Ozone control optimization method for creeping discharge

Abstract

The invention relates to the technical field of ozone control, in particular to a creeping discharge ozone control optimization method, which comprises the following steps: acquiring temperature, humidity, voltage and ozone concentration data based on a discharge device, performing proportion conversion and optimization, extracting trend characteristic factors, screening nodes according to relevance, and sequencing to obtain a creeping discharge ozone control model; the combined effect of electricity, temperature and humidity on ozone change is analyzed, the regulation and control unit is identified, and load distribution configuration is optimized. According to the invention, normalization processing is carried out on temperature, humidity, electrical and ozone concentration data collected in a region, a data linkage mechanism with temperature and humidity change as a core is constructed, node response performance is dynamically sequenced, and periodic data change is fused, so that a set of control flow for cooperatively adjusting temperature, humidity and electrical parameters is formed; real-time adjustment of partitioned loads and dynamic optimization of energy distribution are realized, agile response and multi-region difference matching capability of ozone generation regulation are brought, and the stability under ozone yield fluctuation is improved.

Description

Technical Field

[0001] This invention relates to the field of ozone control technology, and in particular to an optimized method for ozone control via surface discharge. Background Technology

[0002] Ozone control technology involves processes and systems related to the generation and regulation of ozone, mainly including the ozone generation principle, discharge method, discharge equipment structure, ozone concentration monitoring and regulation, and related environmental safety management. It is widely used in water treatment, air purification, industrial oxidation, and other scenarios. Among these, the traditional surface discharge ozone control optimization method refers to using the formation of discharge channels on the surface of an insulator, adjusting electrode structure, material properties, and applied voltage parameters to change the discharge path and intensity, thereby controlling the ozone generation process.

[0003] Existing technologies neglect the dynamic changes in temperature and humidity in multiple regions, making it difficult to identify differences in operating conditions between regions. This results in slow regional control response under a single parameter, local overload, and large fluctuations in ozone production. In environments with high humidity, high temperature, and uneven operating conditions, untimely adjustments lead to uneven energy distribution and unstable ozone production processes, making it difficult to adapt to real-world situations that demand higher standards of differential regulation and energy consumption control. Summary of the Invention

[0004] To address the shortcomings of existing technologies, such as neglecting the dynamic changes in temperature and humidity across multiple regions, difficulty in identifying differences in operating conditions between different regions, slow response of regional regulation under single-parameter drive, local overload, and large fluctuations in ozone production, and the instability of energy distribution and ozone production processes caused by untimely adjustments in high-humidity, high-temperature, and uneven operating environments, this invention provides a surface discharge ozone control optimization method. The technical solution is as follows: On the one hand, a method for optimizing ozone control along surface discharge is provided, including the following steps: S1: Based on the discharge device, collect temperature, humidity, voltage and ozone concentration data of each region, standardize the temperature and humidity ratios, perform correlation analysis between the optimized parameters and ozone concentration, summarize the trend changes of each region, and obtain the node trend characteristic factors. S2: Based on the node trend characteristic factors, calculate the rate of change of temperature and humidity at each monitoring point, analyze its correlation with ozone concentration, screen highly correlated nodes, determine the intensity of influence and prioritize them to obtain the response priority sequence. S3: Based on the response priority sequence, compare the electrode voltage, discharge current density and temperature and humidity data of the top-ranked regions, analyze the ozone change characteristics under multi-parameter linkage, identify key nodes, and obtain the distribution unit of the regulation effect. S4: Based on the distribution unit of the regulation effect, analyze the high-frequency voltage fluctuation of the node, determine the voltage peak amplitude change, and combine the node temperature and humidity changes to compare their correlation with voltage anomalies, identify coupling offset characteristics, and obtain the coupling offset performance quantity.

[0005] On the other hand, the node trend characteristic factors include trend intensity parameters, node association level, and change direction type; the response priority sequence includes priority response level, response node number, and hierarchical sorting identifier; the regulation effect distribution unit includes regulation node group, distribution weight coefficient, and effect category type; and the coupling offset performance includes temperature and humidity offset amplitude, voltage fluctuation characteristics, and coupling performance label.

[0006] On the other hand, the specific steps for the node trend feature factor are as follows: S101: Based on the discharge device, analyze the temperature data, humidity data, power supply voltage data and ozone concentration data of each node, standardize the raw temperature and humidity data collected by each node, merge them in node order, and compare the data under the same standard to obtain a unified parameter baseline group. S102: Based on the unified parameter baseline group, compare it with the ozone concentration change data of each node, establish the correspondence between temperature and humidity and ozone concentration data group by group according to node number, determine the consistency between the direction of temperature and humidity change and the direction of ozone concentration change within the time difference period, calculate the number of times synchronous change occurs, and obtain the linkage trend characteristic quantity. S103: Based on the aforementioned linkage trend characteristic, select nodes whose temperature, humidity and ozone concentration are in the same direction, analyze the frequency and fluctuation amplitude of synchronous changes within each group of nodes, group them according to node number, and obtain node trend characteristic factors.

[0007] On the other hand, the specific steps of the response priority sequence are as follows: S201: Based on the node trend characteristic factor, calculate the rate of change of temperature and humidity of each monitoring node in the continuous sampling period, determine the direction of change of temperature and humidity values ​​between adjacent time periods, compare the magnitude of temperature change and humidity change in the same period, and obtain the range of change magnitude. S202: Based on the range of changes, analyze the synchronicity between the temperature and humidity change trends and the ozone concentration change. According to the consistency of the temperature and humidity change directions with the ozone concentration change directions at each node, count the frequency of occurrence of the synchronization direction to obtain the trend coupling performance. S203: Based on the aforementioned trend coupling performance, select nodes with high frequency of synchronization direction, and sort them according to the coupling performance of each node with ozone concentration change in terms of the frequency and amplitude of synchronization performance to obtain the response priority sequence.

[0008] On the other hand, the specific steps of the regulation distribution unit are as follows: S301: Based on the response priority sequence, analyze the continuous changes in electrode surface voltage, discharge current density, and temperature and humidity of the nodes with higher ranking, compare the synchronous fluctuation of each data item of each node within the sampling period, determine the consistency of the fluctuation direction between each parameter, and obtain multi-parameter change data. S302: Based on the multi-parameter change data, analyze the relationship between the joint fluctuations of voltage, density, and temperature and humidity and the ozone concentration response, count the number of times ozone concentration changes occur synchronously when parameters are linked, analyze the impact of joint changes on ozone response, and obtain the linkage impact index. S303: Based on the aforementioned linkage influence index, select nodes with high synchronization rate in the trend direction, determine the correspondence between the coordinated fluctuation of each node and the ozone response performance, identify the key nodes, and obtain the distribution unit of the regulatory effect.

[0009] On the other hand, the relationship between the coordinated fluctuations of each node and the ozone response is determined using the following formula: ; Calculate the collaborative response offset value, identify the key nodes, and obtain the distribution unit of the regulatory effect, whereby... Representative node The collaborative response offset value, Representative node In time Temperature changes, Representative node In time Humidity changes, Representative node In time Changes in ozone concentration, This represents the total number of moments within the calculation period.

[0010] On the other hand, the specific steps for the coupling offset representation are as follows: S401: Based on the control effect distribution unit, analyze the high-frequency voltage data collected by the node in a continuous cycle, determine the peak and valley changes of voltage fluctuation in each cycle, compare the fluctuation amplitude and change trend between cycles, and statistically analyze the direction and distribution of voltage fluctuation at each node to obtain the fluctuation interval characteristic set. S402: Based on the fluctuation range characteristic set, compare it with the change direction of the node temperature and humidity data, analyze whether the temperature and humidity changes within the same period are consistent with the voltage fluctuation, determine the parameter fluctuation synchronization phenomenon, and count the synchronous change segments to obtain the synchronous response distribution. S403: Based on the synchronous response distribution, determine the offset trend of the combination of node temperature and humidity fluctuations and voltage changes, organize the node numbers and synchronous performance characteristics, analyze the relationship between the offset interval and temperature and humidity changes, and obtain the coupling offset performance quantity.

[0011] On the other hand, the relationship between the analytical offset interval and temperature and humidity changes is expressed by the formula: ; The coupling offset representation is obtained, where, Representing the Temperature, humidity and voltage coupling offset of nodes. Representing the The number of times a node is sampled within the statistical period. Representing the Node number Temperature data from the second sampling, Representing the Average temperature data of the node within the statistical period. Representing the Node number Humidity data from the second sampling, Representing the Average humidity data for nodes within the statistical period. Representing the Node number Voltage data from the next sample Representing the Average voltage data of the node within the statistical period.

[0012] On the other hand, the method also includes: S5: Based on the coupling offset performance, optimize the heat dissipation zone temperature and electrode structure humidity of the associated region, combine current density and ozone response, analyze the influence of temperature and humidity on load distribution, adjust the target power, and obtain the load distribution adjustment configuration; The load distribution adjustment configuration includes load adjustment configuration, target allocation ratio, and regional adjustment identifier.

[0013] On the other hand, the specific steps of the load distribution adjustment configuration are as follows: S501: Based on the coupling offset performance, optimize the temperature parameters of the heat dissipation area of ​​the surface discharge ozone device in the involved region, analyze the spatial distribution relationship between the temperature data of each node and the heat dissipation area of ​​the device, adjust the temperature distribution in the heat dissipation area, and obtain the thermal control regulation zone. S502: Based on the thermal control adjustment zone, adjust the humidity of the electrode unit structure combination components, combine the humidity change characteristics of each electrode structure in the environment, compare the distribution before and after the humidity adjustment, determine the influence of the combined change of humidity and temperature on the discharge current density, and obtain the energy flow characteristic factor. S503: Based on the energy flow characteristic factor and combined with the ozone response data collected by the nodes, determine the impact of regional temperature and humidity changes on the load distribution status of each node of the discharge device, optimize the power distribution of the target area, and obtain the load distribution adjustment configuration.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By normalizing the temperature, humidity, electrical, and ozone concentration data collected from the region, a data linkage mechanism centered on temperature and humidity changes is constructed. The response performance of nodes is dynamically sorted, and periodic data changes are integrated to form a control process that coordinates the adjustment of temperature, humidity, and electrical parameters. This proactively captures the high correlation between temperature and humidity deviations at each node and ozone production fluctuations, enabling real-time adjustment of zoned loads and dynamic optimization of energy distribution. This results in agile response and multi-regional difference matching capabilities for ozone generation regulation, improving stability under ozone production fluctuations and promoting efficient operation and low-consumption control under complex operating conditions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of steps S1 of the present invention; Figure 3 This is a flowchart of steps S2 of the present invention; Figure 4 This is a flowchart of steps S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a method for optimizing ozone control along surface discharge, such as... Figure 1 As shown, it includes the following steps: S1: Based on the discharge device, analyze the temperature, humidity, power supply voltage and ozone concentration data collected in each region, use proportional conversion for the temperature and humidity data of each node, optimize the control scale of temperature and humidity in each region, compare the optimized temperature and humidity data with the ozone concentration corresponding to the node, determine the linkage relationship between temperature and humidity changes in each region and ozone generation, calculate and summarize the trend changes, and obtain the node trend characteristic factors. S2: Based on the node trend characteristic factors, calculate the rate of change of temperature and humidity at each monitoring node, analyze the correlation between the trend of temperature and humidity change and the ozone concentration response, screen the nodes with strong correlation between temperature and humidity change and ozone concentration yield, determine the influence of temperature and humidity on ozone response at each node, arrange them from high to low according to correlation, select the nodes with the highest ranking, and obtain the response priority sequence. S3: Based on the response priority sequence, compare the continuous changes of electrode surface voltage, discharge current density and temperature and humidity in the top-ranked regions, analyze the combined effect of temperature, humidity, current density and voltage on ozone concentration changes, determine the influence of temperature, humidity and electrical parameter combination changes on ozone response, identify key nodes, and obtain the distribution unit of regulation effect. S4: Based on the distribution unit of the regulation effect, analyze the high-frequency voltage fluctuation data of each node, determine the change of voltage peak amplitude within the period, and then combine the node temperature data to analyze the humidity change, compare the correlation between temperature and humidity fluctuations and voltage anomalies, identify nodes with temperature and humidity coupling offset characteristics, and obtain the coupling offset performance quantity. S5: Based on the coupling offset performance, optimize the temperature parameters of the heat dissipation area of ​​the surface discharge ozone device in the involved area, then adjust the humidity of the electrode unit structure combination components, analyze the change of discharge current density, and combine the node ozone response to determine the influence of temperature and humidity on the load distribution status, optimize the power distribution of the target area, and obtain the load distribution adjustment configuration.

[0023] The node trend characteristic factors include trend intensity parameters, node association level, and change direction type; the response priority sequence includes priority response level, response node number, and hierarchical sorting identifier; the regulation effect distribution unit includes regulation node group, distribution weight coefficient, and effect category type; the coupling offset performance includes temperature and humidity offset amplitude, voltage fluctuation characteristics, and coupling performance label; and the load distribution adjustment configuration includes load adjustment configuration, target allocation ratio, and regional adjustment identifier.

[0024] In S1, the discharge device refers to the overall physical structure that performs the surface discharge reaction, typically including components such as the electrodes, insulators, and casing of the ozone generator. Its function is to provide a controllable electrical energy input and reaction environment, enabling air or oxygen to generate ozone under discharge conditions. Proportional conversion refers to normalizing, standardizing, or scaling the temperature and humidity data collected from each monitoring node according to the same mathematical proportional standard to eliminate baseline differences between different nodes and make the data comparable. Control scale refers to the range and magnitude used when adjusting parameters such as temperature and humidity, aiming to ensure that parameters in different areas reflect changes in control targets and actual operating conditions under a unified standard. A node refers to a monitoring point set at different locations on the surface discharge device. Nodes can collect physical parameters such as temperature, humidity, voltage, and ozone concentration in specific areas; correlation comparison refers to the corresponding analysis of optimized temperature and humidity data and ozone concentration data of the same area or different nodes, and the study of the impact of temperature and humidity changes on ozone generation through correlation analysis, trend comparison and other methods; linkage relationship refers to the correlation response between temperature and humidity changes and ozone concentration changes, such as the synchronous change trend of ozone generation when temperature and humidity increase or decrease, that is, the dynamic influence relationship between multiple parameters; trend change refers to the direction and magnitude of changes in temperature, humidity and ozone concentration data within a certain monitoring period or sampling time, which is often described by statistical analysis (such as slope, growth rate, rate of change, etc.).

[0025] In S2, nodes with strong correlation refer to monitoring points with high correlation (data showing a strong correlation) between the correlation coefficient between temperature and humidity change trends and ozone production rate changes. The temperature and humidity changes at these nodes are more sensitive to the impact on ozone production. The impact of temperature and humidity on ozone response refers to the degree and trend of ozone generation at a given node when temperature or humidity parameters change. Its influence is quantified through statistics and data analysis. The nodes ranked at the top are those that, after correlation calculation, are arranged from high to low according to the strength of the influence of temperature and humidity on ozone concentration. These are the nodes with the most significant impact and are the areas that require priority attention and treatment.

[0026] In S3, the top-ranked regions are the monitoring areas (nodes) that show the strongest correlation between temperature, humidity, and ozone response in the response priority sequence, and these regions usually have higher regulatory value. The combined effect of ozone concentration changes refers to the change in ozone concentration under the synergistic effect of changes in multiple parameters such as temperature, humidity, current density, and electrode surface voltage, i.e., the combined influence of multiple parameters on ozone formation. The influence on ozone response refers to the individual or combined changes in parameters such as temperature, humidity, current density, and electrode voltage that cause changes in the ozone formation rate and quantity, and this influence is reflected through data analysis. The key nodes are monitoring points that, after combined effect analysis, can have a sensitive response or dominant role in ozone formation under the synergistic changes of multiple parameters, and are often the focus of subsequent regulation.

[0027] In S4, the change in voltage peak amplitude refers to the maximum and minimum fluctuation amplitude of the node voltage signal within one cycle during high-frequency monitoring, reflecting the electrical strength and drastic changes during discharge. The correlation of voltage anomalies refers to the correlation analysis between temperature or humidity changes and voltage peak amplitude anomalies (such as abnormal increases or decreases) to determine whether temperature and humidity fluctuations are important influencing factors of voltage anomalies. The temperature and humidity coupling offset feature refers to the characteristic that the temperature and humidity change trends of certain nodes show synchronous or coordinated changes with voltage fluctuations within the monitoring cycle, and both deviate from the normal operating conditions. This feature is used to identify abnormal nodes that require key intervention.

[0028] In S5, the heat dissipation area of ​​the surface discharge ozone generator refers to the functional area in the surface discharge ozone generator structure used for heat dissipation. The temperature change in this area directly affects the stability of the discharge process and the ozone generation efficiency. The electrode unit structure assembly refers to the composite electrode assembly in the discharge device that undertakes the functions of discharge and conduction. Its surface state and discharge behavior can be indirectly controlled by adjusting the temperature and humidity. The change in discharge current density refers to the change of current flowing on the electrode surface per unit area over time, which is often used to characterize the discharge intensity and energy distribution. The node ozone response refers to the response of each monitoring node to the amount of ozone generated under certain temperature, humidity, and electrical conditions, which is reflected by the changes in sampling data. The impact on the load distribution state refers to the impact of changes in temperature and humidity parameters on the load distribution ratio and actual operating state of each area of ​​the discharge device during the optimization and control process, which is used to achieve energy distribution and efficiency adjustment.

[0029] like Figure 2 As shown, the specific steps for determining the node trend feature factor are as follows: S101: Based on the discharge device, analyze the temperature data, humidity data, power supply voltage data and ozone concentration data of each node, standardize the raw temperature and humidity data collected by each node, merge them in node order, and compare the data under the same standard to obtain a unified parameter baseline group. The system sequentially reads the raw temperature, humidity, power supply voltage, and ozone concentration values ​​collected from each node. For the temperature data, it extracts the maximum and minimum temperature values ​​for all nodes, records the difference between the node's raw temperature and the boundary value, and then normalizes this difference according to the temperature difference between the maximum and minimum values ​​to form a standard temperature value. The humidity data is normalized in the same way. After completion, the standard temperature and humidity values ​​of each node are merged into a set of parameter data. The data from all nodes are then combined into a standardized dataset with a unified format according to the node numbering order. In practical applications, for example, node 1 collects a temperature of 42.6°C and a humidity of 55%, while node 2 collects values ​​of 38.0°C and 47.5°C. The maximum temperature is 48.5°C, the minimum is 36.0°C, the maximum humidity is 60%, and the minimum is 40%. After standardization, the corresponding values ​​of node 1 and node 2 are adjusted to dimensionless values ​​between 0 and 1. Then, the numerical differences between the standardized data of each node are compared group by group. The difference difference is used for direct comparison. The temperature and humidity difference between adjacent nodes within the set range is marked as data consistent nodes. The upper limit of the difference tolerance is set to 0.5. If the difference between node pairs in both dimensions is less than the upper limit, they are considered to have similar data performance. On this basis, all node pairs that meet the conditions are extracted to form a unified parameter baseline group, which is used as the basic reference for the consistency of temperature and humidity status between nodes.

[0030] S102: Based on a unified parameter baseline group, compare the ozone concentration change data of each node, establish the correspondence between temperature and humidity and ozone concentration data group by group according to node number, determine the consistency between the direction of temperature and humidity change and the direction of ozone concentration change within the time difference period, calculate the number of times synchronous change occurs, and obtain the linkage trend characteristic quantity. For each node, ozone concentration data from the continuous time series of that node is extracted and compared with the standardized temperature and humidity data of that node. The direction of temperature change, humidity change, and ozone concentration change are compared synchronously within each time period. The numerical difference between any two consecutive time points is calculated to determine whether the trend of each parameter is upward or downward. If the three change directions are consistent, it is recorded as a linkage synchronization event; otherwise, it is ignored. Throughout the entire cycle, for example, sampling once per minute and observing continuously for 60 minutes, a total of 60 sets of time data are collected. If a node shows 24 instances of consistent three-direction changes, its linkage frequency is 40%. This frequency is compared with a set threshold, and the judgment standard is set to 35%. Nodes with a frequency equal to or higher than 35% are marked as trend linkage nodes. The total number of linkages and total number of samplings for that node are recorded. All nodes are summarized by number, and their frequency results and linkage status are combined to form a set of linkage trend characteristic quantities.

[0031] S103: Based on the linkage trend characteristic quantity, select nodes whose temperature and humidity are consistent with the ozone concentration direction, analyze the frequency and fluctuation amplitude of synchronous changes within each group of nodes, group them according to node number, and obtain node trend characteristic factors. Nodes with a frequency greater than or equal to 35% are selected for analysis. Temperature, humidity, and ozone concentration changes for each linked event are extracted from each node. The amplitude of these three data points for each event is recorded. The average amplitude of these amplitudes in the historical records of each node is calculated to reflect the strength of the node's fluctuation characteristics during linked events. In actual sampling, for example, if a node identifies 12 events with the same direction, the temperature change is recorded as between 0.5 and 1.8, the humidity change as between 2%RH and 6%RH, and the ozone concentration change as between 4 ppb and 9 ppb for each event. Finally, the average temperature, humidity, and ozone concentration fluctuation values ​​for each node are compiled according to the node number. Simultaneously, the linkage direction of the node is summarized and analyzed. If there are 8 positive changes and 4 negative changes, the node's change direction is identified as positive. The node number, frequency value, fluctuation amplitude, and change direction are combined to form a node trend characteristic factor, outputting three fields: trend strength parameter, node number, and change direction type, for subsequent response priority sequence generation analysis.

[0032] like Figure 3 As shown, the specific steps of the response priority sequence are as follows: S201: Based on the node trend characteristic factor, calculate the rate of change of temperature and humidity of each monitoring node in the continuous sampling period, determine the direction of change of temperature and humidity values ​​between adjacent time periods, compare the magnitude of temperature change and humidity change in the same period, and obtain the range of change magnitude. Extract the temperature and humidity values ​​for each node within a continuous sampling period. Following the time series, read the corresponding temperature values ​​from two adjacent time points and calculate their difference. Divide this difference by the time interval between the two sampling points to obtain the temperature change rate of that node within that time period. Similarly, read the humidity data from two consecutive time points and process it to obtain the humidity change rate. Perform this operation continuously at each node to form a list of time-series temperature and humidity change rates. With a sampling frequency of once per minute, for example, if node A's temperature is 42.0 at 10:00 and 42.7 at 10:01, the temperature change rate is 0.7 / minute. The humidity changes from 55.1% to 53.9% within the same time period, resulting in a humidity change rate of -1.2% / minute. Then, it is determined whether the temperature change direction between each pair of time points is rising, falling, or unchanged. Temperature rates greater than 0 are marked as rising, less than 0 as falling, and equal to 0 as unchanged. Similarly, the direction of humidity change is processed. After determining the direction, the change amplitude of temperature and humidity in each sampling period is compared and their absolute numerical difference is calculated. In the example above, the temperature change amplitude is 0.7 and the humidity change amplitude is 1.2, and the difference between the two is 0.5. By setting the amplitude threshold range, the change amplitude is divided into small amplitude (01), medium amplitude (12), and large amplitude (greater than 2), forming a uniform amplitude range label set in multiple node data, which is used as a reference dimension for the severity of temperature and humidity changes in subsequent analysis, thus obtaining the change amplitude range.

[0033] S202: Based on the range of change, analyze the synchronicity between the temperature and humidity change trends and the ozone concentration change. According to the consistency of the temperature and humidity change directions with the ozone concentration change directions at each node, count the frequency of occurrence of the synchronization direction to obtain the trend coupling performance. The temperature and humidity trends, along with the ozone concentration changes at each node within the same time period, are extracted one by one. The direction of change for these three parameters within the same sampling period is determined. First, the trends of temperature and humidity are combined to determine whether they are rising together, falling together, or in opposite directions. Then, the ozone concentration trend is compared with these trends. For example, if at a node between 10:15 and 10:16, the temperature rises by 0.6 ppb, humidity decreases by 1.3%, and ozone concentration decreases by 2.1 ppb, the temperature and humidity are in opposite directions, but ozone and humidity are both decreasing, while temperature is decreasing in the opposite direction; this is not considered a synchronization event. If the temperature rises by 0.5 ppb, humidity increases by 1.2%, and ozone concentration increases by 4.4 ppb, then the three parameters are in the same direction and are counted as a synchronization trend event. This process is repeated for each node. Within a time period, judgments are made and counts are performed. The total number of synchronous trend events is counted at each node, and the total number of sampling periods is recorded. The trend synchronization frequency of a node is calculated by dividing the number of synchronizations by the total number of samplings. For example, if a node has 22 events with the same direction of change in 60 samplings, its trend synchronization frequency is 36.7%. If the frequency value exceeds the 35% threshold, it is judged as a node with synchronous performance. By counting the trend synchronization frequencies of all nodes, a node synchronization frequency list is constructed. The list indicates the node number, the number of synchronizations, the total number of samplings, and the synchronization frequency value. Coupling strength level classification rules are set. For example, a frequency below 30% is marked as low coupling, 30% to 50% is medium coupling, and greater than 50% is high coupling, forming a set of trend coupling performance with level labels.

[0034] S203: Based on the trend coupling performance, select nodes with high frequency of synchronous performance, and sort them according to the coupling performance of each node with ozone concentration change in terms of the frequency and amplitude of synchronous performance to obtain the response priority sequence; All nodes are categorized by frequency level. First, nodes with frequencies below 30% are removed, retaining only those with frequencies of 30% or higher as a candidate set for response ranking. Within this candidate set, the trend synchronization frequency and ozone concentration variation amplitude of each synchronization event are extracted for each node. The average ozone fluctuation value of all synchronization events for each node is used as the amplitude reference. For example, node B has 20 synchronization events with ozone concentration variations ranging from 3.2 ppb to 9.1 ppb, resulting in an average amplitude of 6.4 ppb. The frequency and amplitude are then analyzed. Instead of performing sorting based on two dimensions, first sort by synchronization frequency from high to low, and then sort by average ozone change amplitude from high to low for nodes with the same frequency. Set the priority weight for frequency sorting to 70% and the weight for amplitude sorting to 30%. Sort the nodes by scoring using a linear weighting method. In this process, set the synchronization frequency sorting benchmark as the range of maximum and minimum values ​​within the node, dividing it into five levels. The amplitude sorting is done in the same way. Finally, sort and number the nodes according to the total score, and output the node number, synchronization frequency, average ozone change amplitude, and score value to obtain the response priority sequence, which is used as a reference for node sorting when regulating resource allocation.

[0035] like Figure 4 As shown, the specific steps of the regulatory distribution unit are as follows: S301: Based on the response priority sequence, analyze the continuous changes of electrode surface voltage, discharge current density and temperature and humidity of the nodes with higher ranking, compare the synchronous fluctuation of each data item of each node in the sampling period, judge the consistency of the fluctuation direction between each parameter, and obtain multi-parameter change data. Based on the node numbers ranked highest in the node priority sorting results, electrode surface voltage data, discharge current density data, and standardized temperature and humidity data are extracted one by one within their sampling period. For each node, a time series record of the four parameters is established, and each sampling point is numbered for subsequent period-by-period comparison. In voltage data processing, the voltage values ​​at adjacent time points are read, and the direction of voltage change is obtained. If the voltage at a later time point is higher than the previous time point, it is marked as an increase; otherwise, it is marked as a decrease. Equal values ​​indicate a stable state. The direction of current density change is determined similarly. Temperature and humidity data are processed using the same rules. Subsequently, in each sampling period, it is determined whether the directions of change of the above four parameters are consistent. If all four parameters are either increasing or all are decreasing, it is marked as a full-parameter synchronization event. If only three parameters are in the same direction, it is considered a partial synchronization event. For example, in a certain sampling period, the voltage rises from 6.1kV to 6.3kV, the current density rises from 1.25mA / cm² to 1.31mA / cm², the temperature rises from 43.5 to 44.1, and the humidity drops from 48% to 46%. Since the humidity direction is opposite, this period is recorded as a three-parameter partial synchronization event. Each node counts the number of four-parameter full synchronizations, three-parameter partial synchronizations, and two-parameter or lower synchronizations that occur throughout the entire period. At the same time, the amplitude range of each parameter change is recorded and classified. For example, voltage change within ±0.1kV is defined as small fluctuation, 0.8kV as medium fluctuation, and more than 0.8kV as large fluctuation. The fluctuation level of each data item is encoded and archived accordingly to obtain multi-parameter change data.

[0036] S302: Based on multi-parameter variation data, analyze the relationship between the joint fluctuations of voltage, density, temperature and humidity and ozone concentration response, count the number of times ozone concentration changes occur synchronously when parameters are linked, analyze the impact of joint changes on ozone response, and obtain linkage impact index. Extract the marked fully synchronized events and partially synchronized events within each node's cycle, and compare them with the ozone concentration data within the same cycle. In each cycle where the four parameters fluctuate consistently, read the corresponding ozone concentration value change and determine if a synchronized change has occurred compared to the previous cycle. If voltage, current density, temperature, and humidity all increase simultaneously, and the ozone concentration also increases, it is marked as a synchronized response event. If the parameters decrease and the ozone also decreases, it is also counted as a synchronized response event. If the direction of ozone change is inconsistent with the direction of the aforementioned parameters, it is not recorded. By counting the total number of synchronized responses for each node throughout all sampling cycles, the ozone response synchronization frequency of the node is calculated. For example, node 5 has a synchronization frequency of 60 sampling cycles. Eighteen full-parameter synchronization events were recorded, 12 of which were accompanied by ozone concentration fluctuations in the same direction, resulting in a linkage synchronization response frequency of 66.7%. The ozone concentration variation range during these 12 events was recorded, and the average value was calculated to assess the response intensity. For example, if the concentration variation range during the event was 5 ppb to 11 ppb, the average variation range was 7.3 ppb. A dataset of linkage event counts and average response ranges was established for each node. At the same time, a range division standard for synchronization response frequency was set: frequency below 30% was considered weak linkage, 30% to 60% was considered medium linkage, and above 60% was considered strong linkage. The linkage level, synchronization frequency, and average response range of each node were used as evaluation dimensions to form a set of linkage impact indicators.

[0037] S303: Based on the linkage impact index, nodes with high synchronization rate in the direction of performance trend are screened, the correspondence between the coordinated fluctuation of each node and the ozone response performance is determined, key nodes are identified, and the distribution unit of the regulatory effect is obtained. The formula used to determine the correlation between coordinated fluctuations at each node and ozone response performance is: ; Calculate the collaborative response offset value, identify the key nodes, and obtain the distribution unit of the regulatory effect, whereby... Representative node The collaborative response offset value, Representative node In time The temperature change is calculated by dividing the actual change by the difference between the maximum and minimum temperature values ​​within the monitoring period of that node. Representative node In time The humidity change is calculated by dividing the actual change by the difference between the maximum and minimum humidity values ​​within the monitoring period of that node. Representative node In time The change in ozone concentration is calculated by dividing the actual change by the difference between the maximum and minimum ozone concentrations within the monitoring period at that node. Represents the total number of moments within the calculation period; The synergistic response offset refers to the overall difference and linkage between the normalized change trends of temperature and humidity at a certain node and the normalized change of ozone concentration at the same node within the monitoring period. It is used to measure the synchronization and offset of multiple parameters (temperature, humidity and ozone response) at a node in the dynamic process. It can help analyze and identify the synergistic relationship and response characteristics of different monitoring nodes under the change of multiple parameters, and facilitate the screening and identification of nodes that are critical under the linkage of multiple parameters and need to be focused on or controlled.

[0038] Select Node As an example, during the observation period (In hourly increments), the temperature values ​​collected by the nodes were 26.2, 26.8, 27.5, 28.0, 27.3, and 26.9°C, respectively; the humidity values ​​were 45, 47, 49, 52, 50, and 46%RH, respectively; and the ozone concentration values ​​were 0.038, 0.041, 0.045, 0.044, 0.043, and 0.040 mg / m³, respectively. 3 After obtaining the hourly change, it is normalized to obtain the normalized change values ​​from the 2nd hour to the 6th hour as follows: , , , , ; The corresponding normalized humidity changes are as follows: , , , , ; The corresponding normalized change in ozone concentration is: , , , , ; Substitute the above values ​​into the formula: ; Calculate the results within the parentheses at each time point in sequence: 2nd hour: ; 3rd hour: ; 4th hour: ; 5th hour: ; 6th hour: ; Summing the above results: ; Substituting into the formula, we get: ; denominator This means that the average value is taken over five observation intervals from time point 2 to time point 6. This structure places all parameters under the same dimension to complete a unified summation operation, ensuring the feasibility and consistency of the overall calculation logic. The obtained numerical result of 0.0496 is a quantitative reflection of the degree of multi-parameter coordinated offset within the current period of the node. If the result is below the preset offset threshold of 0.1, it can be considered that the coordinated trend of the node is relatively consistent. The formula completes the structural form of addition and subtraction combination to calculate the average after normalization of temperature, humidity and ozone concentration. It can form a unified quantitative expression of multi-source heterogeneous data that originally did not have direct addition and subtraction without relying on the algorithm model, so that the results have continuous comparability and the ability to sort between nodes, thereby supporting the subsequent determination and screening process of the distribution unit of the regulatory effect.

[0039] like Figure 5 As shown, the specific steps for the coupling offset representation are as follows: S401: Based on the distribution unit of the regulation effect, analyze the high-frequency voltage data collected by the node in a continuous cycle, determine the peak and valley changes of voltage fluctuation in each cycle, compare the fluctuation amplitude and change trend between cycles, and statistically analyze the direction and distribution of voltage fluctuation at each node to obtain the fluctuation interval characteristic set. Extract the high-frequency voltage raw data of each node within the distribution unit during continuous sampling periods. Read the data sequence of all voltage sampling points in each period, identify the maximum and minimum voltage values ​​within that period and label them as peak and valley values ​​respectively. Calculate the difference between the peak and valley values ​​to obtain the voltage fluctuation amplitude within that period. Simultaneously, record the position difference between the maximum and minimum points within the period to determine the periodic trend of the fluctuation. Repeat the above process for multiple consecutive periods to form a fluctuation amplitude sequence for each node over multiple periods. Then compare the fluctuation amplitudes of each period, and calculate the difference between the fluctuation values ​​of two adjacent periods. A positive difference indicates increased fluctuation, a negative difference indicates decreased fluctuation, and a zero difference indicates a flat fluctuation trend. For example, for a node in 3 consecutive periods... With peak-to-valley differences of 0.8kV, 1.2kV, and 0.9kV respectively, the fluctuations in cycles 1 to 2 are stronger, while those in cycles 2 to 3 are weaker. Further statistics are compiled on the number of times each node experiences increased, decreased, and stable fluctuations across all cycles, and these are archived as percentages. A range standard is set for voltage fluctuation amplitude: fluctuations less than 0.5kV are labeled "low range," 0.5kV to 1.0kV are labeled "medium range," and those above 1.0kV are labeled "high range." Following this rule, the fluctuation amplitude within each cycle is categorized into range types, and a fluctuation amplitude range list is formed using the node number as an index. Combined with fluctuation trend statistics, the periodic voltage fluctuations of each node are represented using structured data, forming a fluctuation range characteristic set.

[0040] S402: Based on the characteristic set of the fluctuation range, compare the change direction with the temperature and humidity data of the nodes, analyze whether the temperature and humidity changes within the same period are consistent with the voltage fluctuation, determine the synchronous phenomenon of parameter fluctuation, and count the synchronous change segments to obtain the synchronous response distribution. For each node, the voltage fluctuation direction and amplitude range are compared cycle by cycle. Temperature and humidity data collected by that node within the corresponding cycle are extracted, and the direction of temperature and humidity change is calculated. Within each cycle, it is determined whether the temperature and humidity values ​​have increased, decreased, or remained the same compared to the previous cycle, and the direction of change is encoded accordingly: temperature increase is set to 1, decrease to -1, and remaining the same to 0. The same processing is applied to humidity. Then, the direction of temperature and humidity change is compared with the direction of voltage fluctuation. If the voltage fluctuation is increasing and both temperature and humidity are increasing, it is a fully synchronized rising segment; if the voltage fluctuation is decreasing and both temperature and humidity are decreasing, it is a fully synchronized falling segment; the rest are asynchronous or partially synchronized segments. After defining each synchronization type, the number of synchronization cycles for each node is counted across all cycles, and the total number of synchronization cycles is divided by the total number of all sampling cycles. The proportion of synchronization cycles is calculated. For example, if node C has 18 fully synchronized cycles and 10 partially synchronized cycles out of 50 cycles, then the proportion of fully synchronized cycles is 36% and the proportion of partially synchronized cycles is 20%. Based on this, the response judgment criteria are set as follows: a proportion of fully synchronized cycles exceeding 30% is considered a medium synchronization response, exceeding 50% is considered a strong synchronization response, and a proportion of synchronized cycles below 15% is considered a weak response node. At the same time, auxiliary judgment is performed by combining the fluctuation range and the temperature and humidity change range. For example, when the voltage fluctuation is in the "high range", a node that synchronizes with the temperature fluctuation greater than 1 and the humidity change range exceeding 3%RH more than 10 times is identified as a highly coupled and strongly synchronized point. The synchronization response distribution of each node is collected and statistically analyzed to generate a synchronization response distribution consisting of indicators such as the number of synchronization cycles, the proportion of synchronization types, and the maximum synchronization segment amplitude.

[0041] S403: Based on the synchronous response distribution, determine the offset trend of the combination of node temperature and humidity fluctuations and voltage changes, organize the node numbers and synchronous performance characteristics, analyze the relationship between the offset interval and temperature and humidity changes, and obtain the coupled offset performance. The relationship between the offset interval and temperature and humidity changes was analyzed using the following formula: ; The coupling offset representation is obtained, where, Representing the Temperature, humidity and voltage coupling offset of nodes. Representing the The number of times a node is sampled within the statistical period. Representing the Node number Temperature data from the second sampling, Representing the Average temperature data of the node within the statistical period. Representing the Node number Humidity data from the second sampling, Representing the Average humidity data for nodes within the statistical period. Representing the Node number Voltage data from the next sample Representing the Average voltage data of the node within the statistical period; Coupling offset performance refers to the quantity of coupling offset within the monitoring period. The synchronous fluctuation combination characteristics between node temperature, humidity, and voltage data and their respective periodic average data are weighted and summed after absolute value processing. This reflects the comprehensive performance of the node's temperature and humidity changes and voltage changes deviating from the average level in the same period. It belongs to quantitative data that reflects the amplitude and direction of the joint changes of multiple parameters.

[0042] Each monitoring node identified as exhibiting a synchronization offset trend is extracted. Five sampling data points for temperature, humidity, and voltage within a set statistical period are then categorized for each node. Taking node k as an example, its original temperature data are 31.2, 32.0, 30.8, 31.5, and 31.7; its original humidity data are 48.3, 49.1, 47.6, 48.8, and 48.5; and its original voltage data are 212.5, 213.2, 211.9, 212.8, and 213.0. First, the original temperature, humidity, and voltage data are normalized to unify their dimensions, resulting in normalized values. They are respectively: 0.44, 0.71, 0.33, 0.56, 0.61; They are respectively: 0.47, 0.74, 0.36, 0.63, 0.56; They are respectively: 0.39, 0.72, 0.22, 0.56, 0.67; The corresponding average values ​​are as follows: , , ; According to the formula, for to The data sets are expanded sequentially, and the calculations are as follows: Group 1 is: ; Take the absolute value as 0.05.

[0043] Group 2 is: ; Take the absolute value as 0.16.

[0044] Group 3 is: ; Take the absolute value as 0.1.

[0045] Group 4 is: ; Take the absolute value as 0.06.

[0046] Group 5 is: ; Take the absolute value as 0.07.

[0047] Sum and average the above 5 sets of data to get: ; This result indicates that the first The coupling offset performance value obtained by the node after normalizing the temperature, humidity, and electrical data and performing a combined difference assessment within the current monitoring period is 0.088. Combined with the offset classification criteria, if the following is set... If the offset is slight, the node is classified as a type of response-coupled node and will be uniformly labeled in the node number set. Simultaneously, the temperature and humidity fluctuation trends within its sampling period are compared. If they are synchronized with the voltage fluctuation direction, the node is included in the synchronous covariance group and classified under positive coupling characteristics, thus completing the identification and organization of the corresponding coupling offset performance. The formula constructs a combined model of temperature, humidity, and voltage difference, simultaneously considering the average differences of the three parameters. The three terms in parentheses correspond to the degree of parameter deviation. Absolute value processing is then used to eliminate offset direction interference. After summing and normalization, the comprehensive coupling offset strength of the node's physical field parameters under different sampling periods can be obtained. Indicates the first All nodes The systematic statistical coverage of the group sampling, the additive term in the operation structure reflects the trend of temperature and humidity jointly enhancing or weakening, the subtraction term is used to remove the voltage disturbance component, and finally divided by The average normalization process produces a uniform-scale offset representation.

[0048] like Figure 6 As shown, the specific steps for load balancing and configuration are as follows: S501: Based on the coupling offset performance, optimize the temperature parameters of the heat dissipation area of ​​the surface discharge ozone device in the involved region, analyze the spatial distribution relationship between the temperature data of each node and the heat dissipation area of ​​the device, adjust the temperature distribution in the heat dissipation area, and obtain the thermal control regulation zone. Extract node temperature change data and their corresponding electrical fluctuation performance labels, read the physical structure coordinate parameters of the heat dissipation area in the surface discharge ozone device, determine the spatial distribution position of each node relative to the heat dissipation structure, and map the node temperature measurement values ​​onto the two-dimensional planar diagram of the heat dissipation structure according to spatial coordinates. Based on the differences in the measured temperature values, a thermal field distribution map is formed. The number of nodes with temperatures higher than 45°C and the area of ​​the region are counted and identified as hot spots. Areas with temperatures lower than 38°C are identified as cold spots, and the intermediate range is identified as transition zones. The zones are set according to the above temperature range standards. The impact of the temperature of the zone on the stability of ozone generation electrical parameters is comprehensively evaluated by the spatial overlap of hot spots and node density. For example, if a hot spot area covers 5 highly coupled nodes and ozone concentration fluctuates abnormally for 5 consecutive cycles, the area is identified as a key control area. Subsequently, the heat dissipation unit is adjusted, such as increasing the air duct speed or changing the fan speed. After the adjustment, the temperature redistribution is monitored. If the area of ​​the hot spot area decreases by more than 30% and the node temperature stabilizes within the range of 40±1°C within 3 cycles, the adjustment is confirmed to be effective, and the area is marked as a thermal control adjustment zone, forming a standardized thermal control adjustment zone configuration.

[0049] S502: Based on thermal control regulation zones, adjust the humidity of the electrode unit structure components, combine the humidity change characteristics of each electrode structure in the environment, compare the distribution before and after humidity adjustment, judge the influence of humidity and temperature combination changes on discharge current density, and obtain energy flow characteristic factors. The system sequentially identifies each electrode structure unit within the adjustment area, extracts humidity measurement data corresponding to its environment, and classifies them into edge, central, and heat dissipation concentration zones based on their location. In each zone, it records the humidity variation range and daily fluctuation frequency. Based on the adjustment target, it controls the output rate of the humidity control device and the operating cycle of the humidifier or dehumidifier. For example, if the humidity in the edge zone remains below 40% for an extended period, the target is raised to 45%–50% by extending the device's operating time from 5 minutes to 7 minutes to control humidification. After adjustment, the system records the stable humidity value and compares the fluctuation range before and after adjustment. If the fluctuation... If the humidity adjustment converges from ±6% to ±3%, it is considered effective. Based on this, the change in discharge current density is monitored, and the direction of change of current value per unit area of ​​the electrode is statistically analyzed to see if it is consistent with the direction of humidity change. If the current density increases after the humidity increases, it is considered positive coupling; if it decreases, it is considered negative coupling. The average change in current density before and after the adjustment of each electrode structure is calculated and summarized in combination with the direction of humidity change. For example, if the humidity of a certain structure increases by 3.5%, the current density increases by 0.18 mA / cm², which is classified as a high-response coupling point. The amount of humidity change, the amount of current density change, and the direction of change are organized according to the node number to generate energy flow characteristic factors.

[0050] S503: Based on the energy flow characteristic factor and combined with the ozone response data collected by the nodes, the influence of regional temperature and humidity changes on the load distribution status of each node of the discharge device is determined, the power distribution in the target area is optimized, and the load distribution adjustment configuration is obtained. First, the current density variation of each electrode structural unit under changes in humidity and temperature is correlated with the ozone response data of its corresponding node. Ozone concentration values ​​for each node under different environmental combinations are extracted to determine the synchronization between node response intensity and temperature and humidity changes. If, after adjustment, the temperature decreases by 2 and the humidity increases by 4%, the ozone concentration at the corresponding node increases from 102 ppb to 116 ppb, this is recorded as a positive enhancement response event. The number of such events is accumulated for all nodes, and the ozone response corresponding to different adjustment combinations is categorized. The total response intensity of each node is quantified and scored, and an influence matrix is ​​established based on each temperature and humidity combination and the resulting ozone concentration change. Based on this, the load distribution status is analyzed, the total power required by each node before and after adjustment is compared, and the power consumption per unit of ozone generation is calculated. If the power consumption per unit of ozone in a certain area decreases by more than 15% after temperature and humidity adjustment, it is determined to be an effective load adjustment area. Based on this, all adjustment areas with the best power response are selected, the power distribution ratio of each area is calculated, and the power supply strategy is adjusted to prioritize the allocation of power resources to areas with better response. For example, the original power ratio of area 1 is adjusted from 25% to 32%, and that of area 3 is adjusted from 30% to 24%, forming a load distribution adjustment configuration containing the area number, the power ratio before and after adjustment, the temperature and humidity combination status, and the power consumption per unit of production capacity.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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 method for optimizing ozone control along surface discharge, characterized in that, The method includes: S1: Based on the discharge device, collect temperature, humidity, voltage and ozone concentration data of each region, standardize the temperature and humidity ratios, perform correlation analysis between the optimized parameters and ozone concentration, summarize the trend changes of each region, and obtain the node trend characteristic factors. S2: Based on the node trend characteristic factors, calculate the rate of change of temperature and humidity at each monitoring point, analyze its correlation with ozone concentration, screen highly correlated nodes, determine the intensity of influence and prioritize them to obtain the response priority sequence. S3: Based on the response priority sequence, compare the electrode voltage, discharge current density and temperature and humidity data of the top-ranked regions, analyze the ozone change characteristics under multi-parameter linkage, identify key nodes, and obtain the distribution unit of the regulation effect. S4: Based on the distribution unit of the regulation effect, analyze the high-frequency voltage fluctuation of the node, determine the voltage peak amplitude change, and combine the node temperature and humidity changes to compare their correlation with voltage anomalies, identify coupling offset characteristics, and obtain the coupling offset performance quantity.

2. The surface discharge ozone control optimization method according to claim 1, characterized in that, The node trend characteristic factors include trend intensity parameters, node association level, and change direction type; the response priority sequence includes priority response level, response node number, and hierarchical sorting identifier; the regulation effect distribution unit includes regulation node group, distribution weight coefficient, and effect category type; and the coupling offset performance includes temperature and humidity offset amplitude, voltage fluctuation characteristics, and coupling performance label.

3. The surface discharge ozone control optimization method according to claim 1, characterized in that, The specific steps for the node trend feature factor are as follows: S101: Based on the discharge device, analyze the temperature data, humidity data, power supply voltage data and ozone concentration data of each node, standardize the raw temperature and humidity data collected by each node, merge them in node order, and compare the data under the same standard to obtain a unified parameter baseline group. S102: Based on the unified parameter baseline group, compare it with the ozone concentration change data of each node, establish the correspondence between temperature and humidity and ozone concentration data group by group according to node number, determine the consistency between the direction of temperature and humidity change and the direction of ozone concentration change within the time difference period, calculate the number of times synchronous change occurs, and obtain the linkage trend characteristic quantity. S103: Based on the aforementioned linkage trend characteristic, select nodes whose temperature, humidity and ozone concentration are in the same direction, analyze the frequency and fluctuation amplitude of synchronous changes within each group of nodes, group them according to node number, and obtain node trend characteristic factors.

4. The surface discharge ozone control optimization method according to claim 1, characterized in that, The specific steps of the response priority sequence are as follows: S201: Based on the node trend characteristic factor, calculate the rate of change of temperature and humidity of each monitoring node in the continuous sampling period, determine the direction of change of temperature and humidity values ​​between adjacent time periods, compare the magnitude of temperature change and humidity change in the same period, and obtain the range of change magnitude. S202: Based on the range of changes, analyze the synchronicity between the temperature and humidity change trends and the ozone concentration change. According to the consistency of the temperature and humidity change directions with the ozone concentration change directions at each node, count the frequency of occurrence of the synchronization direction to obtain the trend coupling performance. S203: Based on the aforementioned trend coupling performance, select nodes with high frequency of synchronization direction, and sort them according to the coupling performance of each node with ozone concentration change in terms of the frequency and amplitude of synchronization performance to obtain the response priority sequence.

5. The surface discharge ozone control optimization method according to claim 1, characterized in that, The specific steps of the regulation distribution unit are as follows: S301: Based on the response priority sequence, analyze the continuous changes in electrode surface voltage, discharge current density, and temperature and humidity of the nodes with higher ranking, compare the synchronous fluctuation of each data item of each node within the sampling period, determine the consistency of the fluctuation direction between each parameter, and obtain multi-parameter change data. S302: Based on the multi-parameter change data, analyze the relationship between the joint fluctuations of voltage, density, and temperature and humidity and the ozone concentration response, count the number of times ozone concentration changes occur synchronously when parameters are linked, analyze the impact of joint changes on ozone response, and obtain the linkage impact index. S303: Based on the aforementioned linkage influence index, select nodes with high synchronization rate in the trend direction, determine the correspondence between the coordinated fluctuation of each node and the ozone response performance, identify the key nodes, and obtain the distribution unit of the regulatory effect.

6. The surface discharge ozone control optimization method according to claim 5, characterized in that, The formula used to determine the correlation between the coordinated fluctuations of each node and the ozone response is: ； Calculate the collaborative response offset value, identify the key nodes, and obtain the distribution unit of the regulatory effect, whereby... Representative node The collaborative response offset value, Representative node In time Temperature changes, Representative node In time Humidity changes, Representative node In time Changes in ozone concentration, This represents the total number of moments within the calculation period.

7. The surface discharge ozone control optimization method according to claim 1, characterized in that, The specific steps for the coupling offset representation are as follows: S401: Based on the control effect distribution unit, analyze the high-frequency voltage data collected by the node in a continuous cycle, determine the peak and valley changes of voltage fluctuation in each cycle, compare the fluctuation amplitude and change trend between cycles, and statistically analyze the direction and distribution of voltage fluctuation at each node to obtain the fluctuation interval characteristic set. S402: Based on the fluctuation range characteristic set, compare it with the change direction of the node temperature and humidity data, analyze whether the temperature and humidity changes within the same period are consistent with the voltage fluctuation, determine the parameter fluctuation synchronization phenomenon, and count the synchronous change segments to obtain the synchronous response distribution. S403: Based on the synchronous response distribution, determine the offset trend of the combination of node temperature and humidity fluctuations and voltage changes, organize the node numbers and synchronous performance characteristics, analyze the relationship between the offset interval and temperature and humidity changes, and obtain the coupling offset performance quantity.

8. The surface discharge ozone control optimization method according to claim 7, characterized in that, The relationship between the analysis offset interval and temperature and humidity changes is expressed by the following formula: ； The coupling offset representation is obtained, where, Representing the Temperature, humidity and voltage coupling offset of nodes. Representing the The number of times a node is sampled within the statistical period. Representing the Node number Temperature data from the second sampling, Representing the Average temperature data of the node within the statistical period. Representing the Node number Humidity data from the second sampling, Representing the Average humidity data for nodes within the statistical period. Representing the Node number Voltage data from the next sample Representing the Average voltage data of the node within the statistical period.

9. The surface discharge ozone control optimization method according to claim 1, characterized in that, The method further includes: S5: Based on the coupling offset performance, optimize the heat dissipation zone temperature and electrode structure humidity of the associated region, combine current density and ozone response, analyze the influence of temperature and humidity on load distribution, adjust the target power, and obtain the load distribution adjustment configuration; The load distribution adjustment configuration includes load adjustment configuration, target allocation ratio, and regional adjustment identifier.

10. The surface discharge ozone control optimization method according to claim 9, characterized in that, The specific steps for configuring load distribution adjustment are as follows: S501: Based on the coupling offset performance, optimize the temperature parameters of the heat dissipation area of ​​the surface discharge ozone device in the involved region, analyze the spatial distribution relationship between the temperature data of each node and the heat dissipation area of ​​the device, adjust the temperature distribution in the heat dissipation area, and obtain the thermal control regulation zone. S502: Based on the thermal control adjustment zone, adjust the humidity of the electrode unit structure combination components, combine the humidity change characteristics of each electrode structure in the environment, compare the distribution before and after the humidity adjustment, determine the influence of the combined change of humidity and temperature on the discharge current density, and obtain the energy flow characteristic factor. S503: Based on the energy flow characteristic factor and combined with the ozone response data collected by the nodes, determine the impact of regional temperature and humidity changes on the load distribution status of each node of the discharge device, optimize the power distribution of the target area, and obtain the load distribution adjustment configuration.

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