Method and system for matching ozone generator production rate with downhole automobile exhaust co load

Abstract

The present application relates to the field of tail gas treatment, in particular to a method and system for matching the yield of an ozone generator with the CO load of underground automobile exhaust, the method comprising: obtaining roadway environment data and operating conditions of underground transport vehicles to predict the current CO emission load; obtaining ozone generator operating parameters to calculate the actual yield; obtaining a matching coefficient by comparing the ratio of the CO load prediction value and the yield monitoring value with a target interval to determine the adjustment direction; generating a control instruction to adjust the operating parameters according to the adjustment direction to dynamically adapt the yield to the CO load. The system is provided with prediction, monitoring, calculation, adjustment direction determination and control modules. The present application solves the problem that the traditional fixed yield cannot adapt to the CO load fluctuation, ensures sufficient degradation of CO when the load changes, eliminates the risk of toxic gas accumulation, avoids secondary pollution and equipment corrosion caused by excess ozone, significantly improves the purification accuracy and stability, reduces the invalid energy consumption, and enhances the energy saving and environmental protection of underground tail gas treatment.

Description

Technical Field

[0001] This invention relates to the field of exhaust gas treatment, specifically to a method and system for matching ozone generator output with CO load from underground vehicle exhaust. Background Technology

[0002] In underground mining operations, transport vehicles are the core equipment for material transfer and personnel transport. The continuous combustion of fuel in these vehicles produces large amounts of toxic and harmful carbon monoxide (CO). If CO cannot be effectively degraded in the tunnels in a timely manner, it will accumulate, leading to substandard air quality and seriously threatening the safety of underground workers. This also violates mine ventilation, environmental protection, and safe operation regulations. Currently, the industry commonly uses ozone oxidation degradation technology to treat CO from underground vehicle exhaust. Ozone's strong oxidizing properties oxidize and decompose toxic CO into harmless carbon dioxide, thus purifying the tunnel exhaust. However, existing ozone purification control systems have significant shortcomings in practical applications. The core problem is that the output rate of the ozone generator cannot accurately match the real-time CO emission load from underground vehicle exhaust. Existing technologies mostly adopt a fixed power, fixed output ozone mode, relying solely on manual adjustments of the ozone generator's operating parameters based on operational experience. This fails to accurately predict the CO emission load based on the real-time operating status of underground transport vehicles and dynamic changes in the tunnel environment. Underground operations are highly dynamic and unpredictable. The starting and stopping, speed, load, and engine workload of underground transport vehicles constantly change, directly causing real-time fluctuations in CO emissions. Simultaneously, environmental parameters such as wind speed and ambient temperature continuously alter the diffusion, dilution, and reaction rates of CO, resulting in a dynamic CO load within the tunnel. Under traditional fixed ozone production operation modes, when underground vehicles are operating intensively and the CO load surges, the ozone production supply is insufficient to completely degrade CO in the exhaust gas, leading to the accumulation of toxic gas residues and posing significant safety hazards. Conversely, when the number of underground vehicles decreases and the CO load drops, ozone continues to be excessively output, resulting in ozone resource waste, significantly increased equipment energy consumption, and the accumulation of excess ozone in the tunnel, causing secondary air pollution, corrosion of underground electromechanical equipment, and shortening equipment lifespan. In summary, existing technologies cannot achieve real-time adaptive matching between ozone production and dynamic CO load, exhibiting core technical problems such as poor purification effect, high energy consumption, and susceptibility to secondary pollution, making it difficult to meet the precise control requirements for exhaust gas purification in complex and dynamic underground scenarios. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for matching the ozone generator output with the CO load of underground vehicle exhaust, so as to solve the problem that the ozone output is fixed in the prior art and cannot adapt to the dynamic changes of CO load, resulting in incomplete exhaust purification or excessive ozone waste.

[0004] To achieve the above objectives, the following technical solution is adopted.

[0005] A method for matching ozone generator output with downhole vehicle exhaust CO load includes the following steps:

[0006] The current operating condition data of the underground transport vehicle and the current roadway environment data are obtained. Based on the current operating condition data and the current roadway environment data, the exhaust gas CO emission load at the current moment is predicted to obtain the predicted CO load value.

[0007] Obtain the current operating parameters of the ozone generator, calculate the current actual output of the ozone generator based on the current operating parameters, and obtain the ozone output monitoring value;

[0008] The ratio of the predicted CO load value to the monitored ozone production value is calculated to obtain the production-load matching coefficient, and the production adjustment direction of the ozone generator is determined according to the production-load matching coefficient and the preset target matching coefficient range.

[0009] Based on the yield adjustment direction, a yield adjustment control command is generated for the ozone generator. The yield adjustment control command is used to adjust at least one operating parameter of the ozone generator so that the ozone yield of the ozone generator changes in a direction that matches the predicted CO load value.

[0010] Optionally, the step of acquiring the current operating condition data and current roadway environment data of the underground transport vehicle, and predicting the exhaust CO emission load at the current moment based on the current operating condition data and the current roadway environment data to obtain the predicted CO load value specifically includes:

[0011] The engine speed signal, engine load rate signal and instantaneous fuel consumption signal are read in real time from the on-board diagnostic terminal of each transport vehicle to form the current operating condition data of each vehicle.

[0012] Read the current cross-sectional average wind speed data and tunnel ambient temperature data from the wind speed sensors and temperature sensors installed in the tunnel, and use them as the current tunnel environment data;

[0013] The effective output power of each car's engine is obtained by multiplying the engine speed of each car by the engine load rate. Then, based on the engine displacement of each car, the effective output power of the engine is converted into the theoretical CO emission per unit time. The theoretical CO emission of all cars is added together to obtain the initial total CO emission.

[0014] Using the current cross-sectional average wind speed data as a dilution factor and the current tunnel ambient temperature data as a reaction rate correction factor, the initial total CO emissions are divided by the dilution factor and then multiplied by the reaction rate correction factor to obtain the predicted CO load value.

[0015] Optionally, the step of obtaining the current operating parameters of the ozone generator, calculating the current actual output of the ozone generator based on the current operating parameters, and obtaining the ozone output monitoring value specifically includes:

[0016] The current effective value of discharge voltage, the current effective value of discharge current, and the current power supply frequency are read from the power controller of the ozone generator. At the same time, the current inlet temperature and outlet temperature of the cooling medium are read from the cooling system of the ozone generator to obtain the current set of operating parameters.

[0017] Multiply the current effective value of the discharge voltage by the current effective value of the discharge current, and then multiply by the power factor to obtain the current input power.

[0018] Based on the difference between the current inlet temperature and the current outlet temperature of the cooling medium, combined with the specific heat capacity and flow rate of the cooling medium, the heat removed from the ozone generator at the current moment is calculated to obtain the heat dissipation power value.

[0019] Subtracting the heat dissipation power value from the current input power yields the effective power for ozone synthesis. Multiplying the effective power by the pre-stored ozone generator electro-ozone conversion efficiency coefficient yields the current actual yield, which is used as the ozone yield monitoring value.

[0020] Optionally, the step of calculating the ratio between the predicted CO load value and the monitored ozone yield value to obtain a yield-load matching coefficient, and determining the yield adjustment direction of the ozone generator based on the yield-load matching coefficient and a preset target matching coefficient range, specifically includes:

[0021] Using the ozone production rate monitoring value as the divisor and the CO load prediction value as the divisor, the ratio of the ozone production rate monitoring value to the CO load prediction value is calculated to obtain the production rate-load matching coefficient.

[0022] Read the pre-configured target matching coefficient range containing the minimum target matching coefficient and the maximum target matching coefficient from the local memory, and compare the yield-load matching coefficient with the minimum target matching coefficient and the maximum target matching coefficient;

[0023] If the yield-load matching coefficient is less than the minimum target matching coefficient, it is determined that the current ozone yield is low relative to the CO load, and the yield adjustment direction is determined to be upward.

[0024] If the yield-load matching coefficient is greater than the maximum target matching coefficient, it is determined that the current ozone yield is too high relative to the CO load, and the yield adjustment direction is determined to be downward.

[0025] If the yield-load matching coefficient is between the minimum target matching coefficient and the maximum target matching coefficient, then the current ozone yield is determined to be matched with the CO load, and the yield adjustment direction is determined to be the maintenance direction.

[0026] Optionally, the step of generating a rate adjustment control command for the ozone generator based on the rate adjustment direction, wherein the rate adjustment control command is used to adjust at least one operating parameter of the ozone generator so that the ozone rate of the ozone generator changes in a direction matching the predicted CO load value, specifically includes:

[0027] When the yield adjustment direction is upward, calculate the first difference between the yield-load matching coefficient and the minimum target matching coefficient, and query the pre-stored yield adjustment amount mapping table based on the first difference to obtain the target yield value to be increased.

[0028] The target yield value is converted into a discharge voltage increase command and a discharge frequency increase command. The discharge voltage increase command and the discharge frequency increase command are combined into an upward adjustment control command. The discharge voltage and discharge frequency of the ozone generator are controlled to increase synchronously through the upward adjustment control command.

[0029] When the yield adjustment direction is downward, calculate the second difference between the yield-load matching coefficient and the maximum target matching coefficient, and query the yield adjustment amount mapping table according to the second difference to obtain the target yield value to be reduced.

[0030] The target yield value is converted into a discharge voltage reduction command and a discharge frequency reduction command. The discharge voltage reduction command and the discharge frequency reduction command are combined into a downward adjustment control command. When the yield adjustment direction is the maintenance direction, an empty command to maintain the current operating parameters is generated.

[0031] Optionally, it also includes a discharge stability monitoring and protection step during the yield adjustment process, specifically including: after generating the yield adjustment control command and sending it to the ozone generator, continuously collecting the instantaneous values ​​of the discharge current and discharge voltage in the discharge chamber of the ozone generator, calculating the phase difference between the instantaneous values ​​of the discharge current and discharge voltage, and obtaining the real-time discharge phase angle;

[0032] The equivalent discharge impedance value under the current discharge state is calculated based on the real-time discharge phase angle, and the equivalent discharge impedance value is compared with the pre-stored normal discharge impedance range.

[0033] If the equivalent discharge impedance value is lower than the minimum value of the normal discharge impedance range, it is determined that there is a risk of micro-arc discharge in the current discharge state. The execution of the yield adjustment control command is immediately suspended, and a discharge abnormality protection command is generated. The discharge abnormality protection command controls the ozone generator to reduce the discharge voltage to a safe value.

[0034] If the equivalent discharge impedance value is higher than the maximum value of the normal discharge impedance range, it is determined that there is a risk of open circuit in the current discharge state, an alarm signal is generated and the yield adjustment is stopped, and the current operating parameters are maintained until the abnormality is eliminated.

[0035] Optionally, it also includes a yield-load matching closed-loop correction step based on the measured CO concentration value, specifically including: within a preset correction time window after the yield adjustment control command is generated and sent to the ozone generator, continuously collecting the measured CO concentration value output by the CO concentration sensor deployed in the roadway to obtain the measured CO concentration time series.

[0036] The peak CO concentration and the stable final CO concentration are extracted from the measured CO concentration time series. The peak CO concentration and the stable final CO concentration are compared with the pre-stored target peak CO concentration and target final CO concentration, respectively, to obtain the peak concentration deviation and the final concentration deviation.

[0037] The concentration peak deviation and the concentration final deviation are input into the fuzzy logic corrector, which outputs the yield-load matching coefficient correction amount according to the preset fuzzy rule base.

[0038] The correction amount of the yield-load matching coefficient is superimposed on the current yield-load matching coefficient to obtain the corrected yield-load matching coefficient. The corrected yield-load matching coefficient is used to replace the current yield-load matching coefficient to redetermine the yield adjustment direction, and the redetermined yield adjustment direction is used as the basis for the next yield adjustment.

[0039] Optionally, it also includes a pre-regulation step that synchronizes ozone production with CO load changes, specifically including:

[0040] CO load prediction values ​​are continuously recorded at multiple past sampling times to form a historical time series of CO load. Linear regression analysis is performed on the historical time series of CO load to calculate the rate of change of CO load at the current time and obtain the slope of the CO load change trend.

[0041] The slope of the CO load change trend is compared with a preset slope threshold. If the slope of the CO load change trend exceeds the positive trend threshold, it is determined that the CO load is in a rapid upward trend. Based on the rate adjustment direction determined according to the rate-load matching coefficient at the current moment, an advance adjustment increment is added, and an advance adjustment control command containing the advance adjustment increment is generated. The advance adjustment control command controls the ozone generator to increase the rate in advance at the current moment.

[0042] If the slope of the CO load change trend is lower than the negative trend threshold, it is determined that the CO load is in a rapid downward trend. Based on the yield adjustment direction determined according to the yield-load matching coefficient at the current moment, a hysteresis adjustment reduction is reduced, and a hysteresis adjustment control command containing the hysteresis adjustment reduction is generated. The hysteresis adjustment control command controls the ozone generator to reduce the yield in advance at the current moment.

[0043] Optionally, the step of inputting the concentration peak deviation and the concentration final deviation into the fuzzy logic corrector, wherein the fuzzy logic corrector outputs the yield-load matching coefficient correction amount according to a preset fuzzy rule base, specifically includes:

[0044] The concentration peak deviation is mapped to the first fuzzy domain according to its numerical value, and the concentration peak deviation is converted into low-level membership degree, medium-level membership degree and high-level membership degree of peak deviation through the first membership degree function to obtain the peak deviation fuzzification vector.

[0045] The concentration final value deviation is mapped to the second fuzzy domain according to its numerical value. The concentration final value deviation is converted into a low-level membership degree, a medium-level membership degree, and a high-level membership degree of the final value deviation through the second membership degree function, and the fuzzy vector of the final value deviation is obtained.

[0046] Each membership value in the peak deviation fuzzification vector is paired with each membership value in the final value deviation fuzzification vector to obtain nine sets of deviation combination states;

[0047] For each set of deviation combination states, the output fuzzy subset corresponding to the deviation combination state is queried in the preset fuzzy rule library to obtain nine sets of output membership values. The fuzzy rule library contains nine fuzzy rules, and each fuzzy rule defines the output fuzzy level of the yield-load matching coefficient correction under a deviation combination state.

[0048] The nine sets of output membership values ​​are defuzzified using the centroid method. The weighted average of the correction values ​​corresponding to all output membership values ​​is calculated, and the result of the weighted average is output as the correction amount of the yield-load matching coefficient.

[0049] The ozone generator yield matching system with the CO load of underground vehicle exhaust includes: a CO load prediction module, an ozone yield monitoring module, a matching coefficient calculation and adjustment direction determination module, and a yield adjustment control module.

[0050] The CO load prediction module is configured to acquire the current operating condition data of the underground transport vehicle and the current roadway environment data, predict the exhaust CO emission load at the current moment based on the current operating condition data and the current roadway environment data, obtain the CO load prediction value, and send the CO load prediction value to the matching coefficient calculation and adjustment direction determination module.

[0051] The ozone production rate monitoring module is configured to acquire the current operating parameters of the ozone generator, calculate the current actual production rate of the ozone generator based on the current operating parameters, obtain the ozone production rate monitoring value, and send the ozone production rate monitoring value to the matching coefficient calculation and adjustment direction determination module.

[0052] The matching coefficient calculation and adjustment direction determination module is configured to receive the CO load prediction value and the ozone production rate monitoring value, perform a ratio calculation between the CO load prediction value and the ozone production rate monitoring value to obtain the production rate-load matching coefficient, determine the production rate adjustment direction of the ozone generator based on the production rate-load matching coefficient and the preset target matching coefficient range, and send the production rate adjustment direction to the production rate adjustment control module.

[0053] The yield adjustment control module is configured to receive the yield adjustment direction and generate a yield adjustment control command for the ozone generator based on the yield adjustment direction. The yield adjustment control command is used to adjust at least one operating parameter of the ozone generator so that the ozone yield of the ozone generator changes in a direction that matches the predicted CO load value.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] This application accurately predicts the CO emission load of underground transport vehicles by collecting real-time operating data and roadway environmental data. Simultaneously, it monitors the actual ozone production rate of the ozone generator and constructs a production-load matching coefficient based on the ratio of these two data points. Combined with a preset target range, it determines the direction of production rate adjustment and generates corresponding control commands. This achieves adaptive and precise matching between the ozone generator production rate and the dynamic CO load underground. It effectively solves the core problem of traditional technologies where the fixed ozone production rate cannot adapt to dynamic changes in CO load, leading to incomplete exhaust gas purification or excessive ozone waste. This approach ensures sufficient degradation of CO exhaust gas during underground CO load fluctuations, completely eliminating the safety hazard of toxic gas accumulation in roadways. It also avoids secondary pollution and equipment corrosion caused by excess ozone, significantly improving the accuracy and stability of underground vehicle exhaust gas purification while significantly reducing the ineffective energy consumption of the ozone generator, thus enhancing the energy efficiency and environmental friendliness of underground mine exhaust gas purification operations.

[0056] Based on this, this application precisely corrects the predicted CO load value by combining vehicle engine operating parameters, tunnel temperature and humidity, and wind speed parameters. It also accurately calculates the actual ozone production rate based on the ozone generator's electrical parameters and heat dissipation losses, significantly improving the accuracy of core data detection and calculation, and providing reliable data support for precise matching and adjustment. Through a refined control mode that determines the direction of production rate adjustment in stages and corresponds to the adjustment amount based on the matching difference, it achieves synchronous and coordinated adjustment of discharge voltage and power supply frequency, making ozone production rate adjustment smoother and more precise, avoiding large fluctuations that could affect equipment operational stability. Simultaneously, this application adds a real-time discharge status monitoring and abnormal protection mechanism. By analyzing the equipment's operating status in real time through discharge impedance, it effectively avoids the risks of micro-arc discharge, circuit breaks, and other faults, ensuring the long-term stable operation of the ozone generator. Based on measured tunnel CO concentration data, a fuzzy logic closed-loop correction mechanism is constructed to dynamically optimize and correct the matching control parameters, compensating for static deviations in the prediction model and continuously improving the system's control accuracy. Furthermore, this application achieves proactive pre-regulation through regression analysis of CO load change trends, which can predict the rise and fall trends of CO load and intervene in regulation in advance, completely solving the lag problem of traditional regulation modes, further adapting to complex and dynamic downhole operation scenarios, comprehensively improving the safety, stability, accuracy and energy efficiency of downhole exhaust gas purification systems, and adapting to the long-term stable purification needs of various downhole transportation operation conditions. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart illustrating the steps of an embodiment of the method for matching the ozone generator yield with the CO load of underground vehicle exhaust in this invention.

[0058] Figure 2 This is a schematic diagram of a module in an embodiment of the ozone generator yield matching system with underground vehicle exhaust CO load of the present invention. Detailed Implementation

[0059] 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.

[0060] 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.

[0061] Example 1

[0062] like Figure 1 As shown in the figure, this embodiment discloses a method for matching ozone generator output with CO load from underground vehicle exhaust. This method is applicable to confined spaces such as underground mining faces and roadway transport channels. It dynamically matches and purifies CO exhaust continuously emitted by underground fuel transport vehicles, adapting to complex working conditions such as multiple vehicles operating randomly and real-time fluctuations in roadway environmental parameters, achieving adaptive and precise control of ozone output. The overall execution logic of this method is to collect two types of basic data in real time: the operating conditions of underground vehicles and the roadway environment. Through multi-dimensional data fusion calculation, it completes the dynamic prediction of CO load from underground exhaust. Simultaneously, it accurately calculates the real-time ozone output of the equipment based on the electrical parameters and heat loss parameters of the ozone generator. By coupling and comparing the two types of core parameters, a quantitative matching system is constructed to dynamically determine the adjustment direction and magnitude of ozone output, outputting precise equipment control commands. At the same time, it is equipped with auxiliary control logic such as real-time discharge state protection, closed-loop correction of measured data, and pre-adjustment of load trends, achieving dynamic matching control with high precision and high stability throughout the entire process.

[0063] In the specific implementation of dynamic CO load prediction in underground mines, the system relies on the on-board diagnostic terminals mounted on each underground transport vehicle to achieve real-time data acquisition under all operating conditions. The on-board diagnostic terminals establish real-time communication with the vehicle's engine electronic control unit, continuously reading engine speed signals, engine load rate signals, and instantaneous fuel consumption signals at a fixed sampling frequency. The sampling frequency is set to a fixed value higher than the fluctuation frequency of underground operating conditions to ensure that the collected data can completely cover all operating conditions such as vehicle start-stop, idling, acceleration, and full-load driving. The three types of operating parameters of all vehicles are synchronously summarized to the underground main control system, integrating them to form the current operating condition data of the underground transport vehicles across the entire area. The acquisition of tunnel environmental data is completed by a distributed sensor network deployed throughout the tunnel. The sensors are evenly distributed along the tunnel cross-section, with no monitoring blind spots. The wind speed sensor continuously collects the average wind speed data of the tunnel cross-section, and the temperature sensor collects the internal ambient temperature data of the tunnel in real time. The two types of environmental data are sampled and updated synchronously with the vehicle operating condition data to ensure the temporal consistency of the operating condition data and environmental data and avoid prediction deviations caused by temporal misalignment.

[0064] After completing the basic data collection, the system calculates the basic CO emissions of each individual transport vehicle. It accurately represents the real-time effective output power of the vehicle's engine by multiplying engine speed and engine load rate. This calculation method truly reflects the engine's current fuel work status and combustion completeness, offering higher accuracy in operating condition identification compared to methods relying solely on engine speed or fuel consumption. Combining the vehicle's factory-calibrated engine displacement parameters, the system converts the engine's effective output power according to operating conditions to obtain the theoretical CO emissions of a single vehicle per unit time. This conversion logic is based on the emission mechanism of internal combustion engine fuel combustion pollutants and is adapted to the emission calculation needs of different displacement and model underground transport vehicles. After calculating the theoretical CO emissions of a single vehicle, the emissions of all operating transport vehicles underground are accumulated to obtain the initial total CO emissions in the tunnel, fully covering the exhaust emission contribution of all vehicles operating in the entire area.

[0065] Based on this, dynamic correction calculations for CO load are performed by combining tunnel environmental parameters. The average wind speed of the tunnel cross section directly determines the air dilution and diffusion efficiency of exhaust gas within the tunnel. The higher the wind speed, the higher the dilution degree of CO gas per unit space, and the lower the local CO load. Therefore, real-time cross-sectional average wind speed data is used as a dynamic dilution factor for CO load to dilute and correct the initial total CO emissions. Tunnel ambient temperature directly affects the oxidation reaction rate of CO and ozone, as well as the molecular diffusion rate of exhaust gas. Changes in ambient temperature directly alter the accumulation and degradation efficiency of CO within the tunnel. Therefore, real-time tunnel ambient temperature data is used as a reaction rate correction factor to perform secondary calibration on the diluted and corrected CO load. By dividing the initial total CO emissions by the dilution factor and then multiplying by the reaction rate correction factor, a CO load prediction value that truly reflects the real-time pollution level of the tunnel is finally obtained. This achieves dual-dimensional dynamic correction based on vehicle operating conditions and tunnel environment, completely solving the problem that static emission calculation methods cannot adapt to dynamic underground operating conditions.

[0066] During the precise calculation of ozone production rate monitoring values, the system collects real-time operating parameters of the entire ozone generator. The parameter collection scope covers two major dimensions: electrical operating parameters and cooling system operating parameters. The system collects the current effective value of discharge voltage, the current effective value of discharge current, and the current power supply frequency in real time from the power controller of the ozone generator. Simultaneously, it collects the inlet temperature, outlet temperature, and real-time flow rate of the cooling medium from the temperature monitoring module and flow monitoring module of the equipment cooling system. All parameters are integrated to form a complete set of current operating parameters of the ozone generator, comprehensively covering the core hardware operating status parameters that affect ozone generation efficiency.

[0067] Based on the collected electrical parameters, the system calculates the effective input power of the equipment. Combining this with the high-voltage discharge characteristics of the ozone generator, it accurately calculates the current input power of the ozone generator by multiplying the current effective discharge voltage, the current effective discharge current, and the equipment's inherent power factor, thus accurately reflecting the total current electrical energy input. Considering the significant heat loss generated during the high-voltage discharge of the ozone generator, which directly reduces the electro-ozone conversion efficiency, the system calculates the real-time heat dissipation power of the equipment using the temperature difference of the cooling medium, the medium's inherent specific heat capacity, and the real-time flow rate. This accurately counts the ineffective power lost as heat during operation. Subtracting the real-time heat dissipation power from the real-time input power yields the effective power fully utilized for the ozone synthesis reaction, eliminating the interference of heat loss on the yield calculation. Combined with the electro-ozone conversion efficiency coefficient determined through multiple calibration tests before the equipment leaves the factory, the system calculates the current actual yield of the ozone generator by multiplying the effective power and the conversion efficiency coefficient. This yields the ozone yield monitoring value, achieving quantitative, accurate, and real-time monitoring of ozone yield, avoiding the large errors and low accuracy of traditional estimation methods.

[0068] In the process of calculating the yield-load matching coefficient and determining the adjustment direction, the system uses standardized ratio calculation logic to establish a quantitative matching relationship between load and yield. The ozone yield monitoring value is used as the divisor, and the CO load prediction value as the divisor. The yield-load matching coefficient is calculated through real-time ratio calculation. This coefficient can intuitively quantify the degree of matching between the current ozone yield and the real-time CO pollution load; the magnitude of the coefficient directly corresponds to the supply-demand matching status. The system's local memory pre-stores a target matching coefficient range calibrated through numerous downhole operating condition tests. This range is comprehensively calibrated based on the downhole safe CO concentration threshold, the optimal ozone degradation reaction ratio, and the safe operating range of the equipment. It includes fixed minimum and maximum target matching coefficients, providing a unified quantitative standard for determining the matching status.

[0069] The system compares the real-time calculated yield-load matching coefficient with the preset target matching coefficient range in real time, forming a three-level differentiated adjustment logic. When the yield-load matching coefficient is less than the minimum target matching coefficient, it indicates that the current ozone production capacity cannot match the real-time CO pollution load, the ozone oxidation and degradation capacity is insufficient, and there is a risk of CO residue accumulation in the tunnel. The system determines the yield adjustment direction to be upward. When the yield-load matching coefficient is greater than the maximum target matching coefficient, it indicates that the current ozone production supply is excessive, far exceeding the optimal ratio required for real-time CO degradation, and there is a risk of ozone over-waste and secondary pollution. The system determines the yield adjustment direction to be downward. When the yield-load matching coefficient is between the minimum and maximum target matching coefficients, it is determined that the current ozone production and CO load are in an optimal matching state, and the degradation efficiency and energy consumption control are both in the optimal range. The system determines the yield adjustment direction to be maintained, and no parameter adjustment is required.

[0070] During the precise generation and execution of yield adjustment control commands, the system achieves refined, graded adjustment based on the matching deviation level, eliminating over- or under-adjustment issues caused by fixed-amplitude adjustments. When the yield adjustment direction is upward, the system accurately calculates the first difference between the real-time yield-load matching coefficient and the minimum target matching coefficient. This difference quantifies the gap size of the current matching state; a larger difference indicates a larger ozone yield gap and a higher required adjustment level. The system calls a pre-stored yield adjustment mapping table, which is calibrated based on the overall equipment characteristics and adjustment response rates under different operating conditions. This table includes a one-to-one correspondence between the difference range and the target yield adjustment amount. The required increase in target yield value is obtained based on the first difference. Subsequently, the target yield increment is synchronously converted into corresponding discharge voltage increase commands and discharge frequency increase commands. These two types of commands are output synchronously and adjusted collaboratively. By synchronously increasing both voltage and frequency parameters, the discharge energy of the ozone generator is steadily increased, thereby precisely improving the ozone yield and ensuring accurate matching between the yield increment and the load gap.

[0071] When the rate adjustment direction is downward, the system calculates the second difference between the real-time rate-load matching coefficient and the maximum target matching coefficient to quantify the excess level of ozone production. Based on the second difference, it queries the rate adjustment mapping table to match the target rate reduction value and generates corresponding commands to reduce discharge voltage and discharge frequency. This dual-parameter synchronous downward adjustment achieves precise reduction of ozone production, avoiding excessive ozone output. When the rate adjustment direction is to maintain, the system generates an empty command to keep all current operating parameters of the ozone generator unchanged, maintaining the optimal matching working state and avoiding equipment operation fluctuations and energy consumption losses caused by ineffective adjustments. The entire adjustment logic achieves deviation quantification, graded adjustment, and dual-parameter coordinated control, resulting in higher adjustment accuracy and stronger equipment operation stability.

[0072] This method incorporates full-process discharge stability monitoring and active protection logic during dynamic adjustment, achieving safe closed-loop control of the adjustment process. After each yield adjustment control command is issued and executed, the system continuously collects the instantaneous values ​​of discharge current and discharge voltage within the ozone generator discharge chamber at high frequency, tracking the high-voltage discharge state throughout the process. The real-time discharge phase angle is calculated using synchronously collected instantaneous voltage and current, accurately reflecting the working phase state of the discharge circuit. Based on the real-time discharge phase angle, the equivalent discharge impedance value under the current discharge state is further calculated. This equivalent discharge impedance value accurately characterizes the electrode working state, dielectric state, and circuit continuity state within the discharge chamber, serving as a core characteristic parameter for determining ozone generator discharge faults.

[0073] The system pre-stores a normal discharge impedance range calibrated based on the equipment's normal operating conditions. It compares the real-time equivalent discharge impedance value with this preset range in real time, enabling accurate fault identification and proactive protection. When the equivalent discharge impedance value is lower than the minimum value of the normal discharge impedance range, it determines that there is a risk of micro-arc discharge in the discharge chamber, such as electrode tip discharge or dielectric breakdown. Micro-arc discharge can lead to uneven discharge, a sharp drop in ozone generation efficiency, and even localized overheating and damage to the equipment. The system immediately suspends the currently executing rate adjustment control command and simultaneously generates a discharge anomaly protection command, forcibly reducing the ozone generator discharge voltage to a preset safe voltage value to ensure equipment hardware safety. When the equivalent discharge impedance value is higher than the maximum value of the normal discharge impedance range, it determines that there is a risk of faults in the discharge circuit, such as poor contact or electrode aging and open circuit. The system immediately outputs an audible and visual alarm signal and simultaneously stops all rate adjustment actions, maintaining stable operation of the equipment's current parameters until the abnormal fault is completely eliminated. This thoroughly avoids the problem of equipment fault expansion during the adjustment process and improves the long-term safety and stability of the equipment.

[0074] This method also incorporates a closed-loop correction logic based on measured CO concentration values ​​for yield-load matching, addressing static bias and environmental interference bias issues inherent in single prediction models and enabling continuous iterative optimization of control accuracy. After each yield adjustment control command is issued and executed, the system opens a preset correction time window. Within this window, it continuously collects measured data from CO concentration sensors deployed throughout the roadway, recording data at a fixed sampling period to form a complete measured CO concentration time series, fully documenting the dynamic changes in roadway CO concentration after adjustment. The system extracts features from the measured time series, accurately identifying the CO concentration peak and stable final CO concentration. The peak concentration reflects the maximum residual pollution level at the moment of adjustment, while the stable final value reflects the steady-state purification effect after adjustment. These two core feature values ​​comprehensively evaluate the actual effect of the yield adjustment.

[0075] The system precisely compares the extracted peak CO concentration and stable final CO concentration with the pre-stored target peak CO concentration and target final CO concentration, respectively, calculating the peak concentration deviation and the final concentration deviation. These two types of deviations quantify the difference between the theoretical adjustment effect and the actual purification effect. The dual-dimensional deviation data is input into a preset fuzzy logic corrector. Based on a built-in standardized fuzzy rule library, the corrector outputs a precise yield-load matching coefficient correction. This correction is used to dynamically adjust the current yield-load matching coefficient, using the corrected coefficient as a new matching criterion for determining the direction of the next yield adjustment. This achieves closed-loop control of "prediction-adjustment-measurement-correction," continuously eliminating control deviations caused by model errors, environmental interference, and equipment aging, allowing the system's control accuracy to continuously optimize during operation.

[0076] The specific execution process of the fuzzy logic corrector includes a complete workflow of multi-dimensional fuzzification, rule matching, and defuzzification, possessing strong adaptability to various operating conditions. The system first fuzzifies the concentration peak deviation, mapping the numerical range of the peak deviation to a preset first fuzzy domain. Using a pre-calibrated first membership function, the precise numerical deviation is converted into low-level, medium-level, and high-level membership degrees of the peak deviation, constructing a fuzzified vector for the peak deviation and achieving a reasonable conversion from precise values ​​to a fuzzy state. Simultaneously, the final concentration deviation is processed in the same dimension, mapping the final deviation to a second fuzzy domain. Using a second membership function, low-level, medium-level, and high-level membership degrees of the final deviation are obtained, constructing a fuzzified vector for the final deviation.

[0077] After constructing the two-dimensional fuzzy vectors, the system performs pairwise full combinations of the membership values ​​of the peak deviation fuzzy vector and the final value deviation fuzzy vector, forming nine different deviation combination states, comprehensively covering all working conditions of downhole purification effect deviation. The system has a built-in fuzzy rule library containing nine exclusive fuzzy rules. Each fuzzy rule corresponds to the output fuzzy level of the yield-load matching coefficient correction under a certain deviation combination state. By matching the current deviation combination state one by one, the corresponding output fuzzy subset is retrieved to obtain nine sets of corresponding output membership values. Finally, the centroid method is used to complete the defuzzification operation, and the weighted average of the correction values ​​corresponding to all output membership values ​​is calculated. The weighted average result is used as the final yield-load matching coefficient correction value for output, achieving precise quantitative correction of deviation. The correction logic is scientifically rigorous and highly adaptable, capable of meeting the optimization needs of various complex deviation working conditions.

[0078] This method further incorporates synchronized pre-regulation logic for ozone production and CO load change trends, overcoming the technical shortcomings of traditional lagging regulation and achieving proactive prediction and control of load changes. The system continuously records CO load forecasts from multiple historical sampling times, arranging them chronologically to construct a complete historical CO load time series, fully preserving recent dynamic load change patterns. Linear regression analysis of the historical CO load time series is performed using the least squares method to accurately calculate the real-time rate of change of CO load at the current moment, quantifying the slope of the CO load change trend. The sign and magnitude of the slope precisely characterize the rise and fall trends and the severity of load changes.

[0079] The system has two preset thresholds: a positive trend threshold and a negative trend threshold, used to distinguish between stable load changes and drastic fluctuations. When the slope of the CO load change trend exceeds the positive trend threshold, it is determined that the underground CO load is in a rapid upward trend, and the roadway pollution load will continue to increase in the short term. Based on the basic adjustment direction determined by the current production rate-load matching coefficient, the system adds an advance adjustment increment adapted to the current rate of increase, generating an advance adjustment control command containing the advance adjustment increment. This controls the ozone generator to increase the ozone production rate in advance, achieving pre-emptive regulation before the pollution load rises and completely eliminating adjustment lag. When the slope of the CO load change trend is lower than the negative trend threshold, it is determined that the underground CO load is in a rapid downward trend. Based on the basic adjustment direction, the system adds an adapted lag adjustment reduction, generating a lag adjustment control command to reduce the ozone production rate in advance, avoiding excess ozone waste after the load decreases. This achieves accurate prediction and advance adaptation of dynamic trends, significantly improving the system's dynamic response capability.

[0080] Example 2

[0081] like Figure 2 As shown, this embodiment discloses an ozone generator yield and underground vehicle exhaust CO load matching system. This system fully implements the ozone generator yield and underground vehicle exhaust CO load matching method described in the previous embodiment. The system adopts a modular architecture design, with each functional module operating independently, interconnected, and collaboratively completing dynamic matching and control. It features accurate data acquisition, efficient processing, rapid control response, comprehensive safety protection, and closed-loop optimization iteration. It can adapt to the complex and harsh working environment of high dust, high humidity, and strong interference underground for extended periods, ensuring the long-term stable, accurate, and energy-efficient operation of the underground exhaust CO purification system. The system includes a CO load prediction module, an ozone yield monitoring module, a matching coefficient calculation and adjustment direction determination module, and a yield adjustment control module. Each module completes real-time data transmission through an underground industrial bus, and the data transmission delay meets the timing requirements of dynamic control.

[0082] The CO load prediction module, as the core unit for front-end data processing and load forecasting, is configured with a multi-source data acquisition and fusion computing architecture. It is primarily responsible for the synchronous acquisition, cleaning, processing, and load prediction output of operating condition data from all underground transport vehicles and roadway environmental data. The CO load prediction module establishes real-time communication with the on-board diagnostic terminals of all underground transport vehicles via a wired communication interface. It supports parallel data acquisition from multiple devices, synchronously reading engine speed signals, engine load rate signals, and instantaneous fuel consumption signals from each vehicle. This enables real-time aggregation and time-series alignment of operating condition data from multiple vehicles. Simultaneously, it incorporates a data filtering algorithm to denoise and remove outliers from the collected raw operating condition data, eliminating abnormal data caused by underground electromagnetic interference and momentary equipment failures, ensuring the authenticity and validity of the operating condition data.

[0083] The CO load prediction module synchronously connects to wind speed and temperature sensors deployed within the tunnel, collecting real-time average wind speed data and ambient temperature data across the tunnel cross-section, and completing synchronous data acquisition and standardized processing. The module has a built-in dedicated CO load calculation program that can calculate the effective output power and theoretical CO emissions of each transport vehicle's engine, summing the total emissions from all vehicles to obtain the initial total CO emissions. This is then combined with wind speed dilution factors and temperature response rate correction factors to perform load correction calculations, ultimately outputting an accurate CO load prediction value. The time-aligned effective CO load prediction value is then transmitted in real-time to the matching coefficient calculation and adjustment direction determination module, providing accurate basic data support for subsequent matching calculations.

[0084] The ozone yield monitoring module is the core unit for equipment operation status sensing and yield quantification calculation, focusing on the acquisition of all-dimensional operating parameters of the ozone generator and accurate calculation of actual yield. The module establishes data connections with the ozone generator's power controller and cooling system monitoring unit, acquiring real-time electrical operating parameters including effective discharge voltage, effective discharge current, and power supply frequency. Simultaneously, it acquires three thermal parameters: cooling medium inlet temperature, cooling medium outlet temperature, and real-time cooling medium flow rate, constructing a complete set of equipment operating parameters. The module incorporates an electrical power calculation model and a heat loss calculation model, enabling accurate calculation of the ozone generator's real-time input electrical power and real-time heat dissipation power. Through effective electrical power conversion and matching with the electro-ozone conversion efficiency coefficient, it accurately obtains the current actual yield of the ozone generator and outputs standardized ozone yield monitoring values.

[0085] The ozone yield monitoring module features continuous monitoring and real-time updates, enabling it to synchronize yield monitoring results with dynamic changes in equipment operating parameters without data update delays. It also has a built-in parameter anomaly identification function, which automatically marks abnormal data and logs it when the collected equipment operating parameters exceed the normal operating range, ensuring the reliability of yield monitoring data. Finally, it continuously transmits the real-time updated ozone yield monitoring values ​​to the matching coefficient calculation and adjustment direction determination module, achieving synchronous linkage with CO load prediction data.

[0086] The matching coefficient calculation and adjustment direction determination module is the core decision-making unit of the system, undertaking the core functions of data coupling calculation, matching status determination, and adjustment direction output. The module receives CO load prediction values ​​from the CO load prediction module and ozone yield monitoring values ​​from the ozone yield monitoring module in real time. Following a fixed ratio calculation logic, it calculates the yield-load matching coefficient in real time, accurately quantifying the real-time matching status of ozone yield and CO load. The module has a built-in local memory pre-stored target matching coefficient ranges calibrated under numerous downhole operating conditions. It can compare the dynamically calculated matching coefficients with the preset ranges in real time, accurately classifying the working states into three categories: upward adjustment direction, downward adjustment direction, and maintenance direction, thus completing the dynamic decision-making for the adjustment direction.

[0087] The matching coefficient calculation and adjustment direction determination module boasts high-speed computing and real-time response capabilities. Its computation cycle adapts to the fluctuation frequency of downhole operating conditions, enabling millisecond-level data updates and status determination, eliminating decision-making lag. Simultaneously, it stores each set of matching coefficient data and adjustment direction determination results in real time, forming an operational log for subsequent data traceability and algorithm optimization. Ultimately, the module transmits the precisely determined yield adjustment direction to the yield adjustment control module in real time, providing a decision-making basis for precise equipment control.

[0088] The yield adjustment control module serves as the system's execution terminal, playing a core role in generating, issuing, executing, and managing control commands. It can match differentiated control logic based on the received yield adjustment direction, achieving adaptive adjustment of the ozone generator's operating parameters. The module has a built-in pre-calibrated yield adjustment mapping table, enabling precise matching of the corresponding yield adjustment range based on the deviation of the matching coefficient, achieving refined, tiered adjustment. When receiving an upward adjustment command, the module accurately calculates the matching coefficient deviation, retrieves the corresponding target yield increment, and simultaneously generates commands to increase discharge voltage and discharge frequency, achieving simultaneous upward adjustment of both parameters. When receiving a downward adjustment command, it matches the yield reduction based on the deviation magnitude, generating commands to decrease discharge voltage and discharge frequency, achieving simultaneous downward adjustment of both parameters. When receiving a maintenance command, it generates a null command to keep the equipment parameters stable.

[0089] The yield regulation control module is equipped with an integrated discharge status monitoring and anomaly protection subunit. It can monitor the instantaneous discharge parameters of the ozone generator throughout the execution of regulation commands, calculate the discharge phase angle and equivalent discharge impedance value in real time, and determine faults based on preset normal discharge impedance ranges. Differentiated protection logics—pause regulation, voltage reduction protection, and alarm shutdown—are implemented for micro-arc discharge risk and open-circuit risk respectively, ensuring the safety of the equipment regulation process. Simultaneously, the module links with an external CO concentration sensor to receive and analyze measured concentration data. Combined with a fuzzy logic correction algorithm, it completes closed-loop correction of the matching coefficient. Furthermore, it can perform load change trend analysis based on historical load time-series data, generating advanced pre-regulation commands and lagging regulation commands. This comprehensively improves the system's control logic, ensuring the accuracy, stability, safety, and forward-looking nature of the entire matching system, fully supporting the precise ozone purification control requirements of dynamic CO loads in downhole environments.

[0090] 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 equivalent to the scope of this invention are included in this invention.

Claims

1. A method for matching the ozone generator yield with the CO load from underground vehicle exhaust, characterized in that, Includes the following steps: Obtain the current operating condition data and current roadway environment data of the underground transport vehicle, and predict the exhaust CO emission load at the current moment based on the current operating condition data and the current roadway environment data to obtain the predicted CO load value; Obtain the current operating parameters of the ozone generator, calculate the current actual output of the ozone generator based on the current operating parameters, and obtain the ozone output monitoring value; The ratio of the predicted CO load value to the monitored ozone production value is calculated to obtain the production-load matching coefficient, and the production adjustment direction of the ozone generator is determined according to the production-load matching coefficient and the preset target matching coefficient range. Based on the yield adjustment direction, a yield adjustment control command is generated for the ozone generator. The yield adjustment control command is used to adjust at least one operating parameter of the ozone generator so that the ozone yield of the ozone generator changes in a direction that matches the predicted CO load value.

2. The method for matching ozone generator yield with downhole vehicle exhaust CO load according to claim 1, characterized in that, The steps of acquiring the current operating condition data and current roadway environment data of the underground transport vehicle, and predicting the exhaust CO emission load at the current moment based on the current operating condition data and current roadway environment data to obtain the predicted CO load value specifically include: The engine speed signal, engine load rate signal and instantaneous fuel consumption signal are read in real time from the on-board diagnostic terminal of each transport vehicle to form the current operating condition data of each vehicle. The current cross-sectional average wind speed data and tunnel ambient temperature data are read from the wind speed sensors and temperature sensors installed in the tunnel, and used as the current tunnel environment data. The effective output power of each car's engine is obtained by multiplying the engine speed of each car by the engine load rate. Then, based on the engine displacement of each car, the effective output power of the engine is converted into the theoretical CO emission per unit time. The theoretical CO emission of all cars is added together to obtain the initial total CO emission. Using the current cross-sectional average wind speed data as a dilution factor and the current tunnel ambient temperature data as a reaction rate correction factor, the initial total CO emissions are divided by the dilution factor and then multiplied by the reaction rate correction factor to obtain the predicted CO load value.

3. The method for matching the ozone generator yield with the CO load from downhole vehicle exhaust as described in claim 1, characterized in that, The step of obtaining the current operating parameters of the ozone generator, calculating the current actual ozone output based on the current operating parameters, and obtaining the ozone output monitoring value specifically includes: The current effective value of discharge voltage, the current effective value of discharge current, and the current power supply frequency are read from the power controller of the ozone generator. At the same time, the current inlet temperature and outlet temperature of the cooling medium are read from the cooling system of the ozone generator to obtain the current set of operating parameters. Multiply the current effective value of the discharge voltage by the current effective value of the discharge current, and then multiply by the power factor to obtain the current input power. Based on the difference between the current inlet temperature and the current outlet temperature of the cooling medium, combined with the specific heat capacity and flow rate of the cooling medium, the heat removed from the ozone generator at the current moment is calculated to obtain the heat dissipation power value. Subtracting the heat dissipation power value from the current input power yields the effective power for ozone synthesis. Multiplying the effective power by the pre-stored ozone generator electro-ozone conversion efficiency coefficient yields the current actual yield, which is used as the ozone yield monitoring value.

4. The method for matching ozone generator yield with downhole vehicle exhaust CO load according to claim 1, characterized in that, The steps of calculating the ratio between the predicted CO load value and the monitored ozone yield value to obtain the yield-load matching coefficient, and determining the yield adjustment direction of the ozone generator based on the yield-load matching coefficient and the preset target matching coefficient range, specifically include: The ozone production rate monitoring value is used as the divisor, and the CO load prediction value is used as the divisor. The ratio of the ozone production rate monitoring value to the CO load prediction value is calculated to obtain the production rate-load matching coefficient. Read the pre-configured target matching coefficient range containing the minimum target matching coefficient and the maximum target matching coefficient from the local memory, and compare the yield-load matching coefficient with the minimum target matching coefficient and the maximum target matching coefficient; If the yield-load matching coefficient is less than the minimum target matching coefficient, it is determined that the current ozone yield is low relative to the CO load, and the yield adjustment direction is determined to be upward. If the yield-load matching coefficient is greater than the maximum target matching coefficient, it is determined that the current ozone yield is too high relative to the CO load, and the yield adjustment direction is determined to be downward. If the yield-load matching coefficient is between the minimum target matching coefficient and the maximum target matching coefficient, then the current ozone yield is determined to be matched with the CO load, and the yield adjustment direction is determined to be the maintenance direction.

5. The method for matching the ozone generator yield with the CO load from downhole vehicle exhaust as described in claim 4, characterized in that, The step of generating a rate adjustment control command for the ozone generator based on the rate adjustment direction, wherein the rate adjustment control command is used to adjust at least one operating parameter of the ozone generator so that the ozone rate of the ozone generator changes in a direction that matches the predicted CO load value, specifically includes: When the yield adjustment direction is upward, calculate the first difference between the yield-load matching coefficient and the minimum target matching coefficient, and query the pre-stored yield adjustment amount mapping table based on the first difference to obtain the target yield value to be increased. The target yield value is converted into a discharge voltage increase command and a discharge frequency increase command. The discharge voltage increase command and the discharge frequency increase command are combined into an upward adjustment control command. The discharge voltage and discharge frequency of the ozone generator are controlled to increase synchronously through the upward adjustment control command. When the yield adjustment direction is downward, calculate the second difference between the yield-load matching coefficient and the maximum target matching coefficient, and query the yield adjustment amount mapping table according to the second difference to obtain the target yield value to be reduced. The target yield value is converted into a discharge voltage reduction command and a discharge frequency reduction command. The discharge voltage reduction command and the discharge frequency reduction command are combined into a downward adjustment control command. When the yield adjustment direction is the maintenance direction, an empty command to maintain the current operating parameters is generated.

6. The method for matching the ozone generator yield with the CO load from downhole vehicle exhaust as described in claim 5, characterized in that, It also includes a discharge stability monitoring and protection step during the yield adjustment process, specifically including: after generating the yield adjustment control command and sending it to the ozone generator, continuously collecting the instantaneous values ​​of the discharge current and discharge voltage in the discharge chamber of the ozone generator, calculating the phase difference between the instantaneous values ​​of the discharge current and discharge voltage, and obtaining the real-time discharge phase angle; The equivalent discharge impedance value under the current discharge state is calculated based on the real-time discharge phase angle, and the equivalent discharge impedance value is compared with the pre-stored normal discharge impedance range. If the equivalent discharge impedance value is lower than the minimum value of the normal discharge impedance range, it is determined that there is a risk of micro-arc discharge in the current discharge state. The execution of the yield adjustment control command is immediately suspended, and a discharge abnormality protection command is generated. The discharge abnormality protection command controls the ozone generator to reduce the discharge voltage to a safe value. If the equivalent discharge impedance value is higher than the maximum value of the normal discharge impedance range, it is determined that there is a risk of open circuit in the current discharge state, an alarm signal is generated and the yield adjustment is stopped, and the current operating parameters are maintained until the abnormality is eliminated.

7. The method for matching the ozone generator yield with the CO load from downhole vehicle exhaust as described in claim 1, characterized in that, It also includes a yield-load matching closed-loop correction step based on the measured CO concentration value, specifically including: within a preset correction time window after the yield adjustment control command is generated and sent to the ozone generator, continuously collecting the measured CO concentration value output by the CO concentration sensor deployed in the roadway to obtain the measured CO concentration time series. The peak CO concentration and the stable final CO concentration are extracted from the measured CO concentration time series. The peak CO concentration and the stable final CO concentration are compared with the pre-stored target peak CO concentration and target final CO concentration, respectively, to obtain the peak concentration deviation and the final concentration deviation. The concentration peak deviation and the concentration final deviation are input into the fuzzy logic corrector, which outputs the yield-load matching coefficient correction amount according to the preset fuzzy rule base. The correction amount of the yield-load matching coefficient is superimposed on the current yield-load matching coefficient to obtain the corrected yield-load matching coefficient. The corrected yield-load matching coefficient is used to replace the current yield-load matching coefficient to redetermine the yield adjustment direction, and the redetermined yield adjustment direction is used as the basis for the next yield adjustment.

8. The method for matching the ozone generator yield with the CO load from downhole vehicle exhaust as described in claim 1, characterized in that, It also includes a pre-regulation step that synchronizes ozone production and CO load changes, specifically including: CO load prediction values ​​are continuously recorded at multiple past sampling times to form a historical time series of CO load. Linear regression analysis is performed on the historical time series of CO load to calculate the rate of change of CO load at the current time and obtain the slope of the CO load change trend. The slope of the CO load change trend is compared with a preset slope threshold. If the slope of the CO load change trend exceeds the positive trend threshold, it is determined that the CO load is in a rapid upward trend. Based on the rate adjustment direction determined according to the rate-load matching coefficient at the current moment, an advance adjustment increment is added, and an advance adjustment control command containing the advance adjustment increment is generated. The advance adjustment control command controls the ozone generator to increase the rate in advance at the current moment. If the slope of the CO load change trend is lower than the negative trend threshold, it is determined that the CO load is in a rapid downward trend. Based on the yield adjustment direction determined according to the yield-load matching coefficient at the current moment, a hysteresis adjustment reduction is reduced, and a hysteresis adjustment control command containing the hysteresis adjustment reduction is generated. The hysteresis adjustment control command controls the ozone generator to reduce the yield in advance at the current moment.

9. The method for matching the ozone generator yield with the CO load from underground vehicle exhaust as described in claim 7, characterized in that, The step of inputting the concentration peak deviation and the concentration final deviation into the fuzzy logic corrector, and the fuzzy logic corrector outputting the yield-load matching coefficient correction amount according to the preset fuzzy rule base, specifically includes: The concentration peak deviation is mapped to the first fuzzy domain according to its numerical value, and the concentration peak deviation is converted into low-level membership degree, medium-level membership degree and high-level membership degree of peak deviation through the first membership degree function to obtain the peak deviation fuzzification vector. The concentration final value deviation is mapped to the second fuzzy domain according to its numerical value. The concentration final value deviation is converted into a low-level membership degree, a medium-level membership degree, and a high-level membership degree of the final value deviation through the second membership degree function, and the fuzzy vector of the final value deviation is obtained. Each membership value in the peak deviation fuzzification vector is paired with each membership value in the final value deviation fuzzification vector to obtain nine sets of deviation combination states; For each set of deviation combination states, the output fuzzy subset corresponding to the deviation combination state is queried in the preset fuzzy rule library to obtain nine sets of output membership values. The fuzzy rule library contains nine fuzzy rules, and each fuzzy rule defines the output fuzzy level of the yield-load matching coefficient correction under a deviation combination state. The nine sets of output membership values ​​are defuzzified using the centroid method. The weighted average of the correction values ​​corresponding to all output membership values ​​is calculated, and the result of the weighted average is output as the correction amount of the yield-load matching coefficient.

10. An ozone generator yield matching system for matching the CO load of underground vehicle exhaust, used to implement the method for matching the ozone generator yield with the CO load of underground vehicle exhaust as described in any one of claims 1 to 9, characterized in that, include: The module includes a CO load prediction module, an ozone yield monitoring module, a matching coefficient calculation and adjustment direction determination module, and a yield adjustment and control module. The CO load prediction module is configured to acquire the current operating condition data of the underground transport vehicle and the current roadway environment data, predict the exhaust CO emission load at the current moment based on the current operating condition data and the current roadway environment data, obtain the CO load prediction value, and send the CO load prediction value to the matching coefficient calculation and adjustment direction determination module. The ozone production rate monitoring module is configured to acquire the current operating parameters of the ozone generator, calculate the current actual production rate of the ozone generator based on the current operating parameters, obtain the ozone production rate monitoring value, and send the ozone production rate monitoring value to the matching coefficient calculation and adjustment direction determination module. The matching coefficient calculation and adjustment direction determination module is configured to receive the CO load prediction value and the ozone production rate monitoring value, perform a ratio calculation between the CO load prediction value and the ozone production rate monitoring value to obtain the production rate-load matching coefficient, determine the production rate adjustment direction of the ozone generator based on the production rate-load matching coefficient and the preset target matching coefficient range, and send the production rate adjustment direction to the production rate adjustment control module. The yield adjustment control module is configured to receive the yield adjustment direction and generate a yield adjustment control command for the ozone generator based on the yield adjustment direction. The yield adjustment control command is used to adjust at least one operating parameter of the ozone generator so that the ozone yield of the ozone generator changes in a direction that matches the predicted CO load value.

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