2000 × 0.6 = 1200. This index comprehensively considers the engine's working frequency and the output ratio of a single work cycle, and is highly positively correlated with fuel consumption rate and pollutant generation rate. The effective working intensity indices of all transport vehicles currently in operation are summed to obtain a total working intensity value. For example, if three transport vehicles are operating simultaneously underground, and the calculated indices are 1200, 850, and 1500 respectively, then the total working intensity value is 1200 + 850 + 1500 = 3550.

[0070] Next, the total workload value is input into a pre-built CO emission prediction model. The CO emission prediction model is built offline, and the process is as follows: In the mine, a typical transport roadway is selected, and a mobile exhaust emission testing system is deployed, including a full-flow dilution sampling system or a micro-dilution partial-flow sampling system, and a carbon monoxide analyzer, such as a non-dispersive infrared absorption spectrometer. Simultaneously, transport vehicles of different models and load capacities are arranged to drive in this roadway under various working conditions, including idling, constant speed, acceleration, and deceleration, while simultaneously recording data from the on-board diagnostic terminal and roadway airflow data to calculate the corresponding total workload value. After accumulating hundreds of valid data samples through experiments, the total workload value is used as the main input variable, and the CO emission mass flow rate per unit time (in g / s) is used as the output variable. Optionally, exhaust emission temperature signals and roadway ambient temperature data are introduced as correction variables, and a multiple linear regression model is used for fitting. The model can be expressed as follows: CO emission intensity equals coefficient B0 plus coefficient B1 multiplied by the total workload value plus coefficient B2 multiplied by the average exhaust gas temperature plus coefficient B3 multiplied by the ambient temperature. Here, B0, B1, B2, and B3 are regression coefficients obtained by fitting experimental data using the least squares method. For example, a set of coefficients obtained by fitting measured data from a mine is: B0=0.12, B1=0.0035, B2=0.008, B3=-0.002. This model has been verified to meet statistical significance requirements, such as an R-squared value greater than 0.85, and has been deployed to an edge computing server as a CO emission prediction model. Using this model, the current CO emission intensity can be calculated based on the real-time total workload value. For example, substituting the total workload value of 3550 into the model yields a result of 5.2 g / s.

[0071] After obtaining the CO emission intensity from the exhaust gas, the ozone demand is calculated. The local database of the edge computing server pre-stores the stoichiometric coefficients for the chemical reaction between ozone and CO. These coefficients are determined based on the main reaction equation: CO + O3 → CO2 + O2. According to this equation, 1 mol of CO reacts with 1 mol of ozone. The molar mass of CO is approximately 28 g / mol, and the molar mass of ozone is approximately 48 g / mol. Therefore, from a mass perspective, oxidizing 1 g of CO theoretically requires approximately 48 ÷ 28 ≈ 1.714 g of ozone. This value of 1.714 is stored as a basic stoichiometric coefficient. However, considering the potential for side reactions in complex gas-phase reactions, or the loss of some ozone through decomposition, this coefficient can be fine-tuned based on actual downhole environment simulation results, for example, within the range of 1.8 to 2.2. Multiplying the calculated CO emission intensity from the exhaust gas by this stoichiometric coefficient yields the baseline ozone demand required for complete reaction under ideal conditions. If the CO emission intensity of the exhaust gas is 5.2 g / s, and the measurement coefficient is 1.8, then the baseline ozone requirement is 5.2 × 1.8 = 9.36 g / s.

[0072] However, the reaction rate constant of ozone and CO is highly dependent on ambient temperature. In underground mines, the ambient temperature in tunnels of different depths and with varying ventilation conditions can fluctuate between 15°C and 35°C, affecting the actual required excess ozone. Therefore, it is necessary to obtain the current ambient temperature data of the tunnels to correct for the baseline demand. A table of the influence coefficients of temperature on the reaction rate is pre-stored in a local database. This table can be created by conducting ozone-CO gas-phase reaction experiments at different temperatures in the laboratory, determining the pseudo-second-order reaction rate constant, and comparing it with the rate constant at a baseline temperature, such as 25°C. For example, experimental measurements show that at 15℃, the reaction rate is approximately 70% of that at 25℃. To achieve the same removal effect within the same time period, more ozone needs to be added. Therefore, the influence coefficient for 15℃ is approximately 1 ÷ 0.7 ≈ 1.43. At 20℃, the reaction rate is approximately 85% of that at 25℃, with an influence coefficient of approximately 1 ÷ 0.85 ≈ 1.18. At 25℃, the influence coefficient is 1.0. At 30℃, the reaction rate increases by 10%, with an influence coefficient of approximately 1 ÷ 1.1 ≈ 0.91. At 35℃, the reaction rate increases by 20%, with an influence coefficient of approximately 1 ÷ 1.2 ≈ 0.83. This table stores the corresponding influence coefficients using temperature as the lookup key. Based on the current ambient temperature data of the tunnel obtained from the current operating condition parameter set, for example, 18℃, the influence coefficient of temperature on the reaction rate is obtained by looking up the table using linear interpolation. For example, interpolating between 1.43 for 15℃ and 1.18 for 20℃, the influence coefficient for 18℃ is approximately 1.43 - (1.43 - 1.18) × (18 - 15) ÷ (20 - 15) = 1.28. Multiplying the baseline ozone demand of 9.36 g / s by this influence coefficient of 1.28, the final theoretical total ozone demand is obtained, which is 9.36 × 1.28 = 11.98 g / s. This theoretical total ozone demand is the total mass of ozone that is expected to be injected into the tunnel space per unit time under the current total load of multiple vehicles and the current temperature to effectively treat vehicle exhaust CO.

[0073] After dynamically determining the theoretical total ozone demand, the core of this invention's method is to finely decompose this total demand in both spatial and temporal dimensions based on the spatiotemporal distribution of transport vehicles. This requires first obtaining the expected spatiotemporal distribution information of the transport vehicles traveling along the tunnels. This process starts with the current real-time position and speed of each transport vehicle. The motion prediction of each transport vehicle must be constrained by the topology of the tunnel path it will travel through. This tunnel path topology is a pre-generated digital road network model that abstracts the entire underground mine transportation network into a directed graph. Nodes in the graph represent physical feature points, such as loading points, unloading points, intersections, ventilation doors, signal chambers, etc. Edges represent continuous tunnel segments connecting two feature points. The attributes of each edge include tunnel segment length, cross-sectional area, maximum allowable speed, gradient, and a parameter representing the turning radius or curvature. When constructing this model, precise topological relationships are established by measuring or importing data from the mine's three-dimensional design model.

[0074] For each transport vehicle, the spatiotemporal trajectory prediction employs a recursive calculation algorithm based on kinematics and road network constraints. First, the vehicle's current real-time position coordinates are matched against edges of the topological graph to determine its current edge and path offset along that edge. Then, using the current real-time speed as the initial speed, it is assumed that the vehicle will proceed according to its planned or historically chosen path probability within a preset future time window, such as 300 seconds. Path selection can be determined by statistical probabilities of historical travel paths at the current node; if no data is available, it defaults to traveling straight along the main transport lane. For speed evolution, a model with first-order inertial smoothing is used: at each prediction step, such as every 1 second, the vehicle's speed is adjusted based on the properties of the current edge. If there is a curve or downhill ahead, the speed is limited by a preset maximum lateral acceleration or deceleration value. The distance the vehicle travels along the edge within each prediction step is calculated, and combined with the edge length and the connectivity of subsequent edges, the predicted arrival time of the vehicle at or past each predefined discrete spatial location point on the lane is calculated. These predefined spatial location points are generated by discretizing along the centerline of each lane at fixed spatial intervals, for example, taking a point every 2 meters, and assigning each point globally unique spatial coordinates. These points cover all possible installation locations of ozone release devices. Finally, a projected spatiotemporal trajectory is generated for each transport vehicle. This is a chronologically ordered sequence, where each element is a triple containing the spatial location coordinates, the projected arrival time, and the projected departure time. The projected departure time is the estimated transit time calculated by dividing the vehicle length by the speed.

[0075] Once the projected spatiotemporal trajectories of all online transport vehicles are generated, this information needs to be superimposed on the time and spatial axes to generate a spatiotemporal distribution matrix. This matrix is ​​a two-dimensional data structure. Its row indices correspond to all spatial location points, and its column indices correspond to a series of consecutive time intervals obtained by discretizing the preset time window. For example, a preset time window of 300 seconds is divided into 30 consecutive time intervals, each with a length of 10 seconds. The generation process is as follows: Initialize a matrix with all elements equal to 0. Then, iterate through the projected spatiotemporal trajectory of each transport vehicle. For each spatiotemporal occupancy information in the trajectory, i.e., the time period from arriving at a point to leaving that point, determine which time intervals this time period falls into, and increment the corresponding element value in the matrix for both the spatial location point and the time interval by 1. In this way, each element value in the matrix represents the number of transport vehicles expected to pass through a certain spatial location point within a certain time interval. For example, the element value of the i-th row and j-th column of the matrix is ​​3, indicating that 3 transport vehicles are expected to pass through spatial location point i within time interval j. This spatiotemporal distribution matrix intuitively reveals the density hotspots and peak periods of mobile emission sources in the roadway over a future period.

[0076] Next, based on this spatiotemporal distribution matrix, the theoretical total ozone demand is divided into multiple sub-release doses, and each sub-release dose is assigned a corresponding release time node and release spatial location. The core idea of ​​the allocation strategy is to release more in areas with high traffic volume and release earlier when traffic arrives. First, an allocation weight is calculated for each spatial location. A preferred calculation method is to sum all the element values ​​in the matrix row corresponding to the spatial location point, i.e., the expected number of vehicles passing through it in each time period within the entire preset time window. This sum represents the expected traffic heat of the spatial location point in the future. The total heat is obtained by adding the traffic heat of all spatial location points. Then, the proportion of the initial sub-release dose allocated to each spatial location point is equal to its own traffic heat divided by the total heat. Multiplying the theoretical total ozone demand by this proportion yields the initial sub-release dose corresponding to each spatial location point. For example, if the traffic heat at a certain point is 15 times, the total heat at all points is 1500 times, and the theoretical total ozone demand is 11.98 g / s, then the sub-release dose rate allocated to that point is 11.98 × 15 ÷ 1500 = 0.1198 g / s.

[0077] However, the sum of the sub-emission doses at all spatial locations obtained in this way may not be exactly equal to the theoretical total ozone demand due to rounding errors in the calculation process. Therefore, a normalization process must be performed. The sum of the initial sub-emission doses at all spatial locations is calculated, and then the theoretical total ozone demand is divided by this sum to obtain a normalization scaling factor. The initial sub-emission doses at all spatial locations are multiplied by this factor to ensure that the sum of the normalized sub-emission doses is exactly equal to the theoretical total ozone demand, thus completing the dose allocation.

[0078] After determining the dosage, the release time and spatial location must be specified for each sub-release dose. The release spatial location is naturally the actual roadway coordinates of that location. The release time is determined by analyzing the time-varying traffic density curve of that location. A row corresponding to that spatial location is extracted from the spatiotemporal distribution matrix, resulting in a time series representing the expected number of vehicles passing that point over a time interval. The time interval corresponding to the largest peak value in this series is identified. The starting time of this peak time interval, or, to ensure ozone meets the traffic flow beforehand, a lead time dependent on wind speed and distance (e.g., 5 seconds), is shifted forward from this starting time as the final release time. If multiple equal peak intervals exist, the first interval that appears is preferred to ensure the timeliness of the initial application.

[0079] At this point, a preliminary multi-level release schedule table is generated, with each record containing the release location, release time node, and sub-release dose. However, this preliminary table may have spatial conflict issues: that is, at the same release time node, two or more ozone release devices that are very close to each other will perform releases. This will cause excessive ozone accumulation in local spaces, while gaps appear in other areas, which is both wasteful and may pose safety risks. Therefore, conflict resolution is required. The specific steps are: extract all records with the same release time node from the preliminary multi-level release schedule table to form a set of simultaneous releases. Within this set, calculate the straight-line distance along the roadway between any two different release locations. The calculation method is to calculate the shortest path length between the two points on the road network by querying the roadway path topology. This distance is compared with a preset spatial conflict threshold. The setting of this preset spatial conflict threshold needs to take into account the effective diffusion radius of ozone after it is ejected from the release port and the mixing characteristics of the roadway cross-section. For example, computational fluid dynamics simulations revealed that ozone released by a certain type of injection device could basically uniformly cover the cross-section 15m downstream of the release point in a tunnel with a wind speed of 1.5m / s, but maintained a high concentration beam within 5m. To avoid beam overlap, a preset spatial conflict threshold can be set to 10m. If the straight-line distance along the tunnel between two release locations is less than 10m, they are considered to be in spatial conflict. For conflict points, a strategy of merging downstream of the airflow is adopted: comparing the wind directions faced by the two conflict points at the same time, determining which point is downstream of the airflow. The sub-release dose of the upstream point is merged into the sub-release dose of the downstream point, that is, the sum of the two doses replaces the original two doses, and the release task of the upstream point is canceled in the scheduling table. If the wind direction is uncertain or extremely weak at this moment, the merging is based on the coordinate magnitude along the tunnel direction, merging in the direction of increasing mileage. For example, if the straight-line distance along the roadway between spatial locations A and B is 6m, which is less than the threshold of 10m, and A is upstream of B, with A's sub-release dose being 0.12g / s and B's sub-release dose being 0.09g / s, then A's dose is merged into B's, B's dose is updated to 0.12 + 0.09 = 0.21g / s, and the release task at point A is cancelled. This conflict finding and merging operation is repeated until the straight-line distance along the roadway between any two retained release spatial locations in the release set at the same time is not less than the preset spatial conflict threshold of 10m. After conflict resolution, an optimized and efficient final multi-level release scheduling table is obtained.

[0080] During the execution phase, the edge computing server's control and scheduling module strictly follows a multi-level release schedule to drive the physical devices. This module has a built-in high-precision timer that cyclically scans all records in the schedule that are in a pending execution state. When the current system time is determined to be equal to or slightly ahead of the release time node of a certain record, a release task is triggered. First, the value of the sub-release dose corresponding to the task is converted into a valve opening duration control signal that the ozone release device can understand. Each model of ozone release device stores a calibration parameter in its controller firmware, which is the ozone mass flow rate (in g / s) when the solenoid valve is fully open under standard operating conditions. This parameter is obtained by actual measurement using a mass flow meter before leaving the factory or after installation. The conversion formula is: valve opening duration equals sub-release dose divided by the ozone mass flow rate. For example, if the sub-release dose is 5g and the device mass flow rate is 2.5g / s, the calculated valve opening duration is 2s. A control message containing the opening duration and trigger command is generated and sent to the solenoid valve controller at a specific IP address via industrial Ethernet, such as EtherNet / IP protocol. Upon receiving the command, the solenoid valve controller actuates the solenoid valve coil, keeping its valve core fully open for a specified 2-second duration. High-pressure or atmospheric-pressure ozone gas flows from the buffer tank and is delivered through pipeline to the nozzle installed at the release space location. At the same instant the solenoid valve opens, a linked control signal is also sent to the airflow-assisted injection unit integrated into the ozone release device. This unit can be a small explosion-proof axial flow fan or an ejector driven by compressed air. It generates a high-speed transverse airflow that acts on the ozone jet flowing from the nozzle, dispersing it and rapidly and uniformly distributing it along the transverse cross-section of the tunnel, forming an ozone curtain rather than a concentrated jet.

[0081] Once the solenoid valve has completed its opening and closing for the specified duration, the release is considered complete. The multi-level release execution control module marks the status of this record in the multi-level release scheduling table as executed and generates an execution confirmation signal containing information such as execution time, actual opening duration, device number, and execution result. This signal is then pushed to the management terminal, such as the host computer configuration software in the dispatch room, for operators to monitor.

[0082] To ensure stable and reliable oxidation, this embodiment constructs a closed-loop control system, which also includes online monitoring of CO concentration after multi-stage release and dynamic dose adjustment between stages. On the sidewall of the tunnel, approximately midway between every two adjacent ozone release devices, a CO concentration monitoring point is set. High-precision explosion-proof CO sensors, such as those based on infrared gas correlation filtering technology, are installed at these points, with a detection limit of 1 ppm and a response time T90 of less than 15 seconds. These sensors report CO concentration data at 1-second intervals. After an upstream ozone release device completes a single release dose, the system initiates a preset monitoring time window. The length of this window is related to the distance of the tunnel section and the current wind speed. For example, if the distance between two points is 50 meters and the current average wind speed at the cross-section is 2 m / s, the window length can be set to a 50-second period from the 10th to the 60th second after the release begins, covering the time it takes for ozone to react with the exhaust gas and flow through the monitoring point. Within this window, CO concentration values ​​at monitoring points are continuously collected, generating an actual CO concentration decay curve that changes over time.

[0083] In the system database, for each monitoring point and corresponding sub-release dose, theoretical expected decrease magnitude and theoretical expected decrease rate are pre-stored. These theoretical values ​​are obtained through prior offline simulation: in a flow tubular reactor or computational fluid dynamics model simulating a tunnel environment, the same wind speed, temperature, and ozone injection rate as in reality are set, and a CO pollution source of the same intensity is simulated for release. The resulting CO concentration peak and decrease curve are obtained, from which the peak decrease magnitude (the difference between the peak concentration and the end concentration of the window, in ppm) and the decrease rate (the half-life, in seconds) are extracted. The peak decrease magnitude and decrease rate extracted from the actual CO concentration decay curve are compared with the theoretical values. An actual oxidation efficiency index is defined as the weighted average of the ratio of the actual peak decrease magnitude to the theoretical expected decrease magnitude and the ratio of the actual decrease rate to the theoretical expected decrease rate, with each weight determined to be 50% based on sensitivity analysis of the field data. For example, if the theoretical expected decrease is 200 ppm and the theoretical expected decrease rate is 40 ppm / s, and the actual peak decrease is 130 ppm and the actual decrease rate is 26 ppm / s, then the actual oxidation efficiency is calculated as (130 ÷ 200) × 0.5 + (26 ÷ 40) × 0.5 = 0.65 × 0.5 + 0.65 × 0.5 = 0.65, which is 65%.

[0084] In the strategy configuration, an efficiency threshold, such as 80%, is preset. If the actual oxidation efficiency (65%) is lower than 80%, it indicates that oxidation in that segment is incomplete, possibly due to actual vehicle density far exceeding prediction or low local wind speed. In this case, a compensation mechanism needs to be activated. A compensation coefficient is calculated for the downstream adjacent ozone releasing device that is not yet releasing ozone in the multi-level release scheduling table. The compensation coefficient is calculated by dividing the preset efficiency threshold by the actual oxidation efficiency to obtain a coefficient greater than 1, and then multiplying it by a safety margin, such as 0.9. Continuing the previous example, the compensation coefficient is equal to 80 ÷ 65 × 0.9 ≈ 1.1077. Then, this compensation coefficient is multiplied by the original sub-release dose of the downstream adjacent ozone releasing device in the multi-level release scheduling table to obtain a new, increased compensated sub-release dose. For example, if the original sub-release dose of the downstream adjacent device is 0.2 g / s, then the compensated sub-release dose is 0.2 × 1.1077 = 0.2215 g / s. The system replaces the original dose in the scheduling table with this compensated sub-release dose, thereby compensating for insufficient upstream oxidation efficiency by increasing the dosage in subsequent steps. If the efficiency is above a threshold, no compensation is needed, or the dosage can be slightly reduced according to symmetric logic to conserve ozone.

[0085] In addition to the closed-loop regulation based on CO concentration feedback described above, the method in this embodiment also possesses a feedforward dynamic adjustment capability based on changes in vehicle driving status. During the execution of the multi-level release schedule, the onboard positioning module continuously samples and reports the real-time driving speed of the transport vehicles. The system backend continuously monitors the real-time speed changes of each transport vehicle. The current real-time driving speed of each transport vehicle is compared with the reference speed used when its predicted spatiotemporal trajectory was generated, and the absolute value of the deviation is calculated. If the absolute value of the deviation of a transport vehicle exceeds a preset speed deviation threshold, for example, if the reference speed is 5 m / s and the deviation threshold is ±1.5 m / s, meaning the speed is below 3.5 m / s or above 6.5 m / s, the dynamic adjustment process of the release strategy is triggered. This threshold is set comprehensively based on the speed fluctuation range during normal vehicle operation and the uncertainty of the prediction model.

[0086] Once triggered, the system first obtains the real-time position of the transport vehicle from the current operating condition parameter set and determines the direction of its speed deviation. If it is in the direction of deceleration, such as slowing down to 1 m / s due to obstacles ahead or queuing at loading points, this indicates that the emission source will remain in its current lane area for a longer period of time. The system calculates the resulting incremental dwell time. The calculation method is as follows: based on the current lane segment and its current distance from the end of that segment, as well as its current speed, it first estimates the time T1 required to traverse the remaining section at the baseline speed, and then estimates the required time T2 at the current low speed. The difference between the two, T2-T1, is the incremental dwell time near the current release space location. For example, if the remaining length of the current segment is 30 m and the baseline speed is 5 m / s, T1 = 30 ÷ 5 = 6 s; if the current speed is 1 m / s, T2 = 30 ÷ 1 = 30 s; then the incremental dwell time is 30 - 6 = 24 s. However, this is not an exact solution. A better approach is to combine path topology to re-predict the short-term trajectory at the current low speed and calculate the additional dwell time at each spatial location. Then, multiply this dwell time increment by the vehicle's CO emission intensity per unit time (value previously saved or recalculated during the prediction phase), and then multiply by the stoichiometric coefficient and temperature coefficient to obtain the additional sub-release dose required in the region. For example, if the vehicle's CO emission intensity per unit time is 2.1 g / s, the stoichiometric coefficient is 1.8, and the temperature coefficient is 1.28, then the additional sub-release dose would be 24 × 2.1 × 1.8 × 1.28 ≈ 116.1 g. This additional dose is then added to the sub-release doses of one or more ozone release devices corresponding to this region. Simultaneously, because the overall movement of the pollution plume has slowed down, the release time nodes originally scheduled for downstream release locations in the original schedule will appear premature. Therefore, the release time of at least one adjacent downstream release location needs to be adjusted backward, i.e., delayed by a preset time step. This step can be a certain proportion of the residence increment time, or a fixed 10s or 20s, to match the delayed arrival of the pollution plume. Conversely, if the velocity deviation direction is the acceleration direction, it indicates that the time the emission source spends in a certain area is shortened, and the exhaust gas residence time is reduced. In this case, the sub-release dose in that area can be reduced accordingly, and the downstream release time needs to be adjusted forward, i.e., released earlier, to avoid ozone release being later than the pollution plume that has accelerated through. The adjusted sub-release dose and release time are updated in real time to the multi-level release scheduling table, overriding the original instructions. Subsequent control cycles will immediately execute according to the new scheduling table. Through this predictive-execution-monitoring-feedback-feedforward composite control system, this method achieves deep adaptation and precise control of the complex and ever-changing traffic flow and smoke emission conditions in underground mines.

[0087] Example 2

[0088] like Figure 2As shown, this embodiment specifically illustrates an ozone oxidation system for CO in the exhaust gas of a vehicle in an underground mine. This system is a specific engineering physical architecture for implementing the method in Embodiment 1, and its module division closely corresponds to the method flow in Embodiment 1.

[0089] The system includes a working condition data acquisition module, an exhaust emission prediction and demand determination module, a spatiotemporal allocation and scheduling module, and a multi-level release execution control module. The working condition data acquisition module is configured to acquire real-time operating status data of underground transport vehicles and real-time airflow data from the roadways, obtain the current working condition parameter set, and send this parameter set to the exhaust emission prediction and demand determination module and the spatiotemporal allocation and scheduling module. To achieve this function, this module consists of an on-board data acquisition unit, an airflow data acquisition unit, and a data aggregation and filtering processing unit at the hardware level. The on-board data acquisition unit is a combination of an on-board diagnostic terminal and an on-board positioning module installed on each transport vehicle. Together, they complete the acquisition and uploading of engine speed signals, engine load rate signals, exhaust emission temperature signals, real-time position coordinates, and real-time driving speed. The airflow data acquisition unit consists of ultrasonic anemometers and temperature sensors distributed across the roadway cross-section, along with their associated data acquisition substations. The data aggregation and filtering unit is a software service running on an edge computing server. It establishes connections with various downhole data sources and receives heterogeneous data through an industrial protocol driver library, such as an OPC UA client. Internally, this unit maintains a message queue and time-series buffer based on a publish-subscribe model. Upon receiving data packets from different sources and timestamps, it aligns and assembles them according to their timestamps to form tuples of operating parameters. The unit incorporates a configurable median filter with a window width of 3, 5, or 7. This filter performs sliding window median filtering on consecutive tuple sequences entering the time-series buffer, removing aberrations and packaging the cleaned sequence of operating parameter tuples into the current operating parameter set. This set is then published via an internal interface in standard JSON or Protocol Buffers format.

[0090] The exhaust emission prediction and demand determination module is configured to receive the current operating condition parameter set, predict the exhaust CO emission intensity based on it, and determine the theoretical total ozone demand. The results are then sent to the spatiotemporal allocation and scheduling module. Internally, this module is divided into three sequentially called sub-modules. The first is the total workload calculation sub-module, which receives the current operating condition parameter set, iterates through the data of all transport vehicles, performs multiplication and summation operations (engine speed multiplied by engine load rate and summed), and outputs the total workload value. The second is the CO emission prediction model sub-module, which encapsulates a pre-trained multiple linear regression model. This model stores coefficients B0, B1, B2, and B3 in a structured file, such as PMML or ONNX format. This sub-module loads the model file, constructs the prediction function, receives the total workload value, and optionally extracts exhaust temperature and ambient temperature data from the operating condition parameter set as input feature vectors. It then outputs the exhaust CO emission intensity through matrix operations. The third is the ozone demand calculation submodule, which embeds a database query interface. It reads chemical reaction stoichiometry coefficients (e.g., 1.8) and a table of temperature-related reaction rate influence coefficients from the system configuration parameter table. This table stores over 200 records corresponding to temperatures and influence coefficients, ranging from 15℃ to 35℃ in 0.5℃ intervals. Following the calculation order described in Example 1, this submodule first calculates the baseline ozone demand, then corrects it by querying the influence coefficients based on the ambient temperature, and finally calculates and outputs the theoretical total ozone demand.

[0091] The spatiotemporal allocation and scheduling module is the hub of the entire system's intelligent decision-making. It is configured to receive real-time location and real-time driving speed data from the theoretical total ozone demand and the current operating condition parameter set, generate a multi-level release scheduling table, and send it to the multi-level release execution control module. The internal structure of this module is key to realizing the invention concept, specifically including a spatiotemporal trajectory prediction unit, a spatiotemporal distribution matrix generation unit, a dose allocation unit, and a release scheduling table generation unit.

[0092] The core of the spatiotemporal trajectory prediction unit is a motion prediction engine based on path topology constraints. Upon startup, the unit loads the mine's underground tunnel path topology from the geographic information system server. This structure is represented by an attribute graph data structure of nodes and edges and resides in memory. It subscribes to the real-time location and speed data streams of each transport vehicle. For each vehicle, it maintains an independent predictor instance. The predictor's algorithm logic is as follows: First, map matching is performed, using a Hidden Markov Model or nearest neighbor algorithm to attach noisy location points to the edges of the path topology. Then, a recursive simulation process is initiated, with a simulation time step configurable to 0.5s or 1s. At each step, based on the vehicle's current speed, the speed limit in the edge attributes, and a preset acceleration or deceleration model, the position for the next second is calculated, and the spatial location points and time traversed are recorded to form the predicted spatiotemporal trajectory. Finally, a sequence of trajectory points is output.

[0093] The spatiotemporal distribution matrix generation unit is connected to the spatiotemporal trajectory prediction unit, receiving all vehicles' predicted spatiotemporal trajectories within a preset time window, such as a set configurable 600s. This unit first performs gridded discretization of both space and time. The spatial grid is the set of all predefined spatial location points along the road network, with configurable intervals, such as 2m or 3m. The temporal grid is a sequence of time intervals of a fixed length, such as 15s, divided into preset time windows. Then, this unit creates a high-dimensional sparse matrix, with row keys representing spatial location point IDs and column keys representing time interval indices. It scans each trajectory in parallel, using time interval interpolation to plot the vehicle's spatiotemporal occupancy information onto this matrix, i.e., performing an atomic increment operation on each spatiotemporal grid cell that falls within the interval. This ultimately generates a complete spatiotemporal distribution matrix. This matrix can be stored as a two-dimensional array or a sparse matrix data structure in memory to support efficient row and column access.

[0094] The dose allocation unit is connected to the spatiotemporal distribution matrix generation unit, receiving the spatiotemporal distribution matrix and the theoretical total ozone demand. This unit incorporates two logical components: a weight calculator and an allocator. The weight calculator iterates through each spatial location row of the spatiotemporal distribution matrix, sums the values ​​of all columns in that row as the allocation weight for that spatial location, and calculates the global weight sum. The allocator then generates an initial sub-release dose for each spatial location according to the dose allocation formula: the initial sub-release dose equals the theoretical total ozone demand multiplied by the weight of that point divided by the global weight sum. The allocator then executes a normalization processor to ensure that the sum of all initial sub-release doses is exactly equal to the theoretical total ozone demand, outputting the final sub-release dose corresponding to each spatial location.

[0095] The release schedule generation unit is connected to the dose allocation unit, and its function is to complete the final spatiotemporal alignment and optimization. It acquires the output of the dose allocation unit and revisits the spatiotemporal distribution matrix. For each spatial location point, it extracts the distribution of the number of transport vehicles in its time series from the matrix, identifies the time interval of the maximum peak using a peak detector, and determines the release time node by combining it with a configurable lead time offset, such as 5 seconds. The release spatial location is obtained directly from the spatial location point database. Subsequently, the unit initiates the conflict resolution processor to implement the logic described in Example 1 for merging conflicting release points based on a distance threshold. A preset spatial conflict threshold configuration value, such as 10m, is stored in the unit's register for comparison with the calculated straight-line distance along the tunnel. After merging, the unit serializes a set of conflict-free records containing release time nodes, release spatial locations, and sub-release doses to generate a standard-format multi-level release schedule table, such as an SQLite database table or a JSON array, and publishes it via the message bus.

[0096] The multi-level release execution control module is configured to receive the multi-level release schedule and control physical devices according to the instructions in the schedule. This module consists of a scheduler, a signal converter, and a device communication adapter. The scheduler runs as a high-priority thread, polling the multi-level release schedule every 100ms, comparing it with the system clock, and triggering expiring tasks. The signal converter is responsible for converting sub-release dose tasks into device control instructions. It maintains a device registry recording the IP address, port number, and calibrated ozone mass flow rate (e.g., 2.5 g / s) of the ozone release device corresponding to each release space location. The conversion calculation is performed directly here, generating a control data packet containing the valve opening duration. The device communication adapter is a protocol stack component supporting multiple industrial communication protocols such as Modbus TCP, EtherNet / IP, or PROFINET. It encapsulates the control data packets into standard frames of the corresponding protocol and accurately sends them to the solenoid valve controller at the target release space location via a downhole industrial Ethernet switch, synchronously driving the I / O module of the airflow-assisted injection unit at that location. After execution, it receives a receipt, updates the schedule status, and reports it to the management terminal.

[0097] Through the organic combination of the above functional modules, the system transforms a complex logic of dynamic, multi-dimensional, and closed-loop control into an engineering system with a clear structure that is easy to deploy and maintain, and fully realizes multi-stage slow-release ozone oxidation of CO in vehicle exhaust gas in underground mines.

[0098] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. A method for ozone oxidation of CO in multi-stage slow-release exhaust gas from vehicles in underground mines, characterized in that, Includes the following steps: The real-time operating status data of underground transport vehicles and real-time airflow data of roadways are obtained to obtain the current operating condition parameter set; based on the current operating condition parameter set, the exhaust CO emission intensity under the current transport conditions is predicted, and the theoretical total ozone demand required to oxidize the exhaust CO is determined according to the exhaust CO emission intensity. The expected spatiotemporal distribution information of the transport vehicle traveling along the alleyway is obtained. Based on the expected spatiotemporal distribution information and the theoretical total ozone demand, the theoretical total ozone demand is divided into multiple sub-release doses, and a corresponding release time node and release spatial location are assigned to each sub-release dose. According to the release time nodes and release spatial locations corresponding to each sub-release dose, the ozone release devices at the corresponding locations are controlled to release ozone in sequence, so that the released ozone comes into contact with the exhaust gas of the moving vehicle along the roadway axis and oxidizes the exhaust gas CO.

2. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 1, characterized in that, The step of obtaining real-time operating status data of underground transport vehicles and real-time airflow data of roadways to obtain the current operating condition parameter set specifically includes: The engine speed signal, engine load rate signal and exhaust emission temperature signal are read in real time from the on-board diagnostic terminal installed on the transport vehicle. At the same time, the real-time position coordinates and real-time driving speed of the transport vehicle are read from the on-board positioning module to obtain the real-time operating status data of each transport vehicle. Real-time airflow data of the tunnel is obtained by reading the current cross-sectional average wind speed data, cross-sectional average wind direction data, and tunnel ambient temperature data from ultrasonic anemometers and temperature sensors installed on the tunnel sidewalls. The real-time operating status data and the real-time airflow data of the roadway are associated at the same acquisition time to form a working condition parameter tuple for the current time, and the working condition parameter tuple is stored in the time-series buffer. Median filtering is performed on the tuples of operating condition parameters at multiple consecutive sampling times in the time-series buffer to remove abnormal jump values ​​caused by transient interference from sensors. The filtered tuples of operating condition parameters are then arranged in chronological order to form the current set of operating condition parameters.

3. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 1, characterized in that, The step of predicting the CO emission intensity of the exhaust gas under the current transportation conditions based on the current operating condition parameter set, and determining the theoretical total ozone demand required to oxidize the CO in the exhaust gas based on the CO emission intensity, specifically includes: The engine speed data and engine load rate data of each transport vehicle are extracted from the current working condition parameter set. The engine speed of each transport vehicle is multiplied by the engine load rate to obtain the effective working intensity index of the engine of that transport vehicle. The effective working intensity indexes of all transport vehicles are summed to obtain the total working intensity value at the current moment. The total workload value is input into the pre-built CO emission prediction model. The CO emission prediction model takes the total workload value as the input variable and the CO emission mass flow rate per unit time as the output variable. It adopts a multiple linear regression model structure based on the measured data in the mine to calculate the exhaust gas CO emission intensity at the current moment. Read the stoichiometric coefficient of ozone and CO pre-stored in the local database, multiply the CO emission intensity of the exhaust gas by the stoichiometric coefficient, and obtain the baseline ozone requirement required for complete reaction. Obtain the current tunnel ambient temperature data, query the pre-stored temperature influence coefficient on the reaction rate based on the tunnel ambient temperature data, multiply the baseline ozone demand by the temperature influence coefficient on the reaction rate, and obtain the theoretical total ozone demand.

4. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 1, characterized in that, The steps of obtaining the predicted spatiotemporal distribution information of the transport vehicle traveling along the alleyway, and based on the predicted spatiotemporal distribution information and the theoretical total ozone demand, dividing the theoretical total ozone demand into multiple sub-release doses, and assigning a corresponding release time node and release spatial location to each sub-release dose, specifically include: The current real-time location and current real-time speed of each transport vehicle are obtained from the vehicle positioning module. Based on the current real-time location, current real-time speed and the roadway topology, the estimated arrival time of each transport vehicle to each spatial location point in the roadway within a future preset time window is predicted, and the estimated spatiotemporal trajectory of each transport vehicle is obtained. The expected spatiotemporal trajectories of all transport vehicles are superimposed on the time axis and the spatial axis. The number of transport vehicles expected to pass through each spatial location point in the alley within each time interval in the future preset time window is calculated to obtain the spatiotemporal distribution matrix. Using the number of transport vehicles expected to pass through each spatial location point in the spatiotemporal distribution matrix as the allocation weight, the theoretical total ozone demand is allocated according to the weight ratio of each spatial location point, so that the spatial location point with more expected transport vehicles receives a larger ozone allocation dose, thus obtaining the initial sub-release dose corresponding to each spatial location point. The initial sub-release dose corresponding to each spatial location point is normalized so that the sum of the sub-release doses of all spatial location points is equal to the theoretical total ozone demand, thus obtaining the multiple sub-release doses; For each spatial location point, the number of transport vehicles expected to pass through the spatial location point in each time interval is extracted from the spatiotemporal distribution matrix. The time interval in which the number of transport vehicles expected to pass through reaches its peak is taken as the release time node of the spatial location point, and the actual roadway coordinates of each spatial location point are taken as the release spatial location corresponding to the sub-release dose. The release time point and release spatial location of each spatial location are associated with the corresponding sub-release dose and stored to form a multi-level release scheduling table.

5. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 4, characterized in that, The steps of obtaining the expected spatiotemporal distribution information of the transport vehicle traveling along the roadway, and based on the expected spatiotemporal distribution information and the theoretical total ozone demand, dividing the theoretical total ozone demand into multiple sub-release doses, and assigning a corresponding release time node and release spatial location to each sub-release dose, further include a step of conflict resolution for multiple release spatial locations under the same release time node, specifically including: Extract all sub-release doses with the same release time node and their corresponding release spatial locations from the multi-level release scheduling table to form a set of releases at the same time. Calculate the straight-line distance along the roadway between any two release spatial locations in the same-time release set, and determine the sub-release doses corresponding to the two release spatial locations whose straight-line distance along the roadway is less than a preset spatial conflict threshold as having spatial conflict; For sub-release doses that are determined to have spatial conflicts, the sub-release doses corresponding to the release spatial positions with smaller straight-line distances along the roadway are merged into the sub-release doses corresponding to the release spatial positions with larger straight-line distances along the roadway. The merged sub-release doses replace the original two sub-release doses, and the release task at the release spatial position with smaller straight-line distances along the roadway is cancelled. Repeat the merging operation until the straight-line distance along the roadway between any two release spatial locations in the simultaneous release set is not less than the preset spatial conflict threshold. Then update the simultaneous release set after conflict resolution to the multi-level release scheduling table.

6. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 1, characterized in that, The step of sequentially controlling the ozone release devices at corresponding locations to release ozone according to the release time nodes and release spatial locations corresponding to each sub-release dose, so that the released ozone comes into contact with and oxidizes the CO in the exhaust gas of moving vehicles along the axial direction of the roadway, specifically includes: Extract the sub-release doses and their release spatial locations corresponding to all release time nodes that are about to arrive at the current moment from the multi-level release scheduling table; for each sub-release dose to be executed at the current moment, convert the dose value of the sub-release dose into a control signal for the valve opening duration of the corresponding ozone release device; The valve opening duration control signal is sent to the solenoid valve controller of the ozone release device at the corresponding release space location, so that the solenoid valve remains open during the valve opening duration to release the corresponding dose of ozone. During the opening of the solenoid valve, the airflow-assisted injection unit at the ozone release device is activated simultaneously to ensure that the released ozone is evenly distributed along the transverse cross section of the roadway. After executing all the sub-release doses to be executed at the current moment, the corresponding release time node in the multi-level release scheduling table is marked as executed, and an execution confirmation signal is returned to the management terminal.

7. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 1, characterized in that, It also includes online monitoring of CO concentration after multi-stage release and dynamic dose adjustment between stages, specifically including: CO concentration monitoring points are set between the release space locations of every two adjacent ozone release devices. Within a preset monitoring time window after the ozone release device completes the release of the sub-release dose, the CO concentration values ​​of each CO concentration monitoring point are continuously collected to obtain the actual CO concentration decay curve of each monitoring point. The peak decrease amplitude and decrease rate are extracted from the actual CO concentration decay curves at each monitoring point. The peak decrease amplitude and decrease rate are compared with the theoretical expected decrease amplitude and theoretical decrease rate corresponding to the pre-stored sub-release dose to calculate the actual oxidation efficiency of the ozone release device. If the actual oxidation efficiency is lower than the preset efficiency threshold, the compensation coefficient of the adjacent ozone releasing device downstream of the ozone releasing device that has not yet performed release is calculated based on the ratio of the actual oxidation efficiency to the preset efficiency threshold. The compensation coefficient is multiplied by the original sub-release dose of the adjacent ozone releasing device to obtain the compensated sub-release dose of the adjacent ozone releasing device. The compensated sub-release dose is used to replace the original sub-release dose of the adjacent ozone releasing device in the multi-level release scheduling table.

8. The ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 1, characterized in that, It also includes a multi-level release strategy dynamic adjustment step based on changes in vehicle driving status, specifically including: During the ozone release process according to the multi-level release schedule, the real-time driving speed of each transport vehicle is continuously monitored. If the absolute value of the deviation between the real-time driving speed of a transport vehicle and the current real-time driving speed used in the prediction exceeds the preset speed deviation threshold, the dynamic adjustment process of the release strategy is triggered. The real-time position and direction of the speed deviation of the transport vehicle that has a speed deviation are obtained from the current working condition parameter set. If the direction of the speed deviation is the deceleration direction, it is determined that the residence time of the exhaust emission source of the transport vehicle in the roadway is extended, and the residence increment time of the exhaust emission source at the release space location caused by the speed deviation is calculated. Based on the residence increment time, the value of the corresponding sub-release dose at the release spatial location is increased, and the release time node of at least one release spatial location downstream of the release spatial location is adjusted forward by a preset time step in the multi-level release scheduling table. The adjusted sub-release dose and release time node are updated in the multi-level release scheduling table, and the corresponding ozone release device is controlled to release ozone according to the updated multi-level release scheduling table.

9. An ozone oxidation system for multi-stage slow-release CO exhaust gas from underground mining vehicles, used to implement the ozone oxidation method for multi-stage slow-release CO exhaust gas from underground mining vehicles as described in any one of claims 1 to 8, characterized in that, include: Operating condition data acquisition module, exhaust emission prediction and demand determination module, time and space allocation and scheduling module, multi-level release execution control module; The working condition data acquisition module is configured to acquire real-time operating status data of underground transport vehicles and real-time airflow data of roadways, obtain the current working condition parameter set, and send the current working condition parameter set to the exhaust gas emission prediction and demand determination module and the spatiotemporal allocation and scheduling module. The exhaust emission prediction and demand determination module is configured to receive the current operating condition parameter set, predict the exhaust CO emission intensity under the current transportation condition based on the current operating condition parameter set, determine the theoretical total ozone demand required to oxidize the exhaust CO according to the exhaust CO emission intensity, and send the theoretical total ozone demand to the spatiotemporal allocation and scheduling module. The spatiotemporal allocation and scheduling module is configured to receive the theoretical total ozone demand and the real-time location data and real-time driving speed data from the current operating condition parameter set, obtain the expected spatiotemporal distribution information of the transport vehicle traveling along the roadway, divide the theoretical total ozone demand into multiple sub-release doses according to the expected spatiotemporal distribution information and the theoretical total ozone demand, assign a corresponding release time node and release spatial location to each sub-release dose, generate a multi-level release scheduling table, and send the multi-level release scheduling table to the multi-level release execution control module; The multi-level release execution control module is configured to receive the multi-level release schedule table and, according to the release time node and release spatial position corresponding to each sub-release dose in the multi-level release schedule table, sequentially control the ozone release device at the corresponding position to perform ozone release.

10. The ozone oxidation system for multi-stage slow-release CO exhaust gas from underground mining vehicles according to claim 9, characterized in that, The spatiotemporal allocation and scheduling module includes a spatiotemporal trajectory prediction unit, a spatiotemporal distribution matrix generation unit, a dose allocation unit, and a release scheduling table generation unit; The spatiotemporal trajectory prediction unit is configured to predict the estimated arrival time of each transport vehicle to each spatial location point in the lane within a future preset time window, based on the current real-time location and current real-time driving speed of each transport vehicle and the lane path topology, thereby obtaining the estimated spatiotemporal trajectory of each transport vehicle. The spatiotemporal distribution matrix generation unit is connected to the spatiotemporal trajectory prediction unit and is configured to receive the expected spatiotemporal trajectories of each transport vehicle, superimpose all the expected spatiotemporal trajectories on the time axis and the spatial axis, calculate the number of transport vehicles expected to pass through each spatial location point in the alley within each time interval in the future preset time window, and generate a spatiotemporal distribution matrix. The dose allocation unit is connected to the spatiotemporal distribution matrix generation unit and is configured to receive the spatiotemporal distribution matrix and the theoretical total ozone demand. Using the number of transport vehicles expected to pass through each spatial location point in the spatiotemporal distribution matrix as the allocation weight, the theoretical total ozone demand is allocated according to the weight ratio of each spatial location point. The initial sub-release doses after allocation are normalized so that the sum of the normalized sub-release doses is equal to the theoretical total ozone demand, and the sub-release doses corresponding to each spatial location point are output. The release schedule generation unit is connected to the dose allocation unit and is configured to receive sub-release doses corresponding to each spatial location point. For each spatial location point, the number of transport vehicles expected to pass through the spatial location point in each time interval is extracted from the spatiotemporal distribution matrix. The time interval in which the number of transport vehicles expected to pass through the spatial location point reaches its peak is taken as the release time node of the spatial location point. The actual roadway coordinates of each spatial location point are taken as the release spatial location corresponding to the sub-release dose. The release time node and release spatial location of each spatial location point are associated and stored with the corresponding sub-release dose to generate a multi-level release schedule table.

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