Intelligent control system for efficient ozone generation

Abstract

The present application relates to the technical field of intelligent control, in particular to a kind of intelligent control system of high-efficiency ozone generation, comprising the following steps: drive distribution module, gas ratio control module, state identification module, leakage discrimination module, feedback regulation module.In the present application, through the linkage judgment of real-time fluctuation trend of temperature and humidity and response priority order, parameter dynamic sequencing and timely identification of leading factor are realized, combined with dry and wet gas ratio control in ozone reaction efficiency interval, and hierarchical analysis of current curve and discharge stability characteristics in discharge cycle, multi-condition risk discrimination of ozone concentration and airflow change synchronization relationship is introduced, and multi-cycle concentration parameters and key operating parameters are used for progressive comparison of screening and rhythm configuration of stable section, forming automatic adjustment strategy based on multi-source working condition participation item, constructing multi-dimensional condition collaborative identification, dynamic adjustment and real-time feedback cycle closed-loop control structure, realizing adaptive optimization of gas path, discharge and adjustment action.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, in particular to an intelligent control system for efficient ozone generation. BACKGROUND

[0002] The technical field of intelligent control includes the automatic management and adjustment of equipment, systems or processes. Its core content is to collect external environmental or system state data based on sensors, make logical judgments with the help of controllers, and issue control instructions to achieve adaptive maintenance or optimized adjustment of system target parameters. It covers embedded systems, programmable logic controllers, industrial buses, control strategy modeling, actuator control, communication interface management, and other content. It is applied to industrial automation, environmental governance, energy management, intelligent manufacturing and other scenarios. With the improvement of microprocessor performance and the enhancement of sensing and perception capabilities, intelligent control technology presents the characteristics of highly integrated, refined and networked systems. The intelligent control system for efficient ozone generation refers to an intelligent adjustment technology for fine control of the ozone generation process under specific environmental conditions. To address the issues of unstable output and reduced reaction efficiency of ozone generating devices under different temperature and humidity conditions, a control logic is constructed using a temperature and humidity composite sensing unit, an ozone discharge control circuit, a microprocessor judgment unit and a feedback control actuator. Specifically, a digital temperature sensor and a capacitive humidity sensor are used to monitor the discharge cavity and the air inlet path in real time. The detection data is transmitted to the microcontroller. The current temperature and humidity state is compared with the built-in control rule table to determine whether it deviates from the set working range. The air preheating equipment, drying device and condensation dehumidification unit are adjusted in linkage to dynamically control the air inlet conditions. The ozone discharge gap and frequency are adjusted according to the environmental parameters. The discharge intensity is adjusted by controlling the PWM signal output to ensure the discharge efficiency and reaction stability under complex environments.

[0003] Traditional ozone generation control technology relies on single rule judgment of temperature and humidity monitoring and discharge adjustment, and cannot respond in real time according to fluctuations in air flow, concentration and other parameters. When the environmental change amplitude increases or the system is repeatedly disturbed, it is limited to trigger control of single parameter overrun, making it difficult for the timing and amplitude of the adjustment action to adapt to the actual needs of the working conditions. In the presence of complex scenarios such as air backflow, leakage and fluctuations, it cannot accurately distinguish between various abnormalities, resulting in misadjustment, response lag, unstable operation and other situations, affecting the consistency of ozone output and the long-term reliability of the device. SUMMARY

[0004] To solve the technical problems existing in the prior art, the embodiments of the present application provide an intelligent control system for efficient ozone generation. The technical solution is as follows:

[0005] On the one hand, an intelligent control system for efficient ozone generation is provided, which comprises:

[0006] The driving distribution module calls the temperature and humidity sensor to extract the temperature change value and humidity change value in real time, analyzes the temperature and humidity change trend and fluctuation rate difference, adjusts the control frequency and response order of the preheating and dehumidification units, and establishes a temperature and humidity linkage control configuration;

[0007] The gas ratio control module calls the temperature and humidity linkage control configuration, obtains the electrode input signal and concentration sampling result in the ozone reaction cavity, judges the reaction efficiency state, adjusts the dry and wet gas supply rhythm, and establishes a dry and wet gas flow dynamic matching interval;

[0008] The state recognition module calls the dry and wet gas flow dynamic matching interval, analyzes the continuity change form of the current curve stable section in the discharge period, judges the discharge stability decline feature and electrode response delay state, recognizes the discharge imbalance grade, adjusts the discharge control configuration, and generates a discharge compensation adjustment parameter;

[0009] The leakage discrimination module calls the discharge compensation adjustment parameter, analyzes the ozone concentration change trajectory and gas flow direction change trend in the current period, evaluates the synchronization offset feature, identifies the leakage risk and sends a response control instruction, and generates a leakage protection execution record.

[0010] As a further scheme of the present application, the temperature and humidity linkage control configuration includes temperature response priority, humidity control rhythm, and linkage adjustment parameter, the dry and wet gas flow dynamic matching interval includes dry gas matching parameter, wet gas matching parameter, and gas flow adjustment timing, the discharge compensation adjustment parameter specifically is spacing adjustment instruction, frequency compensation configuration, and wear grade judgment basis, and the leakage protection execution record includes cut-off action signal, alarm record, and risk grade identification.

[0011] As a further scheme of the present application, the driving distribution module includes:

[0012] The temperature and humidity change trend submodule calls the temperature and humidity sensor to obtain the temperature change value and humidity change value in real time, compares the temperature change direction and humidity change direction in the current period, calculates the absolute difference of the change amplitudes of the two, and establishes temperature and humidity change trend data;

[0013] The fluctuation rate analysis submodule analyzes the temperature change rate and humidity change rate in the same time period based on the temperature and humidity change trend data, calculates the rate difference, and generates a temperature and humidity fluctuation rate difference index;

[0014] The priority order adjustment submodule calls the temperature and humidity fluctuation rate difference index, adjusts the control frequency distribution and response order of the preheating unit and dehumidification unit, optimizes the control configuration of the corresponding execution unit, and generates a temperature and humidity linkage control configuration.

[0015] As a further scheme of the present application, the specific formula for calculating the rate difference is:

[0016] ;

[0017] wherein, is a temperature and humidity fluctuation rate difference index, is a normalized value of the temperature change rate in the first is a normalized value of the temperature change rate in the first is a normalized value of the humidity change rate in the first is a normalized value of the humidity change rate in the first is an index number of a discrete time period in a monitoring period, is the total number of discrete time periods in a monitoring period.

[0018] As a further scheme of the present application, the gas ratio control module comprises:

[0019] The reaction data acquisition submodule acquires the temperature and humidity linkage control configuration, monitors the electrode input voltage, current signal and outlet ozone concentration in the ozone reaction cavity, and establishes a reaction process parameter set in combination with time data;

[0020] The real-time efficiency evaluation submodule analyzes the corresponding relationship between the electrode input signal and the ozone concentration change based on the reaction process parameter set, evaluates and judges the running state interval of the reaction efficiency, and generates a reaction efficiency change interval;

[0021] The adjustment rhythm adjustment submodule compares the change trajectory of the dry gas flow rate with the change sequence of the opening and closing rhythm of the wet gas valve according to the reaction efficiency change interval, adjusts the control cycle length of the dry and wet gas supply channels and the interval section of the valve drive, and establishes a dry and wet gas flow dynamic matching interval.

[0022] As a further scheme of the present application, the state recognition module comprises:

[0023] The discharge behavior analysis submodule acquires the dry and wet gas flow dynamic matching interval, analyzes the current curve in the discharge period, judges the continuity feature of the stable section in the current curve, counts the duration and fluctuation rhythm of the stable section, and generates current stable feature data;

[0024] The electrode wear judgment submodule calculates the proportional relationship of the voltage rising section and the voltage falling section in the period during the discharge based on the current stable feature data, judges the discharge stability decline feature, identifies the delay performance of the electrode response, and obtains discharge abnormal feature parameters;

[0025] The control compensation adjustment submodule judges the discharge imbalance level, evaluates the electrode wear state, adjusts the control configuration of the discharge process, and generates discharge compensation adjustment parameters according to the discharge abnormal feature parameters.

[0026] As a further scheme of the present application, the electrode wear state is evaluated by using the formula:

[0027] ;

[0028] The electrode wear degree index is calculated, and the control configuration of the discharging process is adjusted;

[0029] wherein, is the electrode wear degree index, is the normalized value of the average value of the voltage rising section, is the normalized value of the average value of the voltage falling section, is the normalized value of the average value of the current rising section, is the normalized value of the average value of the current falling section, is the normalized value of the mean square deviation of the current.

[0030] As a further scheme of the present application, the leakage discrimination module comprises:

[0031] The real-time synchronous analysis submodule calls the discharging compensation adjustment parameter, analyzes the continuous change trajectory of the ozone concentration and the change trend of the airflow direction in the current period, screens the offset period in which the airflow propagation direction is synchronized with the ozone release change, and generates concentration airflow synchronous data;

[0032] The leakage risk identification submodule compares the ozone concentration growth amplitude of the airflow flow rate in the falling stage according to the concentration airflow synchronous data, judges the correlation between the airflow change and the ozone release, evaluates the leakage risk, and generates leakage risk judgment data;

[0033] The control response output submodule sends a response control instruction based on the leakage risk judgment data, including outputting a gas cutoff and alarm control instruction, and generates a leakage protection execution record.

[0034] As a further scheme of the present application, the system further comprises:

[0035] The feedback adjustment module analyzes the ozone concentration characteristics in the monitoring period according to the leakage protection execution record, extracts stable section parameters, and compares them with the current period parameters to identify the current period running state, adjust the dry gas response rhythm and the wet gas channel trigger time configuration, and generate a running parameter correction result;

[0036] The running parameter correction result specifically refers to the parameter correction amplitude, the channel configuration state, and the state identification result.

[0037] As a further scheme of the present application, the feedback adjustment module comprises:

[0038] The stable section extraction submodule obtains the leakage protection execution record, analyzes the ozone concentration change curve in the continuous monitoring period, screens out the stable operation period according to the fluctuation amplitude, and establishes the operation stable section data;

[0039] The parameter combination construction submodule extracts the temperature, humidity, voltage, discharge frequency and inlet air flow rate in the target period based on the operation stable section data, combines each parameter to form a comparison parameter group, and obtains a stable parameter combination;

[0040] The state recognition and adjustment submodule calls the stable parameter combination, compares the numerical difference between the parameter combination and the parameter group corresponding to the current period, recognizes the current period operation state, adjusts the dry gas response rhythm and the wet gas channel trigger time, and generates an operation parameter correction result.

[0041] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0042] Through the linkage judgment of the real-time fluctuation trend of temperature and humidity and the response priority order, the parameter dynamic sorting and the timely identification of the dominant factor are realized, the dry and wet gas ratio control in the ozone reaction efficiency interval is combined, the hierarchical analysis of the current curve and the discharge stability characteristics in the discharge period is combined, the multi-condition risk discrimination of the synchronization relationship between the ozone concentration and the air flow change is introduced, the multi-period concentration parameters and the key operation parameters are used for progressive comparison of the screening and rhythm configuration of the stable section, the automatic adjustment strategy based on the multi-source working condition participation item is formed, the multi-dimensional condition cooperative identification, dynamic adjustment and real-time feedback cycle closed-loop control structure is constructed, and the adaptive optimization of the air path, discharge and adjustment action is realized. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical scheme in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0044] Figure 1 The system flowchart of the application;

[0045] Figure 2 The system framework schematic diagram of the application;

[0046] Figure 3 The driving distribution module flowchart of the application;

[0047] Figure 4 The air ratio control module flowchart of the application;

[0048] Figure 5 The state recognition module flowchart of the application;

[0049] Figure 6 Flow chart of the leakage discrimination module of the present application;

[0050] Figure 7 Flow chart of the feedback adjustment module of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the present application will be described below in conjunction with the drawings.

[0052] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0053] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0054] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0055] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0056] The embodiments of the present application provide an intelligent control system for efficient ozone generation, please refer to Figures 1 to 2 The present application provides a technical solution, an intelligent control system for efficient ozone generation, comprising:

[0057] The driving distribution module calls the temperature and humidity sensor, extracts the temperature change value and humidity change value in real time, analyzes the temperature and humidity change trend and fluctuation rate difference, adjusts the control frequency and response order of the preheating and dehumidification unit, and establishes a temperature and humidity linkage control configuration;

[0058] The gas ratio control module calls the temperature and humidity linkage control configuration, obtains the electrode input signal and concentration sampling result in the ozone reaction cavity, judges the reaction efficiency state, adjusts the dry and wet gas supply rhythm, and establishes a dry and wet gas flow dynamic matching interval;

[0059] The status recognition module calls the dynamic ratio range of dry and wet airflow, analyzes the continuous change pattern of the current curve in the stable segment during the discharge cycle, judges the characteristics of discharge stability decline and electrode response delay, identifies the discharge imbalance level, adjusts the discharge control configuration, and generates discharge compensation adjustment parameters.

[0060] The leakage detection module calls the discharge compensation adjustment parameters, analyzes the ozone concentration change trajectory and airflow direction change trend in the current cycle, assesses the synchronization offset characteristics, identifies leakage risks, sends response control commands, and generates leakage protection execution records.

[0061] Based on the leakage protection execution record, the feedback adjustment module analyzes the ozone concentration characteristics within the monitoring cycle, extracts the parameters of the stable section, compares them with the parameters of the current cycle, identifies the current cycle operating status, adjusts the dry gas response rhythm and the wet gas channel trigger time configuration, and generates operating parameter correction results.

[0062] The temperature and humidity linkage control configuration includes temperature response priority, humidity control rhythm, and linkage adjustment parameters. The dynamic ratio range of dry and wet airflow includes dry air ratio parameters, wet air ratio parameters, and airflow adjustment sequence. The discharge compensation adjustment parameters specifically include spacing adjustment commands, frequency compensation configuration, and wear level judgment criteria. The leakage protection execution record includes cut-off action signals, alarm records, and risk level identification. The operating parameter correction results specifically refer to parameter correction magnitude, channel configuration status, and status identification results.

[0063] Please see Figure 2 and Figure 3 The driver allocation module includes:

[0064] The temperature and humidity change trend submodule calls the temperature and humidity sensor to obtain the temperature change value and humidity change value in real time, compares the direction of temperature change and humidity change within the current cycle, calculates the absolute difference between the two changes, and establishes temperature and humidity change trend data.

[0065] The temperature and humidity change trend submodule acquires temperature and humidity change values ​​collected by temperature and humidity sensors. Using real-time temperature and humidity data, it first analyzes the direction of temperature change within the current period and compares this direction with the direction of humidity change. If both temperature and humidity show an upward trend, it indicates that the environmental heat and humidity conditions may be increasing, requiring calculation of the temperature and humidity change rates. The calculation method is as follows: first, the ratio of continuous temperature measurements to time is used to calculate the temperature change rate per unit time; similarly, the humidity change rate is calculated using the ratio of continuous humidity measurements to time. Next, the absolute difference in the magnitude of the two changes is calculated to obtain the temperature and humidity fluctuation rate difference index. In an example, if the temperature change rate is 0.5°C / min and the humidity change rate is 0.3%RH / min within 10 minutes, the temperature and humidity fluctuation rate difference is |0.5 - 0.3| = 0.2 (degrees / minute). Based on this index, the control strategies of preheating and dehumidification equipment in the environment can be adjusted in real time, thereby creating temperature and humidity change trend data.

[0066] The fluctuation rate analysis submodule analyzes the rate of temperature change and the rate of humidity change within the same time period based on temperature and humidity change trend data, calculates the rate difference, and generates a temperature and humidity fluctuation rate difference index.

[0067] The specific formula for calculating the rate difference is:

[0068] ;

[0069] in, This is an index of the difference in the rate of temperature and humidity fluctuations. For the first The normalized value of the rate of temperature change over a given time period relative to the reference maximum rate of temperature change. For the first The normalized value of the rate of humidity change over a given time period relative to a reference maximum rate of humidity change. This refers to the index number of discrete time periods within the monitoring period. This refers to the total number of discrete time periods within the monitoring period.

[0070] In the volatility rate analysis submodule, the volatility rate difference index is calculated using the following formula: ;in, This is an index of the difference in temperature and humidity fluctuation rates, representing the relative deviation between the rates of temperature change and humidity change within the same time period. Indicates the first Normalized values ​​of the rate of temperature change at each time point Indicates the first Normalized values ​​of humidity change rate at each time point. The denominator is enclosed in square roots. The product factor is used for intensity weighting, ensuring the denominator changes synchronously with the rate intensity, reflecting the balance constraint brought by the product, and amplifying the effect of the synchronicity of the intensity changes of the two factors. The numerator represents the absolute deviation between the temperature rate and the humidity rate, describing the magnitude of their difference in trend. The overall formula, combining absolute values, multiplication, and square roots, reflects the multi-faceted weighting of fluctuation differences by multiple factors. This is to obtain the parameters required for calculation. and The data needs to be collected and processed as follows: Collect temperature and humidity change data for the continuous monitoring period, recording once every second, for a total collection time of 30 seconds. Calculate the difference between the first second and the previous second, and then normalize it as follows: , ,in, These are the temperature values ​​collected at the current time and the previous time, respectively, in °C; The values ​​represent the corresponding humidity levels, in %RH. For example, the values ​​collected at the 10th second are as follows: , , , The maximum and minimum values ​​in the intervals are: maximum temperature 24.3, minimum temperature 22.7; maximum humidity 60.1, minimum humidity 55.3. After normalization, we get: ; Substitute into the main formula to perform the calculation:

[0071] ;

[0072] The results show that the difference between the temperature rate and the humidity rate in the current cycle is 0.0776, indicating that there is a certain deviation between their fluctuation trends. This indicator can be used as a quantitative standard for the difference in fluctuation rates, and can be further used in the subsequent priority adjustment module to affect the setting of preheating and dehumidification response strategies.

[0073] Table 1. Sampling data on temperature and humidity changes

[0074] ;

[0075] Table 1 lists the sampled data of temperature and humidity changes at the 9th and 10th seconds, used to calculate the normalized rate value at the 10th second. This dataset was collected using a standard temperature and humidity sensor with an update frequency of 1Hz, meeting the real-time requirements of the monitoring cycle. The normalized rate value, calculated above, is substituted into the formula, and the resulting value of 0.0776 can serve as a reference for the temperature and humidity fluctuation rate difference index during this time period. The formula introduces a square root weighted form by multiplying the temperature and humidity rates, automatically weakening the weight when the rate difference is large and enhancing the sensitivity when the synchronicity difference is significant, effectively improving the response capability to nonlinear offset trend identification; and it can output continuous quantitative indicators to support the selection of dynamic control strategies.

[0076] Priority adjustment of sub-module calls to temperature and humidity fluctuation rate difference index, adjustment of control frequency allocation and response order of preheating unit and dehumidification unit, optimization of control configuration of corresponding execution unit, and generation of temperature and humidity linkage control configuration;

[0077] The priority adjustment submodule calls the temperature and humidity fluctuation rate difference index and adjusts the response order and frequency of the preheating and dehumidification units according to this index and the environmental changes within the current cycle. If the temperature and humidity difference is large, the response priority of the preheating unit is increased, and vice versa. Assuming the temperature and humidity fluctuation rate difference is 0.4 (degrees / minute), the control frequency of the preheating unit increases relatively, while the response cycle of the dehumidification unit decreases. By dynamically controlling the coordination of the preheating and dehumidification equipment, the equipment can intelligently adjust according to real-time environmental conditions, thereby optimizing the entire control process and generating a temperature and humidity linkage control configuration. For example, when the temperature and humidity fluctuation rate difference is greater than 0.3, the preheating unit accounts for 70% of the control frequency, and the dehumidification unit accounts for 30%.

[0078] Please see Figure 2 and Figure 4 The gas ratio control module includes:

[0079] The reaction data acquisition submodule acquires the temperature and humidity linkage control configuration, monitors the electrode input voltage, current signal and outlet ozone concentration in the ozone reaction chamber, and establishes a set of reaction process parameters by combining time data.

[0080] The reaction data acquisition submodule obtains the temperature and humidity linkage control configuration. Based on real-time monitored temperature and humidity data, it acquires electrode input voltage, current signal, and outlet ozone concentration data within the reaction chamber through digital temperature sensors and capacitive humidity sensors, and combines this with time data. These data change in real time with the reaction process. Therefore, by correlating the temperature and humidity values, input voltage, current, and concentration values ​​at each data sampling time point, a set of reaction process parameters is established. For example, within 5 minutes, if the temperature changes by 0.2°C, the humidity changes by 1%RH, the voltage signal is 500V, the current signal is 0.8A, and the concentration changes by 0.5 ppm, then the real-time data at each moment will be collected and correlated based on this to generate a set of reaction process parameters. These data can accurately reflect the ozone generation status under different environmental conditions within the reaction chamber.

[0081] The real-time efficiency assessment submodule analyzes the correspondence between electrode input signals and ozone concentration changes based on the set of reaction process parameters, assesses and determines the operating state range of reaction efficiency, and generates the reaction efficiency change range.

[0082] The real-time efficiency assessment submodule, based on a set of reaction process parameters, analyzes the relationship between electrode input voltage, current signals, and ozone concentration changes. First, it obtains the curves showing the changes in electrode input signals and concentration. Then, it calculates the correlation between current and concentration, analyzing the impact of current changes on concentration changes. In particular, it assesses reaction efficiency by evaluating the synchronicity between current signal change segments and concentration change segments. For example, it assumes that when the current signal reaches 1A, the ozone concentration increases by 0.2 ppm, and conversely, when the current signal decreases, the ozone concentration decreases accordingly. Based on these data, it calculates and assesses the operating state range of reaction efficiency within different time segments, thus generating the reaction efficiency variation range. For instance, in actual operation, if the current signal is stable at 0.8A and the concentration is stable at 0.5ppm, this range is assessed as a low-efficiency operating segment; if the current signal increases to 1.2A and the concentration increases to 1.0ppm, it is assessed as a high-efficiency operating segment.

[0083] The rhythm adjustment submodule compares the change trajectory of the dry gas flow rate with the change sequence of the opening and closing rhythm of the wet gas valve based on the change range of reaction efficiency, adjusts the control cycle length of the dry and wet gas supply channels and the interval of valve drive, and establishes a dynamic ratio range of dry and wet gas flow.

[0084] The rhythm adjustment submodule compares the changes in dry gas flow rate and the opening / closing rhythm of the wet gas valve based on the reaction efficiency variation range. It analyzes the dry gas flow rate and the opening / closing state of the wet gas valve using real-time monitoring data and compares their trajectories. If, within a high reaction efficiency range, the dry gas flow rate increases rapidly while the wet gas valve's opening / closing rhythm lags behind, the wet gas flow rate adjustment needs to be optimized by increasing the response frequency of the wet gas valve. This ensures that the ratio of wet to dry gas meets the optimal conditions for ozone generation. For example, at high reaction efficiency, the dry gas flow rate changes by 0.5 m / s, and the wet gas valve responds once every 5 minutes. As efficiency increases, the response frequency of the wet gas valve increases to once every 2 minutes. By adjusting the control cycle length of the dry and wet gas flows and the valve drive interval parameters, a dynamic dry and wet gas flow ratio range is ultimately generated. This ratio range ensures the matching of gas flow with reaction conditions, improving reaction efficiency.

[0085] Please see Figure 2 and Figure 5 The status recognition module includes:

[0086] The discharge behavior analysis submodule obtains the dynamic ratio range of dry and wet airflow, analyzes the current curve within the discharge cycle, determines the continuity characteristics of the stable section in the current curve, counts the duration and fluctuation rhythm of the stable section, and generates current stability characteristic data.

[0087] The reaction data acquisition submodule, based on real-time temperature and humidity data, and using temperature and humidity changes collected by temperature and humidity sensors, establishes a set of reaction process parameters, taking into account the monitoring results of electrode input voltage, current signal, and outlet ozone concentration, combined with the real-time sampling time. Specifically, within each reaction cycle, real-time data acquired by temperature and humidity sensors, such as temperature at 0.3°C and humidity at 1%RH per minute, combined with real-time values ​​of electrode input voltage (e.g., 500V), current signal (e.g., 0.8A), and ozone concentration changes at 0.5ppm, facilitates the creation of a specific parameter set at different time points. This dataset not only reflects the combined effect of temperature and humidity within the reaction chamber and the electrode reaction but also allows for adjustments to the temperature and humidity control strategy during the reaction process based on actual operating conditions, thereby optimizing the stability and efficiency of the ozone generation process.

[0088] The electrode wear judgment submodule calculates the ratio of voltage rise and fall during the discharge period based on current stability characteristic data, judges the characteristics of decreased discharge stability, identifies the delayed performance of electrode response, and obtains abnormal discharge characteristic parameters.

[0089] The real-time efficiency assessment submodule analyzes temperature, humidity, and current signals from the reaction process parameter set to determine the correlation between electrode input signals and ozone concentration changes. For example, by comparing the changes in current signal and ozone concentration in real time, if the concentration increases with the current value, the reaction efficiency is assessed, and the current reaction efficiency range is determined. If the concentration is stable at 0.5 ppm when the current value is 0.8 A, and the concentration increases to 0.8 ppm when the current value is increased to 1.2 A, then the reaction efficiency range can be determined to be "highly efficient." By comparing the trends of current and concentration changes over different time periods, combined with changes in temperature and humidity data, the reaction efficiency can be divided into inefficient, high-efficiency, and optimal operating ranges, and a reaction efficiency variation range can be generated accordingly to optimize the adjustment configuration of subsequent reaction processes.

[0090] The control compensation adjustment submodule determines the level of discharge imbalance, assesses the electrode wear status, adjusts the control configuration of the discharge process, and generates discharge compensation adjustment parameters based on the discharge abnormality characteristic parameters.

[0091] The formula for assessing electrode wear is as follows:

[0092] ;

[0093] Calculate the electrode wear index and adjust the control configuration of the discharge process;

[0094] in, This is an indicator of electrode wear. This is the normalized value of the average value during the voltage rise phase. This is the normalized value of the average value during the voltage drop segment. This is the normalized value of the average value during the rising phase of the current. This is the normalized value of the average value during the current decline segment. This is the normalized value of the mean square error of the current.

[0095] The formula used to assess electrode wear in the above content is: The process calculates electrode wear indicators, determines the discharge imbalance level, assesses electrode wear status, adjusts the control configuration of the discharge process, and generates discharge compensation adjustment parameters. Formula and parameter descriptions: The normalized average value during the voltage rise phase refers to the average of the recorded voltage values ​​as the voltage increases from its lowest point to its highest point, after normalization to ensure the data falls within the range of 0 to 1. The calculation formula is: ;in, This is the measured value of the voltage during the rising phase. This is the number of sampling points. This value describes the average response during the voltage rise phase. The normalized average value of the voltage drop segment is processed in the same way as the voltage rise segment. The average value of the voltage drop segment is calculated and normalized to represent the magnitude of the voltage drop. The normalized average value of the current rising segment is similar to that of voltage. It describes the current rise by averaging the current rising segment data and normalizing it to the range of 0 to 1. : The normalized average value of the current falling segment, calculated in the same way as the current rising segment, represents the magnitude of the current change during the falling segment. The normalized mean square error of the current is used to measure the intensity of current fluctuations. It is calculated as follows: ;in, Is the current in the first... The value of each measurement point, It is the average value of the current. This represents the number of sampling points. The formula involves absolute value operations, such as... This calculation measures the absolute difference between the rising and falling voltage segments, reflecting the voltage asymmetry between these segments. This method allows for a clearer capture of imbalances or asymmetries in voltage changes. Adding the two absolute differences in the formula aims to aggregate the amplitudes of voltage and current changes, reflecting the combined impact of these two physical quantities on electrode wear. This is achieved by introducing the mean square error of the current fluctuation. The formula normalizes the current variation using square root calculations, thereby adjusting the weighting factor to reasonably mitigate the impact of current fluctuations on the final result. By combining voltage and current variations with the amplitude of current fluctuations, the formula can assess electrode wear, providing a basis for subsequent control compensation adjustments. This comprehensive assessment effectively reflects the electrode's health status and provides guidance for further control.

[0096] Suppose that in a certain experiment, the measured values ​​of the voltage rise and fall segments are as follows (unit: volts): Voltage rise segment: Voltage drop segment: The measured values ​​for the rising and falling segments of the current are as follows (unit: amperes): Rising segment: Current descent segment: ;

[0097] Using this data, calculate the values ​​of each parameter:

[0098] After normalizing the average values ​​of the voltage rise and fall segments:

[0099] ;

[0100] ;

[0101] After normalization, the normalized value of the voltage change is:

[0102] ; ;

[0103] Calculation of the root mean square error of the current:

[0104] ;

[0105] Substituting these calculation results into the formula, the final electrode wear index is obtained. That is:

[0106] ;

[0107] By combining the mean square error of voltage and current changes with current fluctuations, the formula effectively quantifies the wear state of electrodes, particularly enhancing its sensitivity to discharge imbalances. The use of this composite index makes electrode wear detection more accurate, thus providing a more reliable basis for decision-making in subsequent electrode compensation and control systems.

[0108] Table 2 Experimental Data

[0109] ;

[0110] As shown in Table 2, the experimental data for the voltage rise segment, voltage fall segment, and current change segment were obtained through monitoring and real-time measurement, and were then used to calculate the various parameters in the above formula.

[0111] Please see Figure 2 and Figure 6 The leakage detection module includes:

[0112] The real-time synchronization analysis submodule calls the discharge compensation adjustment parameters to analyze the continuous change trajectory of ozone concentration and the change trend of airflow direction within the current cycle, filters the offset time period when the airflow propagation direction is synchronized with the ozone release change, and generates concentration airflow synchronization data.

[0113] The real-time synchronization analysis submodule calls the discharge compensation adjustment parameters and synchronously monitors the ozone concentration and airflow direction during the reaction process. First, it acquires the real-time ozone concentration change trajectory and compares it with the trend of airflow direction changes. Assuming that at a certain moment, the airflow direction changes from forward to reverse, the concentration changes accordingly. In actual operation, when the airflow velocity changes by 0.8 m / s, the ozone concentration changes by 0.3 ppm; conversely, when the airflow velocity drops to 0.5 m / s, the concentration drops to 0.1 ppm. This data is continuously collected and used to generate concentration-airflow synchronization data. By comparing the time relationship and trend between these two, the synchronization offset between airflow changes and ozone concentration can be identified. Further analysis, combined with timestamp data, can accurately determine the synchronicity of airflow changes and ozone concentration changes within the same period, thus obtaining concentration-airflow synchronization data. For example, if the concentration increases by 0.2 ppm when the airflow reverses direction within a certain interval, and then slightly decreases to 0.15 ppm when the airflow recovers, it can be considered that the changes in airflow and ozone release are synchronized during this period, generating accurate synchronization data.

[0114] The leakage risk identification submodule compares the ozone concentration increase during the decreasing phase of the airflow velocity based on the concentration airflow synchronous data, determines the correlation between airflow changes and ozone release, assesses leakage risk, and generates leakage risk assessment data.

[0115] The leakage risk identification submodule, based on synchronized airflow concentration data, calculates the correlation between ozone concentration and airflow velocity by analyzing the increase in ozone concentration during the decreasing phase of airflow velocity. For example, assuming that at one moment the airflow velocity decreases to 0.4 m / s and the ozone concentration increases by 0.1 ppm, and at another moment the airflow velocity decreases to 0.3 m / s and the concentration increases by 0.15 ppm, if these trends are consistent, it indicates a strong correlation between airflow changes and ozone release. Next, by calculating the increase in ozone concentration during the decreasing airflow velocity phase, a comprehensive assessment of the potential leakage risk during this phase can be conducted. Based on this, a risk assessment is performed and leakage risk judgment data is generated. According to the actual values, if the decrease in airflow velocity and the change in concentration fail to maintain the expected synchronization, or if the concentration increase is significantly greater than expected, a leakage risk may occur, and the system immediately enters an early warning state, triggering corresponding protective measures.

[0116] Based on the leakage risk assessment data, the control response output submodule sends response control commands, including output gas cut-off and alarm control commands, and generates leakage protection execution records.

[0117] The control response output submodule, based on leakage risk assessment data, compares the decrease in gas flow rate with changes in concentration to determine if a leakage risk has occurred. If a leakage risk is detected, the system sends response control commands, including gas shut-off control and alarm signal control. For example, when the leakage risk reaches a set threshold, the system sends a command to shut off the gas supply and issues an alarm notification. Through the control response output, the system can promptly shut off the gas source and activate the alarm mechanism upon detecting a leak. This operation effectively prevents potential equipment damage or environmental pollution, ensuring the safety of system operation. This process generates a leakage protection execution log, which is further used for subsequent maintenance and monitoring to ensure the long-term stable operation of the system.

[0118] Please see Figure 2 and Figure 7 The feedback adjustment module includes:

[0119] The stable section extraction submodule obtains leakage protection execution records, analyzes the ozone concentration change curve within the continuous monitoring period, filters out stable operating sections based on the fluctuation amplitude, and establishes stable operating section data.

[0120] The stable zone extraction submodule analyzes the ozone concentration change curve within a continuous monitoring period based on the leakage protection execution record. First, it obtains detailed data on ozone concentration changes over time. If the concentration fluctuation is small within a certain period, and the current, temperature, and humidity are relatively stable, then that period can be designated as a stable zone. By filtering these data based on fluctuation amplitude, assuming that within a certain monitoring period, the concentration change does not exceed 0.2 ppm, the temperature fluctuation does not exceed 1°C, the humidity change does not exceed 3%, and the current signal is stable, then that period can be determined as a stable zone. Further analysis of the concentration curve's stability, combined with corresponding time window data, allows the system to filter out stable operating periods and generate stable operating zone data by combining specific monitoring data. For example, if the ozone concentration fluctuates between 0.4 ppm and 0.5 ppm within a certain period, and the airflow and current parameters do not change significantly during this period, then that period is considered a stable operating zone.

[0121] The parameter combination construction submodule extracts temperature, humidity, voltage, discharge frequency and intake airflow rate within the target time period based on the stable operating segment data, and combines each parameter to form a comparison parameter group to obtain a stable parameter combination;

[0122] The parameter combination construction submodule extracts temperature, humidity, voltage, discharge frequency, and inlet air velocity within the target time period based on stable operating segment data, and uses these parameters to form a parameter set. Specifically, it extracts parameters such as temperature (e.g., 25°C), humidity (e.g., 60%RH), voltage (e.g., 450V), discharge frequency (e.g., 50Hz), and inlet air velocity (e.g., 1.5m / s) from the selected stable operating segment data, and integrates these parameters to form a comparison parameter set. Then, based on the comparison between this parameter set and the stable operating segment data, it further analyzes the changing trends of these parameters and, combined with the actual operating scenario, evaluates their impact on ozone generation efficiency. Assuming that changes in voltage and humidity directly affect ozone concentration during different operating periods, the construction of this parameter set can generate stable parameter combinations and provide basic data support for subsequent state identification and adjustment.

[0123] The status recognition and adjustment submodule calls the stable parameter combination, compares the numerical difference between the parameter combination and the corresponding parameter group in the current cycle, identifies the current cycle operating status, adjusts the dry gas response rhythm and the moisture channel trigger time, and generates operating parameter correction results.

[0124] The state recognition and adjustment submodule calls the stable parameter combination and compares it with the parameter set of the current cycle. It compares the differences between the changes in temperature, humidity, voltage, discharge frequency, and inlet air velocity during the target period and the current cycle. If the difference is small, the current cycle is considered similar to the target stable state. For example, if the target period's temperature is 25°C, humidity is 60%RH, and inlet air velocity is 1.5m / s, while the current cycle's temperature is 24.8°C, humidity is 59%RH, and inlet air velocity is 1.6m / s, the difference is small, and the current state can be considered similar to the target stable state. If the difference is large, the system compares the weights of different parameters and adjusts the dry gas response rhythm and humid gas channel trigger time configuration according to the current operating state. This optimizes the airflow and reaction conditions, generates corrected operating parameters, and optimizes ozone generation efficiency, making it more stable and efficient.

[0125] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0126] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0127] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0128] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0129] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0134] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A highly efficient intelligent control system for ozone generation, characterized in that, The system includes: The drive distribution module calls the temperature and humidity sensor to extract temperature and humidity change values ​​in real time, analyzes the temperature and humidity change trends and fluctuation rate differences, adjusts the control frequency and response sequence of the preheating and dehumidification units, and establishes a temperature and humidity linkage control configuration. The gas ratio control module calls the temperature and humidity linkage control configuration, obtains the electrode input signal and concentration sampling result in the ozone reaction chamber, judges the reaction efficiency status, adjusts the dry and wet gas supply rhythm, and establishes a dynamic ratio range of dry and wet airflow. The status recognition module calls the dynamic ratio range of dry and wet airflow, analyzes the continuous change pattern of the current curve in the stable segment during the discharge cycle, judges the characteristics of discharge stability decline and electrode response delay, identifies the discharge imbalance level, adjusts the discharge control configuration, and generates discharge compensation adjustment parameters. The status recognition module includes: The discharge behavior analysis submodule obtains the dynamic ratio range of the dry and wet airflow. Based on the real-time temperature and humidity data, and the temperature and humidity changes collected by the temperature and humidity sensors, a set of reaction process parameters is established based on the monitoring results of electrode input voltage, current signal and outlet ozone concentration, combined with the real-time sampling time. The current curve within the discharge cycle is analyzed to determine the continuity characteristics of the stable section in the current curve, and the duration and fluctuation rhythm of the stable section are statistically analyzed to generate current stability characteristic data. The electrode wear judgment submodule calculates the ratio of voltage rise and fall during the discharge period based on the current stability characteristic data. By comparing the changes in current signal and ozone concentration in real time, if the current value increases, the concentration increases accordingly. The module evaluates the reaction efficiency and obtains the current reaction efficiency state range. By comparing the current and concentration change trends in different time periods and combining the changes in temperature and humidity data, the reaction efficiency can be divided into low efficiency range, high efficiency range and optimal operating range. Based on this, the reaction efficiency change range is generated, the discharge stability decline characteristics are judged, the delay performance of electrode response is identified, and the discharge abnormality characteristic parameters are obtained. The control compensation adjustment submodule determines the discharge imbalance level, assesses the electrode wear status, adjusts the control configuration of the discharge process, and generates discharge compensation adjustment parameters based on the discharge abnormality characteristic parameters. The electrode wear condition is evaluated using the following formula: ； Calculate the electrode wear index and adjust the control configuration of the discharge process; in, This is an indicator of electrode wear. This is the normalized value of the average value during the voltage rise phase. This is the normalized value of the average value during the voltage drop segment. This is the normalized value of the average value during the rising phase of the current. This is the normalized value of the average value during the current decline segment. This is the normalized value of the mean square error of the current; The leakage detection module calls the discharge compensation adjustment parameters, analyzes the ozone concentration change trajectory and airflow direction change trend in the current cycle, evaluates the synchronization offset characteristics, identifies leakage risks, sends response control commands, and generates leakage protection execution records.

2. The intelligent control system for efficient ozone generation according to claim 1, characterized in that, The temperature and humidity linkage control configuration includes temperature response priority, humidity control rhythm, and linkage adjustment parameters. The dry and wet airflow dynamic ratio range includes dry air ratio parameters, wet air ratio parameters, and airflow adjustment sequence. The discharge compensation adjustment parameters specifically include spacing adjustment command, frequency compensation configuration, and wear level judgment basis. The leakage protection execution record includes cut-off action signal, alarm record, and risk level identifier.

3. The intelligent control system for efficient ozone generation according to claim 1, characterized in that, The drive allocation module includes: The temperature and humidity change trend submodule calls the temperature and humidity sensor to obtain the temperature change value and humidity change value in real time, compares the direction of temperature change and humidity change within the current cycle, calculates the absolute difference between the two changes, and establishes temperature and humidity change trend data. The fluctuation rate analysis submodule analyzes the rate of temperature change and the rate of humidity change within the same time period based on the temperature and humidity change trend data, calculates the rate difference, and generates a temperature and humidity fluctuation rate difference index. The priority adjustment submodule calls the temperature and humidity fluctuation rate difference index, adjusts the control frequency allocation and response order of the preheating unit and the dehumidification unit, optimizes the control configuration of the corresponding execution unit, and generates a temperature and humidity linkage control configuration.

4. The intelligent control system for efficient ozone generation according to claim 3, characterized in that, The specific formula for the difference in calculation rate is as follows: ； in, This is an index of the difference in the rate of temperature and humidity fluctuations. For the first The normalized value of the rate of temperature change over a given time period relative to the reference maximum rate of temperature change. For the first The normalized value of the rate of humidity change over a given time period relative to a reference maximum rate of humidity change. This refers to the index number of discrete time periods within the monitoring period. This refers to the total number of discrete time periods within the monitoring period.

5. The intelligent control system for high-efficiency ozone generation according to claim 3, characterized in that, The gas ratio control module includes: The reaction data acquisition submodule acquires the temperature and humidity linkage control configuration, monitors the electrode input voltage, current signal and outlet ozone concentration in the ozone reaction chamber, and establishes a set of reaction process parameters by combining time data. The real-time efficiency evaluation submodule analyzes the correspondence between the electrode input signal and the ozone concentration change based on the set of reaction process parameters, evaluates and determines the operating state range of the reaction efficiency, and generates the reaction efficiency change range. The rhythm adjustment submodule compares the change trajectory of the dry gas flow rate with the change sequence of the opening and closing rhythm of the wet gas valve based on the change range of the reaction efficiency, adjusts the control cycle length of the dry and wet gas supply channels and the interval of valve driving, and establishes a dynamic ratio range of dry and wet gas flow.

6. The intelligent control system for efficient ozone generation according to claim 1, characterized in that, The leakage detection module includes: The real-time synchronization analysis submodule calls the discharge compensation adjustment parameters to analyze the continuous change trajectory of ozone concentration and the change trend of airflow direction within the current cycle, filters the offset time period when the airflow propagation direction is synchronized with the ozone release change, and generates concentration airflow synchronization data. The leakage risk identification submodule compares the ozone concentration increase during the decreasing phase of the airflow velocity based on the concentration airflow synchronization data, determines the correlation between airflow changes and ozone release, assesses leakage risk, and generates leakage risk assessment data. Based on the leakage risk assessment data, the control response output submodule sends response control commands, including output gas cut-off and alarm control commands, and generates leakage protection execution records.

7. The intelligent control system for efficient ozone generation according to claim 1, characterized in that, The system also includes: The feedback adjustment module analyzes the ozone concentration characteristics within the monitoring cycle based on the leakage protection execution record, extracts the parameters of the stable section, compares them with the parameters of the current cycle, identifies the current cycle operating status, adjusts the dry gas response rhythm and the wet gas channel trigger time configuration, and generates operating parameter correction results. The operational parameter correction results specifically refer to the parameter correction magnitude, channel configuration status, and status identification results.

8. The intelligent control system for efficient ozone generation according to claim 7, characterized in that, The feedback adjustment module includes: The stable section extraction submodule obtains the leakage protection execution record, analyzes the ozone concentration change curve within the continuous monitoring period, filters out stable operating sections based on the fluctuation amplitude, and establishes stable operating section data. The parameter combination construction submodule extracts the temperature, humidity, voltage, discharge frequency and airflow rate within the target time period based on the stable operating segment data, and combines each parameter to form a comparison parameter group to obtain a stable parameter combination; The state recognition and adjustment submodule calls the stable parameter combination, compares the numerical difference between the parameter combination and the corresponding parameter group in the current cycle, identifies the current cycle operating state, adjusts the dry gas response rhythm and the moisture channel trigger time, and generates operating parameter correction results.

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