Intelligent control system for platinum nanoscale surface treatment

Abstract

The application relates to the technical field of platinum surface treatment, and discloses a platinum nanoscale surface treatment intelligent control system.The system comprises an energy distribution regulation and control module, an electric field environment optimization module, an ion dynamic adjustment module, a treatment exception intervention module and a surface treatment quality improvement module.The energy distribution regulation and control module analyzes the energy distribution and the thermal uniformity adaptation degree, and generates an energy distribution control parameter set; the electric field environment optimization module adjusts the cavity electric field distribution balance, and generates an electric field regulation and optimization parameter set; the ion dynamic adjustment module adjusts the ion input rate and the flow rate path distribution proportion, and generates an ion dynamic regulation and control result; the treatment exception intervention module extracts the real-time temperature fluctuation rate and the electric field offset, and generates an exception intervention adjustment data set; and the surface treatment quality improvement module adjusts the target treatment path parameters, and generates a platinum target surface treatment data table.The system realizes accurate regulation and control of platinum nanoscale surface treatment, and improves the treatment effect and stability.

Description

Technical Field

[0001] This invention relates to the field of platinum surface treatment technology, specifically to an intelligent control system for platinum nanoscale surface treatment. Background Technology

[0002] In the processing of platinum materials, surface modification technology has always been a key focus in the industry. Due to its unique physicochemical properties, platinum is widely used in high-end fields such as precision instruments, electronic components, and catalytic reaction devices. These applications place extremely high demands on the uniformity, stability, and performance parameters of the platinum surface. Traditional platinum surface treatment methods often rely on manual experience or semi-automated equipment for control. During the process, the precision of controlling key parameters such as energy distribution, electric field environment, and ion dynamics is often difficult to guarantee.

[0003] In existing technologies, platinum surface treatment equipment often suffers from uneven energy output distribution. During the treatment process, the lack of a real-time dynamic adaptation mechanism between the equipment's operating energy parameters and the platinum surface temperature gradient easily leads to excessive energy concentration or insufficiency in localized areas, resulting in excessive temperature fluctuations on the platinum surface and affecting the surface modification effect. Simultaneously, the control of the electric field environment within the equipment cavity also has significant limitations. The uniformity of the electric field intensity distribution is difficult to control, and fluctuations in the power output of the electric field device directly cause instability in the adsorption and migration processes of ions on the platinum surface, leading to deviations in ion concentration distribution and ultimately reducing the uniformity of the platinum surface treatment.

[0004] The dynamic adjustment of ions also presents numerous challenges. Traditional ion control methods often employ fixed parameter settings, failing to adapt flexibly to real-time changes in the electric field and the platinum surface condition. This leads to deviations in the adsorption amount and migration path of ions on the platinum surface, consequently affecting the rate and quality of surface modification. Furthermore, the intervention mechanism for abnormal situations during processing is inadequate. When temperature fluctuations or electric field shifts occur, existing systems struggle to respond quickly and make precise adjustments, often resulting in localized over- or under-treatment of the platinum surface, impacting the performance of the final product.

[0005] With the development of nanotechnology, the precision requirements for platinum surface treatment are increasing, and the limitations of traditional treatment methods in energy distribution, electric field control, and dynamic ion adjustment are becoming increasingly apparent. For example, in the energy distribution process, existing equipment struggles to dynamically adapt to the real-time temperature gradient and ion flow path velocity of the platinum surface, resulting in a low degree of matching between energy distribution and thermal uniformity. Regarding electric field environment optimization, the electric field strength and gradient changes within the equipment cavity often fluctuate significantly due to unstable power output, affecting the ion trajectory on the platinum surface. In the dynamic ion adjustment stage, the adsorption and migration patterns of ions are difficult to effectively capture and control, causing deviations between the ion concentration distribution trend and the target requirements.

[0006] The mechanisms for handling anomalies during the processing also have significant shortcomings. When the surface temperature of platinum fluctuates or the electric field shifts, existing systems often only offer simple shutdowns or crude parameter adjustments, failing to achieve precise intervention while ensuring processing continuity. This not only affects processing efficiency but may also lead to material waste. Furthermore, the evaluation and improvement of surface treatment quality lack a systematic parameter adjustment mechanism, making it difficult to dynamically optimize the target processing path based on real-time data during the process. Consequently, the final processing results often fall short of expectations. These problems severely restrict the precision, efficiency, and stability of platinum surface treatment, failing to meet the stringent requirements of high-end applications for platinum material surface performance. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent control system for platinum nanoscale surface treatment to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a platinum nanoscale surface treatment intelligent control system, the system comprising:

[0009] The energy distribution and control module, based on the operating status information of the platinum surface treatment equipment, calls the equipment's energy output parameters, platinum surface temperature gradient, and ion flow path velocity data, analyzes the degree of adaptation between energy distribution and thermal uniformity, allocates equipment energy control values ​​and ion flow equalization parameters, and generates a set of energy distribution control parameters.

[0010] Based on the energy distribution control parameter set, the electric field environment optimization module extracts the electric field intensity value and gradient change in the equipment cavity, analyzes the impact of the power output of the electric field device on stability, adjusts the uniformity of the electric field distribution in the cavity, and generates an electric field regulation optimization parameter set.

[0011] The ion dynamic adjustment module analyzes the adsorption and migration of ions on the platinum surface based on the electric field control optimization parameter set, adjusts the ion input rate and flow path distribution ratio, redistributes the distribution trend and dynamic parameter values ​​of ion concentration, and generates ion dynamic control results.

[0012] Based on the ion dynamic control results, the abnormal intervention module extracts the real-time temperature fluctuation rate and electric field offset during the platinum surface treatment process, analyzes the influence of the fluctuation range on the platinum surface modification rate, dynamically adjusts the ion distribution path and temperature field ratio within the target range, and generates an abnormal intervention adjustment dataset.

[0013] The surface treatment quality improvement module analyzes the distribution ratio and processing time of the platinum target surface based on the abnormal intervention adjustment dataset, adjusts the parameters of the target processing path, and generates a platinum target surface treatment data table.

[0014] Preferably, the step of obtaining the degree of fit between the energy distribution and thermal uniformity specifically includes:

[0015] Based on the operating status information of the platinum surface treatment equipment, the energy output parameters, gradient data and ion flow path velocity data of the equipment are extracted. A time window is set, and time points are selected to match the data. By comparing the data correlation and filtering the data, the energy output parameters and gradient data are obtained.

[0016] Based on the energy output parameters and gradient data, the path is matched and verified, the difference between energy and gradient is calculated, the energy distribution and gradient distribution are corrected by combining the flow velocity change, and the path parameters are adjusted by the influence of flow velocity on the data to obtain the energy and gradient matching situation.

[0017] Based on the energy and gradient matching, thermal uniformity analysis is performed, thermal uniformity analysis standards are set, and the energy distribution under different flow rates is evaluated in combination with the dynamic changes in equipment operation. The uniformity index is compared and the flow rate conditions are optimized to obtain the degree of fit between energy distribution and thermal uniformity.

[0018] Preferably, the step of obtaining the energy distribution control parameter set specifically includes:

[0019] Based on the degree of adaptation between the energy distribution and thermal uniformity, the energy transmission and uniformity changes of the equipment under differentiated operating conditions are analyzed, and the energy distribution of the equipment is weighted and calculated to obtain the preliminary energy regulation requirements of the equipment.

[0020] Based on the initial energy regulation requirements of the equipment, the energy balance between the equipment is analyzed, the relationship between energy transmission efficiency and load distribution between the equipment is identified, and the energy regulation parameters of the equipment are corrected.

[0021] By combining the energy regulation dataset between devices with the thermal uniformity adaptation results, the energy between devices is allocated, optimized, and matched to the required balance and uniformity requirements, resulting in a set of energy distribution control parameters.

[0022] Preferably, the steps for obtaining the electric field strength value and gradient change within the device cavity are as follows:

[0023] Based on the energy distribution control parameter set, temperature data inside the equipment cavity is extracted, temperature points within each time period are selected, and temperature fluctuations are analyzed by combining the temperature change trends at different locations inside the cavity to obtain temperature data inside the equipment cavity.

[0024] Based on the temperature data inside the equipment cavity, the electric field strength value of each temperature point is calculated. By analyzing the relationship between temperature and electric field, the change in electric field at each measurement point is identified. Combined with the equipment structural parameters, the electric field changes at different locations are compared to obtain electric field distribution and gradient distribution data.

[0025] Based on the electric field distribution and gradient distribution data, the overall electric field distribution inside the equipment cavity is analyzed, the electric field gradient is optimized by combining temperature data, the impact of electric field changes on equipment performance is analyzed, the stable electric field configuration under differentiated operating conditions is determined, and the electric field strength value and gradient change amount inside the equipment cavity are obtained.

[0026] Preferably, the step of obtaining the electric field control optimization parameter set specifically includes:

[0027] Based on the electric field intensity value and gradient change in the cavity of the device, the time series of electric field change is determined, the current electric field intensity value is compared with the original electric field data, the electric field gradient at each moment is analyzed, and corresponding thresholds are defined according to the device state partition to generate a preliminary electric field change parameter set.

[0028] The preliminary electric field change parameter set is analyzed to analyze the influence of the electric field inside the cavity on the stability of the device's power output and to identify the correlation between the electric field and the power output.

[0029] By analyzing the power stability influence coefficient of the aforementioned section and combining it with the cavity electric field variation parameters, the electric field distribution uniformity is adjusted, the electric field control data is optimized, and an electric field control optimization parameter set is generated.

[0030] Preferably, the steps for obtaining the ion dynamic regulation results are as follows:

[0031] Based on the electric field modulation optimization parameter set, ion adsorption data on the platinum surface is extracted, the adsorption rate of ions on the surface of different materials is monitored, and the migration characteristics of ions are inferred by combining external environmental factors such as time and temperature. Adsorption and migration rate coefficients are defined, and an adsorption and migration dynamic parameter set is generated.

[0032] The influence of the adsorption and migration dynamic parameter set on ion flow rate and distribution was analyzed, and the ratio between flow path and ion input rate was optimized according to the requirements of ion concentration distribution on platinum surface.

[0033] By analyzing the ion concentration regulation results, adjusting the ratio between ion input rate and flow path, allocating the ion concentration distribution trend, and combining adsorption migration parameters and adjustment coefficients, the dynamic ion regulation results are obtained.

[0034] Preferably, the steps for obtaining the abnormal intervention adjustment dataset are as follows:

[0035] Based on the dynamic ion regulation results, the monitoring equipment monitors the temperature fluctuation rate and electric field offset in real time during the processing, identifies the fluctuation range, eliminates abnormal values ​​due to equipment faults, analyzes the average fluctuation rate of the data, and obtains temperature and electric field fluctuation data.

[0036] The influence of the temperature and electric field fluctuation range on the platinum surface modification rate was analyzed. Using the known platinum modification rate, the relationship between electric field and temperature was analyzed, and the modification rate under the different fluctuation range was calculated.

[0037] Based on the modification rate, the ion distribution path and temperature field ratio within the target range are dynamically adjusted. Adjustments are made based on the relationship between the modification rate influence data and the temperature and electric field fluctuation range. Ion flow rate and temperature control range are allocated, and an abnormal intervention adjustment dataset is generated.

[0038] Preferably, the steps for obtaining the platinum target surface treatment data table are as follows:

[0039] Based on the abnormal intervention adjustment dataset, the distribution and processing time of the platinum target surface are analyzed. Surface characteristic data at different processing time points are collected, the surface time distribution is sorted out, the characteristic change trend is analyzed, and the data is classified to obtain platinum surface distribution data.

[0040] Based on the platinum surface distribution data, the target processing path parameters are adjusted, the optimal processing time and characteristic distribution of the platinum surface are analyzed, and the characteristic changes under different processing conditions are compared. The operating conditions of processing temperature, time and ion concentration are adjusted to obtain the target processing path parameters.

[0041] Based on the target processing path parameters, the processing conditions are adjusted according to the current operating parameters, and the relationship between the variables of processing time, temperature, and ion concentration is controlled. Real-time processing is performed according to the adjusted parameters to obtain a platinum target surface treatment data table.

[0042] Preferably, the adjustment steps for the target processing path parameters specifically include:

[0043] Based on the platinum surface distribution data, the temperature threshold and ion concentration critical value for different processing stages were extracted. Combined with the equipment operation history data, the influence weight of each parameter on surface properties was analyzed.

[0044] By comparing surface characteristic data under different treatment conditions, the optimal combination of temperature, time, and ion concentration is determined, and the fluctuation range and rate of change of each parameter are adjusted to ensure the stability of the treatment process.

[0045] Based on the combined adjustment results of various parameters, the target processing path parameters are generated.

[0046] Preferably, the step of dynamically adjusting the ion distribution path and temperature field ratio within the target range specifically includes:

[0047] Based on the data on the effect of the modification rate, the adjustment threshold for the ion distribution path and the fluctuation range of the temperature field ratio are set.

[0048] Monitor the ion flow rate and temperature changes during real-time processing. When the parameters exceed the set threshold, gradually adjust the ion input rate and temperature control parameters to bring the ion distribution path and temperature field ratio back to the target range.

[0049] Record parameter changes during the adjustment process, analyze the adjustment effects, and optimize the adjustment strategy to ensure the stability of the processing and the quality of surface treatment.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] The energy distribution control module can comprehensively analyze energy output parameters, temperature gradients, and ion flow path velocities based on equipment operating status information, achieving a better fit between energy distribution and thermal uniformity. This avoids the problems of localized overheating or under-processing caused by energy distribution imbalances in traditional treatments. The electric field environment optimization module, based on the energy distribution control parameter set, meticulously adjusts the electric field strength and gradient changes within the cavity. This reduces the interference of unstable power output from the electric field device on the processing, resulting in a more balanced electric field distribution within the cavity and providing a more stable environmental basis for ion movement on the platinum surface.

[0052] The ion dynamic adjustment module utilizes electric field control to optimize the parameter set, deeply analyzes the adsorption and migration patterns of ions, and adjusts the ion input rate and flow path distribution ratio to make the ion concentration distribution trend more consistent with the target requirements. This solves the problem of uneven surface modification effects caused by uneven ion distribution in traditional treatments. The processing anomaly intervention module can capture the temperature fluctuation rate and electric field offset in real time during the processing. By dynamically adjusting the ion distribution path and temperature field ratio, it promptly corrects the adverse effects of fluctuations on the platinum surface modification rate, avoids the degradation of processing quality caused by the expansion of anomalies, and ensures the continuity and stability of the processing.

[0053] The surface treatment quality improvement module, based on anomaly intervention and adjustment datasets, precisely controls the distribution ratio and processing time of the platinum target surface. By optimizing the target processing path parameters, the final treatment effect is made closer to the expected standard. The entire system, through the organic integration of its modules, forms a complete closed loop from energy distribution, electric field optimization, ion adjustment, anomaly intervention to quality improvement. During processing, it can dynamically adjust based on real-time data, significantly improving the precision and uniformity of platinum surface treatment. Processing efficiency is also improved by reducing abnormal downtime and rework. Simultaneously, the system can adapt to different processing needs, flexibly adjusting parameters to meet diverse surface modification requirements. This makes the platinum surface performance more aligned with the needs of practical applications, reducing material waste due to poor treatment results. While improving product quality, it also reduces overall processing costs. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent control system for platinum nanoscale surface treatment described in this invention.

[0055] Figure 2 A flowchart for obtaining the degree of adaptation between energy distribution and thermal uniformity;

[0056] Figure 3 Flowchart for obtaining the energy distribution control parameter set;

[0057] Figure 4 A flowchart for obtaining the optimal parameter set for electric field control;

[0058] Figure 5 A flowchart for adjusting the dataset acquisition for abnormal intervention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figures 1-5 This invention provides a platinum nanoscale surface treatment intelligent control system, the system comprising:

[0061] The energy distribution and control module acquires real-time operating status information of the platinum surface treatment equipment, including its current operating power, speed, and the working status of each component. Based on this information, it retrieves the equipment's energy output parameters (such as voltage and current), platinum surface temperature gradient (the rate of temperature change at different locations), and ion flow path velocity data (the velocity of ions along different paths). By analyzing this data, it assesses the compatibility between energy distribution and thermal uniformity, determining whether the current energy distribution can maintain a uniform temperature on the platinum surface. Based on the assessment results, it allocates equipment energy control values ​​(such as adjusting the output power) and ion flow equalization parameters (such as changing the distribution ratio of ions along different paths), ultimately generating an energy distribution control parameter set. This parameter set contains specific values ​​and instructions for regulating the equipment's energy output and ion flow distribution.

[0062] The electric field environment optimization module receives a set of energy distribution control parameters from the energy distribution and regulation module, and extracts the electric field strength values ​​(the strength of the electric field at different locations within the cavity) and gradient changes (the rate of change of electric field strength with position) within the equipment cavity. It analyzes the impact of the power output of the electric field device (such as the output power of the electric field generator) on the stability of the electric field, for example, whether power fluctuations lead to significant changes in the electric field strength. Based on the analysis results, it adjusts the uniformity of the electric field distribution within the cavity, for example, by changing the position or output power of the electric field generator, to make the electric field strength in different regions of the cavity more consistent, thereby generating an electric field regulation optimization parameter set. This parameter set contains specific parameter settings for adjusting the electric field distribution.

[0063] The ion dynamic adjustment module, based on an electric field-controlled optimized parameter set, deeply analyzes the adsorption (number and velocity of ions adhering to the platinum surface) and migration (trajectory and velocity of ions on the platinum surface). Based on the analysis results, it adjusts the ion input rate (number of ions input per unit time) and the flow path distribution ratio (percentage of ion flow velocity along different paths), redistributes the ion concentration distribution trend (e.g., increasing or decreasing ion concentration in specific regions of the platinum surface) and dynamic parameter values ​​(e.g., the rate of change of ion concentration over time), ultimately generating the ion dynamic control result, which reflects the adjusted ion distribution and motion state.

[0064] The anomaly intervention module, based on the results of dynamic ion regulation, extracts in real time the temperature fluctuation rate (the magnitude of temperature change over time) and electric field offset (the difference between the actual and target electric field strengths) during the platinum surface treatment process. It analyzes the impact of these fluctuations and offsets on the platinum surface modification rate (the speed at which platinum surface properties change). When the impact exceeds a preset range, it dynamically adjusts the ion distribution path (changing the route ions take to reach the platinum surface) and temperature field ratio (the proportional relationship of temperatures in different regions) within the target range, generating an anomaly intervention adjustment dataset. This dataset contains specific regulatory parameters for correcting anomalies.

[0065] The surface treatment quality improvement module receives the abnormal intervention and adjustment dataset and analyzes the distribution ratio of the platinum target surface (the proportion of areas with different treatment states) and the treatment time. Based on the analysis results, it adjusts the parameters of the target treatment path (such as treatment temperature, time, ion concentration, etc.) and finally generates a platinum target surface treatment data table. This data table records the various parameters and treatment results of the optimized platinum surface treatment and can be directly used to guide the actual platinum nanoscale surface treatment process.

[0066] Example 1:

[0067] The process of obtaining the degree of adaptation between energy distribution and thermal uniformity begins with the comprehensive collection of operational status information of the platinum surface treatment equipment. This operational status information encompasses the real-time operating parameters of each core component of the equipment, including but not limited to the output voltage and current fluctuations of the power module, the sealing pressure of the vacuum chamber, and the activation status of the ion source. Based on this information, three types of key data are extracted: the equipment's energy output parameters, gradient data, and ion flow path velocity data. Energy output parameters include the total energy released by the equipment per unit time and the energy allocation ratio of different functional modules; gradient data mainly reflects the temperature change rate of different areas of the platinum surface, i.e., the ratio of the temperature difference between two adjacent points to their distance; and ion flow path velocity data records the instantaneous and average velocities of ions along the preset flow path inside the chamber.

[0068] A fixed time window is set, the length of which can be adjusted according to the processing accuracy requirements of the equipment, for example, 5 minutes. Within each time window, multiple time points are selected at equal time intervals, such as every 30 seconds. The energy output parameters, gradient data, and ion flow path velocity data corresponding to each time point are matched. By comparing the correlation between these data, such as analyzing the synchronicity between voltage changes in energy output parameters and temperature change rates in gradient data, and the correlation between fluctuations in ion flow path velocity data and the former two, the data is filtered. Data points with weak correlations, such as abnormal jump values ​​caused by momentary sensor malfunctions, are removed, resulting in the pre-filtered energy output parameters and gradient data.

[0069] Based on the filtered energy output parameters and gradient data, the preset paths of the ion flow are matched and verified. During the verification process, the energy transfer data on each path must be checked against the temperature gradient data of the corresponding region. For example, if the energy output parameters of a path show a continuous increase in energy supply, but the temperature gradient of the corresponding region shows a decreasing trend, it is considered a mismatch. For mismatches, the difference between energy and gradient is calculated, represented by the sum of the deviations between the actual measured values ​​and the theoretical calculated values. The energy distribution and gradient distribution are corrected by considering changes in the ion flow rate, as the energy carried by the ion flow varies at different flow rates, affecting the heat transfer efficiency of the platinum surface. For example, when the ion flow rate increases, the energy carried by the ion flow is transferred to the platinum surface at a faster rate, thus affecting the local temperature gradient. Based on the specific impact of the flow rate on the data, relevant path parameters, such as path width and ion flow inlet angle, are adjusted until a reasonable match between energy and gradient is achieved, resulting in a detailed record of the energy and gradient matching.

[0070] Based on the matching of energy and gradient, thermal uniformity analysis is performed. First, thermal uniformity analysis criteria are established, including specific indicators such as the maximum allowable temperature difference on the platinum surface and the upper limit of temperature fluctuation frequency. In conjunction with dynamic changes during equipment operation, such as periodic fluctuations in energy output and phased adjustments in ion flow rate, the energy distribution under different flow rate conditions is evaluated. For example, the energy distribution in different regions of the platinum surface is evaluated at ion flow rates of 10 m / s, 15 m / s, and 20 m / s. Thermal uniformity indicators under different flow rate conditions are compared, such as calculating the standard deviation and range of temperature in each region, and the flow rate conditions are optimized based on the comparison results. If the temperature standard deviation is smallest at a flow rate of 15 m / s and the thermal uniformity analysis criteria are met, then 15 m / s is adopted as the optimized flow rate condition. Through the above process, a quantitative result of the fit between energy distribution and thermal uniformity is finally obtained, presented in the form of a fit index; a higher index indicates a better fit. The calculation of the fit index takes into account a variety of factors, including the uniformity of energy distribution, the stability of temperature gradient, and the degree of matching with flow velocity conditions.

[0071] Example 2:

[0072] The acquisition of the energy distribution control parameter set is based on the degree of adaptation between energy distribution and thermal uniformity. First, the energy transmission and uniformity changes of the equipment under differentiated operating conditions are analyzed. These differentiated operating conditions encompass the equipment's operating status at different power levels, different processing stages, and different environmental parameters. For example, when the equipment is operating at low power, energy transmission is mainly concentrated in the core processing area, with a smaller energy coverage area in the peripheral areas; while during high power operation, the energy transmission range expands, but energy density varies in different areas. Differences in processing stages are reflected in the step-like increase in energy transmission during startup, the constant fluctuation during stable operation, and the gradual decay during shutdown. Changes in environmental parameters include the internal pressure of the cavity and the purity of the inert gas, all of which affect the energy transmission efficiency and distribution pattern within the equipment. By analyzing these differentiated conditions one by one, the changes in energy transmission paths, the proportion of energy loss, and the fluctuations in thermal uniformity indicators are recorded, and then a weighted calculation of the equipment's energy distribution is performed. During the weighted calculation, weight values ​​are assigned according to the degree of influence of different regions on the quality of platinum surface treatment. The weight value of the core treatment area is higher than that of the edge area. This yields the initial energy regulation requirements of the equipment, which clarifies the approximate range within which the energy output of each region needs to be increased or decreased.

[0073] Based on the initial energy regulation requirements of the equipment, the energy balance between the devices is further analyzed. Here, "devices" refers to the various sub-devices constituting the platinum surface treatment system, including energy supply devices, ion generators, and temperature control components. During the analysis, it is necessary to identify the energy transfer efficiency between each sub-device, i.e., the effective utilization rate of energy transferred from one sub-device to another, and the load distribution relationship between each sub-device, i.e., the proportion of energy processing load borne by each sub-device to the total system load. For example, when the output power of the energy supply device increases, it is necessary to check whether the ion generator can receive and convert this energy in a timely manner. If energy transfer lag occurs, it indicates that the energy transfer efficiency between the two is low. Simultaneously, if the load share of one sub-device consistently exceeds 80% of its rated load, while the load share of other sub-devices is below 50%, it indicates an unbalanced load distribution. To address these situations, the equipment energy regulation parameters are corrected. Correction methods include adjusting the energy transfer interface parameters between sub-devices and optimizing the operating thresholds of the energy conversion modules, so that the energy transfer efficiency of each sub-device remains within a reasonable range and the load distribution tends to be balanced.

[0074] By combining the energy regulation dataset between devices with the thermal uniformity adaptation results, energy is allocated among the devices. During the allocation process, the thermal uniformity adaptation results are used as a benchmark to ensure that the energy distribution meets the thermal uniformity requirements of platinum surface treatment. For example, if the thermal uniformity adaptation results show insufficient temperature uniformity in a certain area, the energy supply to that area needs to be increased, and the energy output of the surrounding areas needs to be adjusted accordingly to maintain overall thermal balance. Simultaneously, the required balance requirements are matched, ensuring that the operating parameters of each sub-device do not exceed safe thresholds after energy allocation, and that the energy interaction between them remains stable. Through iterative optimization, the energy output parameters of each sub-device, the allocation ratio of energy transmission paths, and the control values ​​of energy conversion efficiency are adjusted, ultimately resulting in an energy distribution control parameter set. This parameter set includes the specific energy output values ​​of each sub-device, the time node control of energy transmission, and the energy allocation coefficients for different operating stages. It can be directly used to guide the energy regulation operation of the devices, enabling the entire system to achieve balanced energy operation among the sub-devices while meeting the thermal uniformity requirements.

[0075] Example 3:

[0076] The acquisition of electric field strength and gradient changes within the equipment cavity begins with the analysis of the energy distribution control parameter set. Temperature data within the cavity is extracted from this parameter set. This data is collected by temperature sensors distributed at various locations within the cavity, including multiple monitoring points at the top, bottom, side walls, and near the platinum surface. Temperature points within each time period are selected; the time period can be divided according to the stages of the processing, such as collecting a temperature point every 10 seconds during the pretreatment stage and every 5 seconds during the core processing stage. By combining the temperature change trends at different locations within the cavity, temperature fluctuations are analyzed. For example, the magnitude of temperature rise or fall at a specific monitoring point over multiple consecutive time periods, as well as the temperature difference between adjacent monitoring points, are observed. This yields more detailed temperature data within the equipment cavity, reflecting the temperature state of different regions within the cavity at different times.

[0077] Based on temperature data within the equipment cavity, the electric field strength value corresponding to each temperature point is calculated. Through long-term accumulated experimental data, a correlation model between temperature and electric field is established. This model reflects how temperature changes affect the electric field strength under a specific equipment structure. Using this model, the corresponding electric field strength value is calculated based on the measured value at each temperature point, and the change in electric field at each measurement point is identified, i.e., the difference in electric field strength between two adjacent time points. Combined with the equipment's structural parameters, such as the cavity's geometric dimensions, the arrangement of internal electrodes, and the distribution of insulating materials, the electric field changes at different locations are compared. For example, the synchronous changes in electric field strength between the cavity's central and peripheral regions are analyzed, as well as significant differences in the electric field gradient between regions near and far from the electrodes. This yields electric field distribution and gradient distribution data. The electric field distribution data records the absolute value of the electric field strength at each location within the cavity, while the gradient distribution data records the rate of change of the electric field strength with spatial location.

[0078] Based on electric field distribution and gradient distribution data, the overall electric field distribution within the device cavity is analyzed to determine whether the electric field intensity exhibits a regular distribution and whether there are regions with excessively strong or weak electric fields. The electric field gradient is optimized using temperature data; for example, when the temperature in a certain region is high and the electric field gradient is large, the electric field gradient is reduced by adjusting the corresponding electrode parameters in that region to minimize the impact of temperature on the electric field. The impact of electric field changes on device performance is analyzed, such as whether instability in electric field intensity causes ion trajectories to deviate from the preset path, thereby affecting the adsorption effect of ions on the platinum surface. Based on the analysis results, a stable electric field configuration is determined under different operating conditions, including adjustments to energy output parameters and changes in ion flow rate. For each operating condition, a corresponding set of electric field intensity values ​​and gradient variation ranges is determined, ultimately forming complete data on electric field intensity values ​​and gradient variation within the device cavity.

[0079] The acquisition of the electric field control optimization parameter set is based on the electric field intensity value and gradient change within the equipment cavity. First, the time series of electric field changes is determined, and the electric field intensity value and gradient change at each moment are arranged chronologically to form a continuous change curve. The current electric field intensity value is compared with the original electric field data, which serves as the baseline value of the electric field parameters under standard operating conditions, and the degree of deviation between the two is calculated. The electric field gradient at each moment is analyzed to clarify the rate and direction of gradient change. The cavity is divided into different regions according to the equipment's state, such as near-electrode, intermediate-electrode, and far-electrode regions based on distance from the electrodes. A corresponding threshold for electric field intensity and gradient change is defined for each region. When the electric field parameters in a certain region exceed the threshold, control is required. Through the above process, a preliminary electric field change parameter set is generated, which includes the electric field intensity, gradient change, and deviation from the threshold values ​​for each region at different times.

[0080] The preliminary electric field variation parameter set is analyzed to study the impact of the electric field within the cavity on the power output stability of the equipment. By comparing the electric field intensity fluctuation curve and the power output fluctuation curve, the correlation pattern between the two is identified. The relationship between the electric field and power output is identified, such as whether high-frequency fluctuations in electric field intensity will cause corresponding fluctuations in power output, and whether an electric field anomaly in a certain region will lead to a decrease in overall power output. The power stability influence coefficient of different sections is analyzed; this coefficient quantifies the degree of influence of electric field changes on power stability in different regions. The larger the coefficient, the more significant the impact of electric field changes on power stability in that region. Based on the cavity electric field variation parameters, the uniformity of the electric field distribution is adjusted. Regions with a large power stability influence coefficient are prioritized for regulation by changing the voltage output of the electrodes and adjusting the spatial position of the electrodes to stabilize the electric field intensity in those regions. The electric field regulation data is optimized, such as correcting the output parameters of the electrode control module and adjusting the sampling frequency of the electric field monitoring. Finally, an optimized electric field regulation parameter set is generated, which includes the electric field regulation target value for each region, the time node for regulation execution, and the corresponding electrode operation parameters.

[0081] The calculation method for the section power stability influence coefficient is as follows:

[0082]

[0083] In the formula, K is the influence coefficient of power stability in the section, and E i Let E0 be the measured electric field strength of a certain segment at time i, and P be the standard electric field strength of that segment. i Let be the power output value of the device in this segment at time i, and n be the total number of samples in this time period.

[0084] Example 4:

[0085] The acquisition of ion dynamic control results is based on an optimized parameter set for electric field control. Ion adsorption data on the platinum surface is extracted from this parameter set. This data is collected by ion sensors installed near the platinum surface, including the number of ions adsorbed per unit area and the proportion of different ion types. Simultaneously, the adsorption rate of ions on different surface materials is monitored, such as the difference in adsorption rate between the platinum surface and the stainless steel inner wall of the cavity, as well as the changes in the adsorption rate of ions in different regions of the platinum surface (e.g., edge and center regions). By combining time factors, such as different time points after the start of treatment (10, 20, 30 minutes), and temperature factors, such as maintaining the platinum surface temperature at different conditions (150℃, 200℃, 250℃), the migration characteristics of ions are inferred. For example, at higher temperatures, ions may migrate more easily from the edge region to the center region of the platinum surface, while the overall migration rate of ions may gradually slow down as the treatment time increases. The adsorption rate coefficient and migration rate coefficient are defined. The adsorption rate coefficient is used to represent the change in the number of adsorbed ions per unit area per unit time, and the migration rate coefficient is used to represent the distance that ions migrate per unit time. Based on these coefficients, a set of dynamic adsorption and migration parameters is generated. This set of parameters covers detailed data such as the amount of adsorption, adsorption rate, migration direction and migration speed of ions on platinum surface and other material surfaces under different time and temperature conditions.

[0086] The influence of adsorption-migration dynamic parameter sets on ion flow rate and distribution was analyzed. For example, a high ion adsorption rate in a certain region leads to a decrease in ion flow rate near that region, thus affecting the overall ion distribution. Based on the required ion concentration distribution on the platinum surface—such as certain functional areas requiring higher ion concentrations to enhance surface hardness, while other areas require lower ion concentrations to maintain surface smoothness—the ion input rate and flow path distribution ratio were optimized. If the central region of the platinum surface requires increased ion concentration, the flow rate proportion of the ion flow path leading to that region was increased, and the overall ion input rate was appropriately increased.

[0087] The analysis of ion concentration control results involves checking whether the optimized ion concentration in each region of the platinum surface meets the preset requirements. If the ion concentration in a certain region is still lower than the target value, the ratio of the ion input rate to the flow path in that region is further adjusted, such as increasing the flow rate proportion of that path from 20% to 30%, while fine-tuning the proportions of other paths to maintain the overall ion flow balance. Combining adsorption and migration parameters with adjustment coefficients, the adjustment coefficients are used to fine-tune the concentration distribution based on the adsorption and migration characteristics of ions. For example, for regions with strong adsorption capacity, the adjustment coefficient is appropriately reduced to avoid excessive ion aggregation. The final result of dynamic ion control includes information such as the adjusted ion input rate, the flow rate distribution ratio of each path, and the expected ion concentration in each region of the platinum surface.

[0088] The acquisition of the abnormal intervention and adjustment dataset is based on the results of dynamic ion regulation. Temperature fluctuation and electric field offset are monitored in real time during the processing using temperature and electric field sensors distributed within the cavity. Temperature fluctuation is the ratio of the maximum temperature change per unit time to the average temperature, and electric field offset is the difference between the actual measured electric field strength and the set target electric field strength. The ranges of these fluctuations and offsets are identified; for example, temperature fluctuation within ±2% and electric field offset within ±5% are considered normal. Abnormal values ​​caused by equipment malfunctions, such as sudden temperature rises or falls due to poor sensor wiring contact, and sudden changes in electric field strength to zero due to electrode short circuits, are removed. The average fluctuation of the remaining valid data is calculated to obtain temperature and electric field fluctuation data that reflect the stability of the processing.

[0089] The effects of temperature and electric field fluctuations on the platinum surface modification rate were analyzed. For example, a temperature fluctuation exceeding ±3% may significantly affect the modification rate; excessive electric field offset may lead to a decrease in the modification rate. Using known platinum modification rate data—specifically, modification rate values ​​recorded under different temperature and electric field conditions during historical processing—the relationship between electric field, temperature, and modification rate was analyzed. For instance, an increase in temperature within a certain range accelerates the modification rate, while an excessive electric field offset inhibits it. Based on this relationship, the modification rate under different fluctuation ranges was calculated, such as the specific modification rate values ​​corresponding to a temperature fluctuation of ±4% and an electric field offset of ±6%.

[0090] Based on the calculated modification rate, the ion distribution path and temperature field ratio within the target range are dynamically adjusted. If the modification rate is lower than expected, it is analyzed whether this is due to an unreasonable ion distribution path or an unbalanced temperature field ratio in a certain region. The ion flow path in that region is then adjusted, such as by changing the ion jet angle to increase ion coverage. Simultaneously, the temperature field ratio between that region and surrounding regions is adjusted, such as by relatively increasing the temperature of that region by 5%. Based on the relationship between the modification rate and the fluctuation range of temperature and electric field, the adjustment range of the ion flow rate and the specific range of temperature control are determined, such as adjusting the ion flow rate from 15 m / s to 18 m / s and setting the temperature control range to 200℃ ± 5℃. Finally, an abnormal intervention adjustment dataset is generated, which includes ion distribution path adjustment schemes, temperature field ratio adjustment parameters, ion flow rate control values, and temperature control ranges for different fluctuation conditions.

[0091] Example 5:

[0092] The acquisition of platinum target surface treatment data was based on an anomaly intervention and adjustment dataset. The distribution of platinum target surfaces and treatment time were analyzed. Surface characteristic data were collected at fixed time intervals during treatment, the intervals of which could be set according to the duration of the treatment process, such as every 15 minutes. The collected surface characteristic data included surface roughness, nano-hardness, and elemental composition ratios. This data was acquired using high-precision surface inspection instruments, covering the detection results of different regions of the platinum surface. This data was then organized to form a surface time distribution, i.e., the surface characteristic values ​​of each region at different time points. The trends of these characteristics were analyzed, such as whether surface roughness gradually decreased or first decreased and then increased with the extension of treatment time, and how the proportion of platinum with other elements in the elemental composition changed. The data was categorized, with regions showing similar trends grouped together, and time points at the same treatment stage grouped together. This yielded platinum surface distribution data, which included various surface characteristic parameters of different categories of regions at different time points.

[0093] Based on platinum surface distribution data, the parameters of the target processing path were adjusted. The relationship between the optimal processing time and the characteristic distribution of the platinum surface was analyzed, i.e., determining the time point at which the surface characteristics of each region are closest to the expected target. The changes in characteristics under different processing conditions were compared. These conditions included different processing temperatures (e.g., 180℃, 200℃, 220℃), different processing times (e.g., 60 minutes, 90 minutes, 120 minutes), and different ion concentrations (e.g., 1×10⁻⁶). 15 ions / cm 2 3×10 15 ions / cm 2 5×10 15 ions / cm 2 For example, compare the differences in hardness and roughness of platinum surfaces treated at 200℃ for 90 minutes versus 220℃ for 60 minutes. Based on the comparison results, adjust the operating conditions of treatment temperature, time, and ion concentration. If the surface hardness is closer to the target value when treated at 200℃ for 90 minutes, then that temperature and time are prioritized, and the ion concentration is adjusted accordingly to match this condition. This yields the target treatment path parameters, which include specific temperature settings, time duration, ion concentration values, and adjustment ranges for each parameter.

[0094] Based on the target processing path parameters, the processing conditions are adjusted according to the current operating parameters. The relationship between processing time, temperature, and ion concentration is controlled; for example, when the processing temperature increases, the processing time is appropriately shortened to avoid over-processing; when the ion concentration increases, the temperature is fine-tuned to maintain a stable reaction rate. Real-time processing is performed according to the adjusted parameters, continuously recording the actual values ​​of each parameter and the corresponding surface characteristic data during the process. After processing, these data are compiled and summarized to form a platinum target surface treatment data table. This data table details the time points in the processing process, the temperature at each time point, the ion concentration, and the corresponding surface characteristic detection results.

[0095] The parameters of the target processing path were adjusted based on platinum surface distribution data. Temperature thresholds and critical ion concentration values ​​for different processing stages were extracted. The processing stages were divided into an initial stage, a reaction stage, and a stabilization stage. The temperature threshold for each stage was the highest and lowest permissible temperature for that stage, and the critical ion concentration value was the minimum ion concentration that needed to be reached or the maximum value that could not be exceeded for that stage. Combining historical equipment operation data—that is, the parameters and corresponding surface characteristic results recorded in similar past processing processes—the influence weight of each parameter on surface characteristics was analyzed, such as the influence weight of temperature on surface hardness and the influence weight of ion concentration on surface roughness.

[0096] By comparing surface characteristic data under different treatment conditions, the optimal combination of temperature, time, and ion concentration was identified to achieve the best possible surface properties. The fluctuation range of each parameter was adjusted; for example, the temperature fluctuation range was reduced from ±5℃ to ±3℃ to reduce characteristic fluctuations. The rate of change of parameters was also adjusted; for example, the rate of temperature increase was reduced from 10℃ / min to 5℃ / min to allow sufficient time for the surface properties to adapt to the parameter changes. Based on the combined results of these parameter adjustments, target treatment path parameters were generated. These parameters comprehensively considered the threshold requirements of each stage, the influence patterns in historical data, and the stability of the adjusted parameters.

[0097] The ion distribution path and temperature field ratio within the target range are dynamically adjusted based on the modification rate influence data. An adjustment threshold is set for the ion distribution path; when the deviation between the ion flow rate and the target flow rate on a certain path exceeds this threshold, path adjustment is initiated. A fluctuation range for the temperature field ratio is set; the ratio of temperatures in different regions must be maintained within this range.

[0098] The system monitors ion flow rate and temperature changes during real-time processing. Ion flow rate is monitored by flow rate sensors installed along the path, while temperature changes are acquired by temperature sensors distributed across the platinum surface. When parameters exceed set thresholds, the ion input rate is gradually adjusted; for example, if the flow rate along a path is 10% lower than the target value, the ion input rate along that path is increased by 5%. Temperature control parameters are also adjusted; for example, if the temperature ratio of a certain area to other areas exceeds the range, the heating power of that area is fine-tuned. Through these adjustments, the ion distribution path and temperature field ratio gradually return to the target range.

[0099] Record parameter changes during the adjustment process, including ion flow rate, temperature, adjustment magnitude, and time points before and after adjustment. Analyze this data to understand the effect of the adjustment measures on parameter regression, such as the length of time it takes for a particular adjustment measure to bring the parameter back to the target range. Based on the analysis results, optimize the adjustment strategy, such as the magnitude or frequency of adjustment, to make subsequent adjustments more accurate and effective.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A platinum nanoscale surface treatment intelligent control system, characterized in that, The system includes: The energy distribution and control module, based on the operating status information of the platinum surface treatment equipment, calls the equipment's energy output parameters, platinum surface temperature gradient, and ion flow path velocity data, analyzes the degree of adaptation between energy distribution and thermal uniformity, allocates equipment energy control values ​​and ion flow equalization parameters, and generates a set of energy distribution control parameters. Based on the energy distribution control parameter set, the electric field environment optimization module extracts the electric field intensity value and gradient change in the equipment cavity, analyzes the impact of the power output of the electric field device on stability, adjusts the uniformity of the electric field distribution in the cavity, and generates an electric field regulation optimization parameter set. The ion dynamic adjustment module analyzes the adsorption and migration of ions on the platinum surface based on the electric field control optimization parameter set, adjusts the ion input rate and flow path distribution ratio, redistributes the distribution trend and dynamic parameter values ​​of ion concentration, and generates ion dynamic control results. Based on the ion dynamic control results, the abnormal intervention module extracts the real-time temperature fluctuation rate and electric field offset during the platinum surface treatment process, analyzes the influence of the fluctuation range on the platinum surface modification rate, dynamically adjusts the ion distribution path and temperature field ratio within the target range, and generates an abnormal intervention adjustment dataset. The surface treatment quality improvement module analyzes the distribution ratio and processing time of the platinum target surface based on the abnormal intervention adjustment dataset, adjusts the parameters of the target processing path, and generates a platinum target surface treatment data table. The specific steps for dynamically adjusting the ion distribution path and temperature field ratio within the target range are as follows: Based on the data on the influence of modification rate, the adjustment threshold for ion distribution path and the fluctuation range of temperature field ratio were set. Monitor the ion flow rate and temperature changes during real-time processing. When the parameters exceed the set threshold, gradually adjust the ion input rate and temperature control parameters to bring the ion distribution path and temperature field ratio back to the target range. Record parameter changes during the adjustment process, analyze the adjustment effect, and optimize the adjustment strategy to ensure the stability of the processing and the quality of surface treatment; The specific steps for obtaining the electric field control optimization parameter set are as follows: Based on the electric field intensity value and gradient change in the cavity of the device, the time series of electric field change is determined, the current electric field intensity value is compared with the original electric field data, the electric field gradient at each moment is analyzed, and corresponding thresholds are defined according to the device state partition to generate a preliminary electric field change parameter set. The preliminary electric field change parameter set is analyzed to analyze the influence of the electric field inside the cavity on the stability of the device's power output and to identify the correlation between the electric field and the power output. By analyzing the influence coefficient of power stability in the section and combining the cavity electric field variation parameters, the electric field distribution uniformity is adjusted, the electric field control data is optimized, and an electric field control optimization parameter set is generated. The calculation method for the section power stability influence coefficient is as follows: In the formula, K is the influence coefficient of power stability in the section, and E i Let E0 be the measured electric field strength of a certain segment at time i, and P be the standard electric field strength of that segment. i Let be the device power output value corresponding to this segment at time i, and n be the total number of samples within the corresponding time period of this segment.

2. The intelligent control system for platinum nanoscale surface treatment according to claim 1, characterized in that, The specific steps for obtaining the degree of fit between the energy distribution and thermal uniformity are as follows: Based on the operating status information of the platinum surface treatment equipment, the energy output parameters, gradient data and ion flow path velocity data of the equipment are extracted. A time window is set, and time points are selected to match the data. By comparing the data correlation and filtering the data, the energy output parameters and gradient data are obtained. Based on the energy output parameters and gradient data, the path is matched and verified, the difference between energy and gradient is calculated, the energy distribution and gradient distribution are corrected by combining the flow velocity change, and the path parameters are adjusted by the influence of flow velocity on the data to obtain the energy and gradient matching situation. Based on the energy and gradient matching, thermal uniformity analysis is performed, thermal uniformity analysis standards are set, and the energy distribution under different flow rates is evaluated in combination with the dynamic changes in equipment operation. The uniformity index is compared and the flow rate conditions are optimized to obtain the degree of fit between energy distribution and thermal uniformity.

3. The intelligent control system for platinum nanoscale surface treatment according to claim 2, characterized in that, The specific steps for obtaining the energy distribution control parameter set are as follows: Based on the degree of adaptation between the energy distribution and thermal uniformity, the energy transmission and uniformity changes of the equipment under differentiated operating conditions are analyzed, and the energy distribution of the equipment is weighted and calculated to obtain the preliminary energy regulation requirements of the equipment. Based on the initial energy regulation requirements of the equipment, the energy balance between the equipment is analyzed, the relationship between energy transmission efficiency and load distribution between the equipment is identified, and the energy regulation parameters of the equipment are corrected. By combining the energy regulation dataset between devices with the thermal uniformity adaptation results, the energy between devices is allocated, optimized, and matched to the required balance and uniformity requirements, resulting in a set of energy distribution control parameters.

4. The intelligent control system for platinum nanoscale surface treatment according to claim 3, characterized in that, The specific steps for obtaining the electric field strength value and gradient change within the device cavity are as follows: Based on the energy distribution control parameter set, temperature data inside the equipment cavity is extracted, temperature points within each time period are selected, and temperature fluctuations are analyzed by combining the temperature change trends at different locations inside the cavity to obtain temperature data inside the equipment cavity. Based on the temperature data inside the equipment cavity, the electric field strength value of each temperature point is calculated. By analyzing the relationship between temperature and electric field, the change in electric field at each measurement point is identified. Combined with the equipment structural parameters, the electric field changes at different locations are compared to obtain electric field distribution and gradient distribution data. Based on the electric field distribution and gradient distribution data, the overall electric field distribution inside the equipment cavity is analyzed, the electric field gradient is optimized by combining temperature data, the impact of electric field changes on equipment performance is analyzed, the stable electric field configuration under differentiated operating conditions is determined, and the electric field strength value and gradient change amount inside the equipment cavity are obtained.

5. The intelligent control system for platinum nanoscale surface treatment according to claim 4, characterized in that, The specific steps for obtaining the ion dynamic regulation results are as follows: Based on the electric field modulation optimization parameter set, ion adsorption data on the platinum surface is extracted, the adsorption rate of ions on the surface of different materials is monitored, and the migration characteristics of ions are inferred by combining external environmental factors such as time and temperature. Adsorption and migration rate coefficients are defined, and an adsorption and migration dynamic parameter set is generated. The influence of the adsorption and migration dynamic parameter set on ion flow rate and distribution was analyzed, and the ratio between flow path and ion input rate was optimized according to the requirements of ion concentration distribution on platinum surface. By analyzing the ion concentration regulation results, adjusting the ratio between ion input rate and flow path, and allocating the ion concentration distribution trend, the dynamic regulation results of ions are obtained by combining adsorption migration parameters and adjustment coefficients.

6. The intelligent control system for platinum nanoscale surface treatment according to claim 5, characterized in that, The specific steps for obtaining the abnormal intervention adjustment dataset are as follows: Based on the dynamic ion regulation results, the monitoring equipment monitors the temperature fluctuation rate and electric field offset in real time during the processing, identifies the fluctuation range, eliminates abnormal values ​​due to equipment faults, analyzes the average fluctuation rate of the data, and obtains temperature and electric field fluctuation data. The effects of temperature and electric field fluctuation range on the surface modification rate of platinum were analyzed. Using the known platinum modification rate, the relationship between electric field and temperature was analyzed, and the modification rate under different fluctuation ranges was calculated. Based on the modification rate, the ion distribution path and temperature field ratio within the target range are dynamically adjusted. Adjustments are made based on the relationship between the modification rate influence data and the temperature and electric field fluctuation range. Ion flow rate and temperature control range are allocated, and an abnormal intervention adjustment dataset is generated.

7. The intelligent control system for platinum nanoscale surface treatment according to claim 6, characterized in that, The specific steps for obtaining the platinum target surface treatment data table are as follows: Based on the abnormal intervention adjustment dataset, the distribution and processing time of the platinum target surface are analyzed. Surface characteristic data at different processing time points are collected, the surface time distribution is sorted out, the characteristic change trend is analyzed, and the data is classified to obtain platinum surface distribution data. Based on the platinum surface distribution data, the target processing path parameters are adjusted, the optimal processing time and characteristic distribution of the platinum surface are analyzed, and the characteristic changes under different processing conditions are compared. The operating conditions of processing temperature, time and ion concentration are adjusted to obtain the target processing path parameters. Based on the target processing path parameters, the processing conditions are adjusted according to the current operating parameters, and the relationship between the variables of processing time, temperature and ion concentration is controlled. Real-time processing is performed according to the adjusted parameters to obtain a platinum target surface treatment data table.

8. The intelligent control system for platinum nanoscale surface treatment according to claim 7, characterized in that, The specific steps for adjusting the target processing path parameters are as follows: Based on the platinum surface distribution data, the temperature threshold and ion concentration critical value for different processing stages were extracted. Combined with the equipment operation history data, the influence weight of each parameter on surface properties was analyzed. By comparing surface characteristic data under different treatment conditions, the optimal combination of temperature, time, and ion concentration was determined, and the fluctuation range and rate of change of each parameter were adjusted to ensure the stability of the treatment process. Based on the combined adjustment results of various parameters, the target processing path parameters are generated.

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