Intelligent control system for efficient ozone generation

Abstract

The invention relates to the technical field of intelligent control, in particular to an intelligent control system for efficient ozone generation, which comprises the following steps: a driving distribution module, a gas ratio regulation and control module, a state recognition module, a leakage judgment module and a feedback regulation module. According to the method, through linkage judgment of the real-time fluctuation trend of the temperature and humidity and the response priority sequence, dynamic sorting of parameters and timely recognition of dominant factors are achieved, and by combining dry and wet gas ratio regulation and control in an ozone reaction efficiency interval and grading analysis of a current curve and discharge stability characteristics in a discharge period, the ozone reaction efficiency is improved. Multi-condition risk judgment of a synchronous relation between ozone concentration and airflow change is introduced, screening of stable sections and progressive comparison of rhythm configuration are performed by applying multi-period concentration parameters and key operation parameters, and an automatic adjustment strategy based on multi-source working condition participation items is formed. A closed-loop control structure of multi-dimensional condition collaborative recognition, dynamic adjustment and real-time feedback circulation is constructed, and self-adaptive optimization of gas path, discharge and adjustment actions is achieved.

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. It is difficult to 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. The timing and amplitude of the adjustment action are difficult to adapt to the actual needs of the working conditions. In the presence of complex scenarios such as air backflow, leakage and fluctuation, it is difficult to accurately distinguish 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 present application provides an intelligent control system for efficient ozone generation. The technical solution is as follows: On the one hand, an intelligent control system for efficient ozone generation is provided, which includes: The driving distribution module calls the temperature and humidity sensor, extracts temperature change value and humidity change value in real time, analyzes temperature and humidity change trend and fluctuation rate difference, adjusts control frequency and response order of the preheating and dehumidification unit, and establishes temperature and humidity linkage control configuration; The gas ratio control module calls the temperature and humidity linkage control configuration, obtains electrode input signal and concentration sampling result in the ozone reaction cavity, judges reaction efficiency state, adjusts dry and wet gas supply rhythm, and establishes dry and wet gas flow dynamic matching interval; The state recognition module calls the dry and wet gas flow dynamic matching interval, analyzes continuity change form of the current curve stable section in the discharge period, judges discharge stability decline feature and electrode response delay state, recognizes discharge imbalance grade, adjusts discharge control configuration, and generates discharge compensation adjustment parameter; The leakage discrimination module calls the discharge compensation adjustment parameter, analyzes ozone concentration change trajectory and gas flow direction change trend in the current period, evaluates synchronous offset feature, recognizes leakage risk and sends response control instruction, and generates leakage protection execution record.

[0005] As a further scheme of the present application, the temperature and humidity linkage control configuration comprises temperature response priority, humidity control rhythm and linkage adjustment parameter, the dry and wet gas flow dynamic matching interval comprises dry gas matching parameter, wet gas matching parameter and gas flow adjustment time sequence, the discharge compensation adjustment parameter specifically is spacing adjustment instruction, frequency compensation configuration and wear grade judgment basis, and the leakage protection execution record comprises cut-off action signal, alarm record and risk grade identification.

[0006] As a further scheme of the present application, the driving distribution module comprises: The temperature and humidity change trend submodule calls the temperature and humidity sensor, obtains temperature change value and humidity change value in real time, compares temperature change direction and humidity change direction in the current period, calculates absolute difference of change amplitudes of the two, and establishes temperature and humidity change trend data; The fluctuation rate analysis submodule analyzes temperature change rate and humidity change rate in the same time period based on the temperature and humidity change trend data, calculates rate difference, and generates temperature and humidity fluctuation rate difference index; The priority order adjustment submodule calls the temperature and humidity fluctuation rate difference index, adjusts control frequency distribution and response order of the preheating unit and the dehumidification unit, optimizes control configuration of the corresponding execution unit, and generates temperature and humidity linkage control configuration.

[0007] As a further scheme of the present application, the specific formula of the calculated rate difference is: ; Wherein, is the temperature and humidity fluctuation rate difference index, is the first a normalized value of the temperature change rate in the time period relative to a reference maximum temperature change rate, is the number of the first a normalized value of the humidity change rate in the time period relative to a reference maximum humidity change rate, is an index number of a discrete time period in the monitoring period, is the total number of discrete time periods in the monitoring period.

[0008] As a further scheme of the present application, the gas ratio control module comprises: 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; 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; 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.

[0009] As a further scheme of the present application, the state recognition module comprises: 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; The electrode wear judgment submodule calculates the proportional relationship of the voltage rising section and the voltage falling section in the period 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; The control compensation adjustment submodule judges the discharge imbalance level according to the discharge abnormal feature parameters, evaluates the electrode wear state, adjusts the control configuration of the discharge process, and generates discharge compensation adjustment parameters.

[0010] As a further scheme of the present application, the electrode wear state is evaluated by the formula: ; The electrode wear degree index is calculated, and the control configuration of the discharge process is adjusted; wherein, is the electrode wear degree index, is a normalized value of the average value of the voltage rising section, is a normalized value of the average value of the voltage falling section, a normalized value of the average value of the current rising section, a normalized value of the average value of the current falling section, a normalized value of the mean square error of the current.

[0011] As a further scheme of the present application, the leakage discrimination module comprises: The real-time synchronous analysis submodule calls the discharge 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; The leakage risk identification submodule compares the ozone concentration growth amplitude of the airflow speed 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; 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.

[0012] As a further scheme of the present application, the system further comprises: 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; The running parameter correction result specifically refers to the parameter correction amplitude, the channel configuration state, and the state recognition result.

[0013] As a further scheme of the present application, the feedback adjustment module comprises: The stable section extraction submodule obtains the leakage protection execution record, analyzes the ozone concentration change curve in the continuous monitoring period, screens the stable running period according to the fluctuation amplitude, and establishes running stable section data; The parameter combination construction submodule extracts the temperature, humidity, voltage, discharge frequency and inlet airflow speed in the target period based on the running stable section data, combines each parameter to form a comparison parameter group, and obtains a stable parameter combination; The state recognition adjustment submodule calls the stable parameter combination, compares the numerical difference between the parameter combination and the corresponding parameter group in the current period, identifies the current period running state, adjusts the dry gas response rhythm and the wet gas channel trigger time, and generates a running parameter correction result.

[0014] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects: Through linkage judgment of real-time fluctuation trend of temperature and humidity and response priority order, parameter dynamic sequencing and timely identification of dominant factors are realized, combined with dry and wet gas ratio control in the ozone reaction efficiency interval, grading analysis of current curve and discharge stability characteristics in the discharge period, multi-condition risk discrimination of synchronous relationship between ozone concentration and airflow change is introduced, and multi-cycle concentration parameters and key operation parameters are used for progressive comparison of screening and rhythm configuration of stable section, forming an automatic adjustment strategy based on multi-source working condition participation, constructing a closed-loop control structure of multi-dimensional condition collaborative identification, dynamic adjustment and real-time feedback cycle, realizing adaptive optimization of air path, discharge and adjustment action. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 The system flowchart of the present application; Figure 2 The system framework schematic diagram of the present application; Figure 3 The driving distribution module flowchart of the present application; Figure 4 The air ratio control module flowchart of the present application; Figure 5 The state recognition module flowchart of the present application; Figure 6 The leakage discrimination module flowchart of the present application; Figure 7 The feedback adjustment module flowchart of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the present application will be described below in combination with the drawings.

[0018] 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 way. 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.

[0019] In the embodiments of the present application, "image" and "picture" can be used interchangeably, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0020] 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.

[0021] 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 combination with the drawings and specific embodiments.

[0022] The present application provides 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: 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 sequence of the preheating and dehumidification unit, 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 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; 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, identifies the discharge imbalance grade, adjusts the discharge control configuration, and generates discharge compensation adjustment parameters; The leakage discrimination module calls the discharge compensation adjustment parameters, analyzes the ozone concentration change trajectory and gas flow direction change trend in the current period, evaluates the synchronous offset feature, identifies the leakage risk and sends the response control instruction, and generates a leakage protection execution record; The feedback adjustment module analyzes the ozone concentration feature in the monitoring period according to the leakage protection execution record, extracts the stable section parameters, and compares them with the current period parameters, identifies the current period running state, adjusts the dry gas response rhythm and wet gas channel trigger time configuration, and generates a running parameter correction result.

[0023] The temperature and humidity linkage control configuration includes temperature response priority, humidity control rhythm, linkage adjustment parameter, dry and wet gas flow dynamic matching interval includes dry gas matching parameter, wet gas matching parameter, air flow adjustment time sequence, discharge compensation adjustment parameter specifically is interval adjustment instruction, frequency compensation configuration, wear grade judgment basis, leakage protection execution record includes cut-off action signal, alarm record, risk grade identification, running parameter correction result specifically points parameter correction amplitude, channel configuration state, state identification result.

[0024] Please refer to Figure 2 and Figure 3 , the drive distribution module includes: The temperature and humidity change trend sub-module calls the temperature and humidity sensor, obtains the temperature change value and the humidity change value in real time, compares the temperature change direction and the 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; The temperature and humidity change trend sub-module obtains the temperature change value and the humidity change value collected by the temperature and humidity sensor, and first analyzes the temperature change direction in the current period through the real-time obtained temperature and humidity data, and compares the direction with the humidity change direction. If both the temperature and humidity show an upward trend, it indicates that the environmental hot and humid conditions may tend to increase, and at this time the temperature and humidity change rate needs to be calculated. The calculation method is, first, the temperature change rate per unit time is calculated through the ratio of the continuous measurement value of the temperature data to the time, and similarly, the humidity change rate is calculated through the ratio of the continuous measurement value of the humidity data to the time. Then, the absolute difference of the change amplitudes of the two is calculated, and the temperature and humidity fluctuation rate difference index is obtained. In the 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(°C / min). Based on this index, the control strategy of the preheating and dehumidifying equipment in the environment can be adjusted in real time, thereby creating temperature and humidity change trend data.

[0025] The fluctuation rate analysis sub-module analyzes the temperature change rate and the 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; The specific formula for calculating the rate difference is: ; Wherein, is the temperature and humidity fluctuation rate difference index, is the normalized value of the temperature change rate in the i-th period relative to the reference maximum temperature change rate, is the normalized value of the humidity change rate in the i-th period relative to the reference maximum humidity change rate, ​​Index number of discrete time period in monitoring cycle, Total number of discrete time period in monitoring cycle.

[0026] In the fluctuation rate analysis submodule, the fluctuation rate difference index calculation formula is: ; Wherein, is the temperature and humidity fluctuation rate difference index, which represents the relative deviation between the temperature change rate and the humidity change rate in the same time period, represents the temperature change rate normalization value of the time node, represents the humidity change rate normalization value of the time node. The denominator part is wrapped with the square root , which is used to introduce the intensity weighting after the multiplication factor, so that the denominator changes synchronously with the rate intensity, embodies the balance constraint brought by multiplication, and amplifies the influence of the synchronization of the intensity change of the two; The numerator part is the absolute deviation of temperature rate and humidity rate, which describes the difference amplitude of the change trend. In the overall formula, the absolute value, multiplication and square root are combined to reflect the multi-angle weighting processing of fluctuation difference by multiple factors. To obtain the parameters and required for calculation, the following methods are used for acquisition and processing: collect the temperature and humidity change data in the continuous monitoring cycle, record once every 1 second, and the total collection time is 30 seconds. Calculate the difference between the 1st second and the previous second, and use the following normalization processing: , , wherein, are the temperature values collected at the current time and the previous time, respectively, in ℃; are the corresponding humidity values, in %RH. Taking the 10th second as an example, the collected values are as follows: , , , The maximum and minimum values in the interval are: the maximum temperature is 24.3, the minimum temperature is 22.7, the maximum humidity is 60.1, and the minimum humidity is 55.3. The normalized processing is: ; ; Substitute into the main formula for calculation: ; The result shows 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 the fluctuation trends of the two, which can be used as a quantitative standard for fluctuation rate difference, and further used in the subsequent priority order adjustment module for calling and using, affecting the setting of preheating and dehumidification response strategy.

[0027] Table 1 Temperature and humidity change sampling data ; As shown in Table 1, the change sampling data of temperature and humidity from 9th second to 10th second is listed for calculating the rate normalization value of 10th second. This data set is collected by a standard temperature and humidity sensor with an update frequency of 1 Hz, which meets the real-time requirement of the monitoring period. According to the above calculation, the normalized rate value is obtained and substituted into the formula, and the final 0.0776 can be used as a reference basis for the temperature and humidity fluctuation rate difference index of this time period. The formula introduces a weighted form through the product factor of temperature and humidity rate and root, so that the rate difference is automatically weakened in intensity amplitude and enhanced in synchronization difference, effectively improving the response ability to identify nonlinear deviation trends; and the continuity quantitative index can be output to support dynamic control strategy selection.

[0028] The priority order adjustment submodule calls the temperature and humidity fluctuation rate difference index to adjust the control frequency distribution and response order of the preheating unit and the dehumidification unit, optimize the control configuration of the corresponding execution unit, and generate a temperature and humidity linkage control configuration; The priority order adjustment submodule calls the temperature and humidity fluctuation rate difference index, adjusts the response order and frequency of the preheating unit and the dehumidification unit according to the index and the environmental change condition in the current period, and if the temperature and humidity difference is large, the response priority of the preheating unit is increased, otherwise the priority of the dehumidification unit is increased. Assuming that the temperature and humidity fluctuation rate difference is 0.4 (°C / min) at this time, in this case, the control frequency of the preheating unit is relatively increased, and the response period of the dehumidification unit is reduced. By dynamically controlling the coordination of preheating and dehumidification equipment, the equipment can be intelligently adjusted according to real-time environmental conditions, thereby optimizing the entire control process, generating a temperature and humidity linkage control configuration, for example, when the temperature and humidity fluctuation rate difference is greater than 0.3, the control frequency of the preheating unit is 70%, and the control frequency of the dehumidification unit is 30%.

[0029] Please refer to Figure 2 and Figure 4 , the gas ratio control module includes: The reaction data acquisition submodule obtains 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 combined with time data; The reaction data acquisition submodule obtains the temperature and humidity linkage control configuration, based on the real-time monitored temperature and humidity data, obtains the electrode input voltage, current signal and outlet ozone concentration data in the reaction cavity through the digital temperature sensor and the capacitive humidity sensor, and combines the time data. These data will change in real time with the reaction process, so by correlating the temperature and humidity values, input voltage, current and concentration values at each data sampling time point, a reaction process parameter set 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 0.5ppm, then the real-time data at each moment will be collected and correlated based on this, generating a reaction process parameter set. These data can accurately reflect the ozone generation status under different environmental conditions in the reaction cavity.

[0030] 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. The real-time efficiency evaluation submodule analyzes the relationship between the electrode input voltage, current signal and ozone concentration change based on the reaction process parameter set. First, the change curve of the electrode input signal and the concentration is obtained, and then the correlation between the current and the concentration is calculated, the influence of the current change on the concentration change is analyzed, and the reaction efficiency is evaluated through the synchronicity of the current signal change section and the concentration change section. Assuming that when the current signal reaches 1A, the ozone concentration increases by 0.2ppm, and vice versa, when the current signal decreases, the ozone concentration will decrease accordingly. Based on these data, the running state interval of the reaction efficiency in different time intervals is calculated and evaluated, and then the reaction efficiency change interval is generated. For example, in actual operation, if the current signal is stable at 0.8A and the concentration is stable at 0.5ppm, then in this interval, the running section with low efficiency is evaluated. If the current signal is increased to 1.2A and the concentration is increased to 1.0ppm, then the running section with high efficiency is evaluated.

[0031] The adjustment rhythm adjustment submodule compares the change trajectory of the dry gas flow rate and the change sequence of the wet gas valve opening and closing rhythm 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. The adjustment rhythm adjustment submodule compares the dry gas flow rate and the change sequence of the wet gas valve opening and closing rhythm according to the reaction efficiency variation interval, analyzes the opening and closing state of the dry gas flow rate and the wet gas valve through the real-time monitored data, and compares the change trajectories of the two, if in the interval with higher reaction efficiency, the dry gas flow rate rises faster and the opening and closing rhythm of the wet gas valve lags behind, the adjustment of the wet gas flow needs to be optimized, the response frequency of the wet gas valve is increased, so as to ensure that the ratio of wet gas and dry gas meets the best condition of ozone generation, for example, when the reaction efficiency is higher, the dry gas flow rate changes to 0.5 m / s, the response frequency of the wet gas valve is once every 5 minutes, when the efficiency is improved, the response frequency of the wet gas valve is increased to once every 2 minutes, by adjusting the control cycle length of the dry and wet gas flow and the valve driving interval parameters, the dry and wet gas flow dynamic matching interval is finally generated, which ensures the matching of the gas flow and the reaction condition and improves the reaction efficiency.

[0032] Please refer to Figure 2 and Figure 5 , the state recognition module comprises: The discharge behavior analysis submodule obtains the dry and wet gas flow dynamic matching interval, analyzes the current curve in the discharge period, judges 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; The reaction data acquisition submodule acquires the temperature and humidity data in real time, acquires the temperature change and humidity change values through the temperature and humidity sensor, respectively based on the monitoring results of the electrode input voltage, current signal and outlet ozone concentration, and combines the real-time sampling time to establish a reaction process parameter set. Specifically, in each reaction period, the data acquired in real time by the temperature and humidity sensor, such as the temperature 0.3°C and the humidity 1%RH collected every minute, combined with the real-time values of the electrode input voltage such as 500V, the current signal such as 0.8A, and the change data of the ozone concentration 0.5ppm, facilitate the creation of a specific parameter set at different time points. This data set not only reflects the comprehensive effect of temperature and humidity in the reaction cavity and electrode reaction, but also adjusts the temperature and humidity control strategy in the reaction process according to the actual running state, so as to optimize the stability and efficiency of the ozone generation process.

[0033] The electrode wear judgment submodule calculates the proportional relationship of the voltage rising section and the voltage falling section in the period based on the current stability characteristic data, judges the discharge stability decline characteristics, identifies the delay performance of the electrode response, and obtains the discharge abnormal characteristic parameters; The real-time efficiency evaluation submodule judges the corresponding relationship between the electrode input signal and the ozone concentration change by analyzing the temperature and humidity and current signals in the reaction process parameter set. For example, by comparing the current signal and the change of the ozone concentration in real time, if the current value increases and the concentration correspondingly increases, the reaction efficiency is evaluated, and the current reaction efficiency state interval is obtained. 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 increases to 1.2 A, it can be judged that the reaction efficiency interval is “high efficiency” through this change. By comparing the current and concentration change trend in different time periods, combined with the change of temperature and humidity data, the reaction efficiency can be divided into low efficiency interval, high efficiency interval and best running interval, and the reaction efficiency change interval is generated accordingly to optimize the subsequent reaction process adjustment configuration.

[0034] The control compensation adjustment submodule judges the discharge imbalance level according to the discharge abnormal characteristic parameters, evaluates the electrode wear state, adjusts the control configuration of the discharge process, and generates the discharge compensation adjustment parameters; The electrode wear state is evaluated by using the formula: ; The electrode wear degree index is calculated, and the control configuration of the discharge process is adjusted; 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 error of the current.

[0035] In the above content, the electrode wear state is evaluated by using the formula: ; the electrode wear degree index is calculated, the discharge imbalance level is judged, the electrode wear state is evaluated, the control configuration of the discharge process is adjusted, and the discharge compensation adjustment parameters are generated. The formula and parameter description are as follows: : The normalized average value of the voltage rising section, which refers to the average value of the recorded voltage value in the process of the voltage rising from the lowest point to the highest point, and is normalized to ensure that the data is within the range of 0 to 1. The calculation formula is: ; wherein, is the measured value of the voltage in the rising section, is the number of sampling points. This value describes the average response of the voltage rising section. : The normalized average value of the voltage falling section, which is the same as the processing method of the voltage rising section, calculates the average value of the voltage value in the falling section, and is normalized to represent the change amplitude of the voltage falling. : Normalized average of the current rise segment, similar to the voltage processing, describes the current rise situation by averaging the current rise segment data and normalizing to the range of 0 to 1. : Normalized average of the current fall segment, calculated in the same way as the current rise segment, represents the change amplitude of the current fall segment. : Normalized value of the mean square error of the current, used to measure the fluctuation intensity of the current, calculated as: ; where, is the value of the current at the th measurement point, is the average value of the current, is the number of sampling points. The absolute value operation involved in the formula, such as , calculates the absolute difference between the voltage rise segment and the fall segment, reflecting the asymmetry of the voltage between these segments. In this way, the imbalance or asymmetry phenomenon in voltage change can be captured more clearly. The addition of the two absolute differences in the formula aims to aggregate the change amplitudes of voltage and current, reflecting the joint influence of these two physical quantities on electrode wear. By introducing the mean square error of current fluctuation, the square root operation is used to normalize the change amplitude of the current, thereby adjusting the weighting factor so that the influence of current fluctuation on the final result is reasonably adjusted. By combining voltage, current change, and current fluctuation amplitude, the formula can evaluate electrode wear, thereby providing a basis for subsequent control compensation adjustment. Through this comprehensive evaluation, the health status of the electrode can be effectively reflected, and further control can be guided.

[0036] Suppose in a certain experiment, the measured values of the voltage rise and fall segments are as follows (unit: volts): voltage rise segment: ; voltage fall segment: ; the measured values of the current rise and fall segments are as follows (unit: amperes): current rise segment: ; current fall segment: ; From these data, the values of various parameters are calculated: The normalized average of the voltage rise and fall segments is: ; ; After normalization, the normalized value of the voltage change is: ; ; The mean square error of the current is calculated: ; The electrode wear degree index is finally obtained by bringing the calculation results into the formula That is: ; The formula can effectively quantify the wear state of the electrode by combining the voltage, current change and mean square deviation of current fluctuation, especially the sensitivity to discharge imbalance. The use of this composite index makes the electrode wear detection more accurate, thereby providing more reliable decision basis for subsequent electrode compensation and control system.

[0037] Table 2 experimental data ; As shown in Table 2, the experimental data of the voltage rising section, the voltage falling section and the current change section are obtained by monitoring and real-time measurement, and then used to calculate the parameters in the above formula.

[0038] Please refer to Figure 2 and Figure 6 , the leakage discrimination module includes: The real-time synchronous analysis submodule calls the discharge compensation adjustment parameter, analyzes the continuous change trajectory of ozone concentration and the change trend of airflow direction in the current period, filters the offset period of airflow propagation direction and ozone release change synchronization, and generates concentration airflow synchronization data; The real-time synchronous analysis submodule calls the discharge compensation adjustment parameter, and synchronously monitors the ozone concentration and airflow direction in the reaction process. First, the real-time ozone concentration change trajectory is obtained, and compared with the change trend of airflow direction. Assuming that at a certain moment, the airflow direction changes from forward flow to reverse flow, at this time the concentration also changes. In actual operation, when the airflow speed changes to 0.8 m / s, the ozone concentration changes to 0.3 ppm, and vice versa, when the airflow speed decreases to 0.5 m / s, the concentration decreases to 0.1 ppm. The data is continuously collected and used to generate concentration airflow synchronization data. By comparing the time relationship and change trend between the two, the synchronization offset between airflow change and ozone concentration can be identified. Further analysis, combined with timestamp data, can accurately determine the synchronization of airflow change and ozone concentration change in the same period, and obtain concentration airflow synchronization data. For example, if the concentration increases by 0.2 ppm when the airflow flows reversely in a certain interval, and then the concentration slightly falls to 0.15 ppm when the airflow recovers, it can be considered that the change of airflow and ozone release is synchronized in this period, and accurate synchronization data is generated.

[0039] The leakage risk identification submodule compares the ozone concentration growth rate of airflow speed in the falling stage according to the concentration airflow synchronization data, judges the correlation between airflow change and ozone release, evaluates the leakage risk, and generates leakage risk judgment data; The leakage risk identification submodule is based on the concentration airflow synchronous data, and calculates the correlation between the airflow flow rate and the ozone concentration growth rate in the falling stage by analyzing the ozone concentration growth rate. Assuming that at a certain moment, the airflow flow rate drops to 0.4 m / s, and the ozone concentration growth rate is 0.1 ppm, and at another moment, the airflow flow rate drops to 0.3 m / s, and the concentration growth rate is 0.15 ppm. If the growth trends of the two are consistent, it indicates that the airflow change and the ozone release have a strong correlation. Next, by calculating the ozone concentration increase in the airflow flow rate falling stage, it can be comprehensively evaluated whether there is a leakage risk in this stage. On this basis, risk assessment is carried out and leakage risk judgment data is generated. According to the actual numerical value, if the airflow flow rate drop and the concentration change fail to keep the expected synchronization, or the concentration growth rate is significantly greater than the expected value, a leakage risk may occur, and the system immediately enters the warning state and triggers the corresponding protection measures.

[0040] The control response output submodule sends response control instructions based on the leakage risk judgment data, including output gas cutoff and alarm control instructions, and generates leakage protection execution records; The control response output submodule judges whether a leakage risk occurs by comparing the airflow flow rate drop and the concentration change based on the leakage risk judgment data. If a leakage risk is identified, the system will send response control instructions, including gas cutoff control and alarm signal control. For example, when the leakage risk reaches the set threshold, the system sends instructions to cut off the gas supply channel and sends an alarm notification. Through the control response output, the system can cut off the gas source in time and start the alarm mechanism when a leakage is found. This operation effectively prevents potential equipment damage or environmental pollution and ensures the safety of system operation. This process generates leakage protection execution records and is further used for later maintenance and monitoring to ensure the long-term stable operation of the system.

[0041] Please refer to Figure 2 and Figure 7 , the feedback regulation module comprises: The stable section extraction submodule obtains the leakage protection execution records, analyzes the ozone concentration change curve in the continuous monitoring period, selects the stable operation period according to the fluctuation amplitude, and establishes the running stable section data; The stable section extraction submodule analyzes the ozone concentration change curve in the continuous monitoring period according to the leakage protection execution record, first acquires detailed data of the change of the ozone concentration with time, and if the concentration fluctuation is not large in a certain period of time, the current is relatively stable, and the temperature and humidity are relatively stable, the time period can be marked as a stable section. Through fluctuation amplitude screening of these data, it is assumed that in a certain monitoring period, the concentration change amplitude 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, it can be judged that the time period is a stable section. Further analyze the stability of the concentration curve, combine the corresponding time window data, the system screens out the stable running period, and generates the running stable section data by combining the specific monitoring data. For example, the ozone concentration fluctuates between 0.4 ppm and 0.5 ppm in a certain period of time, and the airflow and current parameters do not change greatly in this period, so this period is considered as a stable running section.

[0042] The parameter combination construction submodule extracts the temperature, humidity, voltage, discharge frequency and inlet airflow speed in the target period based on the running stable section data, and combines each parameter to form a comparison parameter group to obtain a stable parameter combination. The parameter combination construction submodule extracts the temperature, humidity, voltage, discharge frequency and inlet airflow speed in the target period based on the running stable section data, and uses these parameters to form a parameter group. Specifically, the temperature such as 25°C, the humidity such as 60%RH, the voltage such as 450V, the discharge frequency such as 50Hz, and the inlet airflow speed such as 1.5m / s are extracted from the screened stable section data, and these parameters are integrated to form a comparison parameter group. Then, based on the comparison of the parameter group and the stable section data, the change trend of these parameters is further analyzed, and the influence of the actual running scene on the ozone generation efficiency is evaluated. It is assumed that the changes of the voltage and the humidity will directly affect the ozone concentration in different running periods, at this time, through the construction of the parameter group, a stable parameter combination can be generated, and basic data support is provided for subsequent state recognition and adjustment.

[0043] 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, identifies the current period running state, adjusts the dry gas response rhythm and the wet gas channel trigger time, and generates a running parameter correction result. The state recognition adjustment sub-module calls the stable parameter combination, compares the differences between the temperature, humidity, voltage, discharge frequency and air inlet flow rate in the target period and the change values in the current period one by one, and if the differences are small, it is considered that the current period is similar to the target stable state. For example, in a certain operating period, if the temperature of the target period is 25°C, the humidity is 60% RH, and the air inlet flow rate is 1.5 m / s, while the temperature of the current period is 24.8°C, the humidity is 59% RH, and the air inlet flow rate is 1.6 m / s, the differences are small, and it can be considered that the current state is similar to the target stable state. If the differences are large, the system will compare the weights of different parameters, adjust the dry gas response rhythm and wet gas channel trigger time configuration according to the current operating state, so as to optimize the air flow and reaction conditions, generate operating parameter correction results, optimize the ozone generation efficiency, and make it more stable and efficient.

[0044] The above embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the flow or function described in the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 by wired (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0045] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, and means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the front and rear associated objects are in an "or" relationship, but can also represent an "and / or" relationship, which can be understood according to the context.

[0046] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including a single item or any combination of multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0047] It should be understood that the size of the sequence number of the above-mentioned processes does not mean the order of execution in various embodiments of the present application. The execution order of the processes should be determined according to their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0048] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized 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 implementation should not be considered beyond the scope of the present application.

[0049] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0050] In several embodiments provided by the present application, 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 schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0051] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0052] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0053] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0054] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection 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 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 high-efficiency 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 5, characterized in that, The status recognition module includes: The discharge behavior analysis submodule obtains the dynamic ratio range of the 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. The electrode wear judgment submodule calculates the ratio of voltage rise and fall during the discharge period based on the current stability characteristic data, judges the characteristics of decreased discharge stability, identifies the delayed performance of electrode response, and obtains abnormal discharge characteristic parameters. 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.

7. The intelligent control system for efficient ozone generation according to claim 6, characterized in that, 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.

8. The intelligent control system for efficient ozone generation according to claim 6, 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.

9. 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.

10. The intelligent control system for efficient ozone generation according to claim 9, 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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