Intelligent control system for platinum nanoscale surface treatment

Abstract

The invention relates to the technical field of platinum surface treatment, and discloses an intelligent control system for platinum nanoscale surface treatment. The system comprises an energy distribution regulation and control module, an electric field environment optimization module, an ion dynamic adjustment module, a treatment abnormality intervention module and a surface treatment quality improvement module. The energy distribution regulation and control module analyzes the adaptation degree of energy distribution and heat uniformity and generates an energy distribution control parameter set; the electric field environment optimization module adjusts the electric field distribution balance of the cavity and generates an electric field regulation and control optimization parameter set; the ion dynamic regulation module regulates the ion input rate and the flow velocity path distribution proportion, and generates an ion dynamic regulation result; the abnormal intervention processing module extracts the real-time temperature fluctuation rate and the electric field offset and generates an abnormal intervention adjustment data set; and the surface treatment quality improving module adjusts the target treatment path parameters and generates a platinum target surface treatment data table. According to the system, precise regulation and control of platinum nanoscale surface treatment are achieved, and the treatment effect and stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of platinum surface treatment, in particular to an intelligent control system for platinum nano-scale surface treatment. Background Art

[0002] Surface modification technology has long been a key focus in the processing of platinum materials. Due to its unique physical and chemical properties, platinum is widely used in high-end applications 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 rely heavily on manual control or semi-automated equipment. During the processing process, the precise control of key parameters such as energy distribution, electric field environment, and ion dynamics is often difficult to ensure.

[0003] In existing technologies, platinum surface treatment equipment often suffers from uneven energy output distribution. During the treatment process, there is a lack of a real-time dynamic adaptation mechanism between the energy parameters of the equipment during operation and the temperature gradient on the platinum surface. This can easily lead to excessive or insufficient energy in local areas, which in turn causes large temperature fluctuations on the platinum surface, affecting the surface modification effect. At the same time, there are obvious limitations in regulating the electric field environment within the equipment cavity. The distribution and balance of the electric field intensity are difficult to control. Fluctuations in the power output of the electric field device directly lead to unstable adsorption and migration processes of ions on the platinum surface, resulting in deviations in the ion concentration distribution and ultimately a decrease in the uniformity of the platinum surface treatment.

[0004] There are also many problems with the dynamic adjustment of ions. Traditional ion control methods mostly use fixed parameter settings and cannot be flexibly adjusted according to real-time electric field changes and the state of the platinum surface. This leads to deviations in the adsorption amount and migration path of ions on the platinum surface, which in turn affects the rate and quality of surface modification. In addition, during the treatment process, the intervention mechanism for abnormal situations is not perfect. When the temperature fluctuates or the electric field shifts, the existing system is difficult to respond quickly and make precise adjustments, which often leads to local over-treatment or under-treatment of the platinum surface, affecting 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 ion dynamic adjustment are becoming increasingly prominent. For example, during the energy distribution process, existing equipment has difficulty dynamically adapting to the real-time temperature gradient of the platinum surface and the flow rate of the ion flow path, resulting in a low match between energy distribution and thermal uniformity. In terms of electric field environment optimization, the electric field intensity and gradient changes within the equipment cavity often fluctuate significantly due to unstable power output, affecting the movement trajectory of ions on the platinum surface. In the dynamic adjustment of ions, the adsorption and migration patterns of ions are difficult to effectively capture and control, resulting in a deviation between the distribution trend of ion concentration and the target requirements.

[0006] There are also obvious deficiencies in the mechanism for handling abnormal situations during the treatment process. When the surface temperature of platinum fluctuates or the electric field shifts, the existing system can often only perform simple shutdown processing or extensive parameter adjustments, and cannot achieve precise intervention while ensuring the continuity of treatment. This not only affects the treatment efficiency, but may also lead to material waste. At the same time, the evaluation and improvement of surface treatment quality lacks a systematic parameter adjustment mechanism, making it difficult to dynamically optimize the target treatment path based on real-time data during the treatment process, resulting in the final treatment effect being difficult to meet the expected standards. The existence of these problems has severely restricted the accuracy, efficiency and stability of platinum surface treatment, and it is unable to meet the stringent requirements of high-end application scenarios for the surface performance of platinum materials. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent control system for platinum nano-scale surface treatment to solve the problems raised in the above background technology.

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

[0009] The energy distribution control module uses the operating status information of the platinum surface treatment equipment, the equipment's energy output parameters, the platinum surface temperature gradient, and the ion flow path velocity data to analyze the degree of adaptation between energy distribution and thermal uniformity, allocate equipment energy control values ​​and ion flow balance parameters, and generate an energy distribution control parameter set;

[0010] The electric field environment optimization module extracts the electric field intensity value and gradient change in the device cavity based on the energy distribution control parameter set, analyzes the impact of the electric field device power output on stability, adjusts the cavity electric field distribution balance, and generates an electric field control 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 the ion concentration, and generates an ion dynamic adjustment result;

[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 impact 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 data set;

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

[0014] Preferably, the step of obtaining the degree of adaptation between energy distribution and thermal uniformity is specifically as follows:

[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. The time window is set, and the time points are selected to match the data. By comparing the data correlation and performing data screening, the energy output parameters and gradient data are obtained.

[0016] Based on the energy output parameters and gradient data, the path is matched and checked, the difference between energy and gradient is calculated, the energy distribution and gradient distribution are corrected in combination with the flow rate change, and the path parameters are adjusted according to the influence of the flow rate on the data to obtain the energy and gradient matching situation;

[0017] Based on the energy and gradient matching, a thermal uniformity analysis is performed, and the thermal uniformity analysis standard is set. Combined with the dynamic changes in equipment operation, the energy distribution under differentiated flow rate conditions is evaluated, the uniformity indicators are compared, and the flow rate conditions are optimized to obtain the degree of adaptation between energy distribution and thermal uniformity.

[0018] Preferably, the steps of acquiring the energy distribution control parameter set are specifically as follows:

[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 weightedly calculated to obtain the preliminary energy control requirements of the equipment;

[0020] Based on the preliminary energy control requirements of the equipment, analyze the energy balance between the equipment, identify the relationship between energy transmission efficiency and load distribution between the equipment, and modify the equipment energy control parameters;

[0021] Combining the energy control data set between the devices with the thermal uniformity adaptation result, the energy between the devices is distributed, the required balance and uniformity requirements are optimized and matched, and the energy distribution control parameter set is obtained.

[0022] Preferably, the steps of obtaining the electric field strength value and gradient variation in the device cavity are specifically as follows:

[0023] Based on the energy distribution control parameter set, extract the temperature data in the device cavity, screen the temperature points in each time period, combine the temperature change trends of different positions in the cavity, analyze the temperature fluctuation, and obtain the temperature data in the device cavity;

[0024] Based on the temperature data in the device cavity, the corresponding electric field strength value is calculated for each temperature point. By analyzing the relationship between temperature and electric field, the electric field change at each measurement point is identified. Combined with the device structural parameters, the electric field changes at different positions 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 in the device cavity is analyzed, the electric field gradient is optimized in combination with the temperature data, the impact of electric field changes on device performance is analyzed, the stable electric field configuration under differentiated operating conditions is determined, and the electric field strength value and gradient change in the device cavity are obtained.

[0026] Preferably, the steps of obtaining the electric field control optimization parameter set are specifically as follows:

[0027] Based on the electric field intensity value and gradient change in the device cavity, determine the time series of the electric field change, compare the current electric field intensity value with the original electric field data, analyze the electric field gradient at each moment, and define corresponding thresholds according to the device state partition to generate a preliminary electric field change parameter set;

[0028] Analyzing the preliminary electric field variation parameter set, analyzing the effect of the electric field in the cavity on the power output stability of the device, and identifying the correlation between the electric field and the power output;

[0029] By analyzing the power stability influence coefficient of the section and combining the cavity electric field change parameters, the electric field distribution balance is adjusted, the electric field control data is optimized, and the electric field control optimization parameter set is generated.

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

[0031] Based on the electric field control optimization parameter set, ion adsorption data on the platinum surface is extracted, the adsorption rate of ions on the surfaces of different materials is monitored, and the migration characteristics of the ions are inferred by combining external environmental factors such as time and temperature. The adsorption and migration rate coefficients are defined to generate a dynamic parameter set for adsorption and migration;

[0032] Analyze the effect of the adsorption migration dynamic parameter set on ion flow rate and distribution, and optimize the ratio between the flow rate path and the ion input rate based on the requirements of the ion concentration distribution on the platinum surface;

[0033] The ion concentration control results are analyzed, the proportional relationship between the ion input rate and the flow path is adjusted, the distribution trend of the ion concentration is allocated, and the adsorption migration parameters and the adjustment coefficient are combined to obtain the ion dynamic control results.

[0034] Preferably, the step of acquiring the abnormal intervention adjustment data set is specifically:

[0035] Based on the results of the dynamic ion control, the monitoring device monitors the temperature fluctuation rate and electric field offset in real time during the treatment process, identifies the fluctuation range, eliminates abnormal values ​​of equipment failure, analyzes the average fluctuation rate of the data, and obtains temperature and electric field fluctuation data;

[0036] Analyze the effects of the temperature and electric field fluctuation range on the platinum surface modification rate, use the known platinum modification rate, analyze the relationship between the electric field and temperature, and calculate the modification rate under the differentiated fluctuation range;

[0037] According to the modification rate, the ion distribution path and temperature field ratio within the target range are dynamically adjusted. According to the relationship between the modification rate impact data and the temperature and electric field fluctuation range, the ion flow rate and temperature control range are allocated to generate an abnormal intervention adjustment data set.

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

[0039] Based on the abnormal intervention adjustment data set, the platinum target surface distribution and processing time analysis is performed, surface characteristic data at differentiated processing time points are collected, the surface time distribution is sorted, 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, target treatment path parameters are adjusted, the optimal treatment time and characteristic distribution of the platinum surface are analyzed, and by comparing characteristic changes under differentiated treatment conditions, the operating conditions of treatment temperature, time, and ion concentration are adjusted to obtain target treatment path parameters;

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

[0042] Preferably, the step of adjusting the target processing path parameters is specifically as follows:

[0043] Based on the platinum surface distribution data, the temperature thresholds and ion concentration critical values ​​of different processing stages are extracted, and combined with the equipment operation history data, the influence weight of each parameter on the surface characteristics is 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 change rate of each parameter are adjusted to ensure the stability of the treatment process;

[0045] The adjustment results of each parameter are integrated to generate the target processing path parameters.

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

[0047] Based on the modification rate impact data, setting the adjustment threshold of the ion distribution path and the fluctuation range of the temperature field ratio;

[0048] Monitor the ion flow rate and temperature changes during real-time processing. When it is detected that the parameters exceed the set threshold, the ion input rate and temperature control parameters are gradually adjusted to return the ion distribution path and temperature field ratio to the target range;

[0049] Record parameter change data during the adjustment process, analyze the adjustment effect and optimize the adjustment strategy to ensure the stability of the treatment process and the quality of surface treatment.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The energy distribution control module comprehensively analyzes energy output parameters, temperature gradients, and ion flow path velocity based on equipment operating status information, achieving a more optimal fit between energy distribution and thermal uniformity. This avoids localized overheating or insufficient treatment caused by unbalanced energy distribution in traditional treatments. The electric field environment optimization module, based on a set of energy distribution control parameters, meticulously adjusts the electric field intensity and gradient within the chamber. This reduces interference with the treatment process caused by unstable power output from the electric field device, resulting in a more balanced electric field distribution in the chamber and a more stable environment for ion movement across the platinum surface.

[0052] The Ion Dynamic Adjustment Module leverages electric field control to optimize parameter sets and conducts in-depth analysis of ion adsorption and migration patterns. By adjusting the ion input rate and flow path distribution ratio, the ion concentration distribution trend is more aligned with the target requirements, addressing the issue of uneven ion distribution in traditional treatments leading to varying surface modification effects. The Treatment Anomaly Intervention Module captures temperature fluctuations and electric field offsets during the treatment process in real time. By dynamically adjusting the ion distribution path and temperature field ratio, it promptly corrects the adverse effects of these fluctuations on the platinum surface modification rate, preventing the escalation of anomalies that could lead to a decline in treatment quality and ensuring the continuity and stability of the treatment process.

[0053] The surface treatment quality improvement module adjusts the data set based on abnormal interventions, and precisely controls the distribution ratio and processing time of the target platinum surface. By optimizing the target processing path parameters, the final processing effect is closer to the expected standard. Through the organic connection of various modules, the entire system forms a complete closed loop from energy distribution, electric field optimization, ion adjustment, abnormal intervention to quality improvement. During the processing process, it can be dynamically adjusted according to real-time data, so that the accuracy and uniformity of platinum surface treatment are significantly improved, and the processing efficiency is also improved due to the reduction of abnormal shutdowns and rework. At the same time, the system can adapt to different processing needs and meet diverse surface modification requirements through flexible adjustment of parameters, so that the performance of the platinum surface is more in line with the needs of actual application scenarios, reducing material waste caused by poor processing effects, while improving product quality, it also reduces overall processing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a working principle diagram of the platinum nano-scale surface treatment intelligent control system of the present invention;

[0055] Figure 2 Flowchart obtained for energy distribution and thermal uniformity adaptation degree;

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

[0057] Figure 4 Flowchart for obtaining the optimized parameter set for electric field control;

[0058] Figure 5 Flowchart for adjusting dataset acquisition for unusual interventions. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] See also Figures 1-5 The present invention provides an intelligent control system for platinum nano-scale surface treatment, the system comprising:

[0061] The energy distribution control module will obtain the operating status information of the platinum surface treatment equipment in real time. This information covers the current operating power, operating speed, working status of each component of the equipment, etc. Based on this information, the energy output parameters of the equipment (such as voltage, current, etc.), the temperature gradient of the platinum surface (the temperature change rate at different positions) and the ion flow path velocity data (the flow speed of ions on different paths) are called. By analyzing these data, the degree of adaptation between energy distribution and thermal uniformity is evaluated, that is, whether the current energy distribution can keep the temperature of the platinum surface uniform. According to the evaluation results, the energy control value of the equipment (such as adjusting the output power) and the ion flow balance parameter (such as changing the distribution ratio of ions on different paths) are allocated, and finally an energy distribution control parameter set is generated. This parameter set contains specific values ​​and instructions for controlling the energy output of the equipment and the ion flow distribution.

[0062] The electric field environment optimization module receives the energy distribution control parameter set from the energy distribution control module, from which it extracts the electric field intensity value (the strength of the electric field at different positions in the cavity) and the gradient change (the rate of change of the electric field intensity with position) within the device 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 will cause large changes in the electric field intensity. Based on the analysis results, it adjusts the balance of the electric field distribution in the cavity, for example, by changing the position or output power of the electric field generator so that the electric field intensity in each area of ​​the cavity tends to be consistent, and then generates an electric field control optimization parameter set, which contains specific parameter settings for adjusting the electric field distribution.

[0063] The ion dynamic adjustment module, based on an optimized set of electric field control parameters, conducts in-depth analysis of ion adsorption (the number and velocity of ions adhering to the platinum surface) and migration (the trajectory and velocity of ions moving across the platinum surface) on the platinum surface. Based on these results, the module adjusts the ion input rate (the number of ions input per unit time) and the flow path distribution ratio (the proportion of ion flow along different paths). This module also redistributes the ion concentration distribution (e.g., increasing or decreasing the ion concentration in specific areas of the platinum surface) and dynamic parameter values ​​(e.g., the rate of change of ion concentration over time). Ultimately, it generates an ion dynamic adjustment result, which reflects the adjusted ion distribution and motion.

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

[0065] The surface treatment quality improvement module receives the abnormal intervention adjustment dataset and analyzes the distribution ratio of the target platinum 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, and ion concentration). Ultimately, it generates a platinum target surface treatment data sheet, which records the optimized platinum surface treatment parameters and treatment results, and can be used directly to guide the actual platinum nanoscale surface treatment process.

[0066] Example 1:

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

[0068] Set a fixed time window. The length of the time window can be adjusted according to the processing accuracy requirements of the equipment. For example, 5 minutes is selected as a time window. In each time window, multiple time points are selected at equal time intervals, such as one time point every 30 seconds, and 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 synchronization of the voltage change in the energy output parameter and the temperature change rate in the gradient data, as well as the relationship between the fluctuation of the ion flow path velocity data and the former two, the data is screened. Eliminate those data points with weak correlation, such as abnormal jump values ​​caused by transient sensor failures, and finally obtain the energy output parameters and gradient data that have been preliminarily screened.

[0069] Based on the screened energy output parameters and gradient data, the pre-set ion flow paths are verified for matching. During this verification process, the energy transfer data for each path is individually checked to ensure that it matches the temperature gradient data for the corresponding region. For example, if the energy output parameters for a path show a continuous increase in energy supply, while the temperature gradient in the corresponding region shows a downward trend, it is considered a mismatch. In such cases, the difference between the energy and gradient is calculated, represented by the sum of the deviations between the actual measured values ​​and the theoretically calculated values. The energy and gradient distributions are corrected based on changes in ion flow rate. This is because the energy carried by ions varies at different flow rates, which also affects the heat transfer efficiency of the platinum surface. For example, as the ion flow rate increases, the energy transferred to the platinum surface increases, affecting the local temperature gradient. Based on the specific impact of flow rate on the data, relevant path parameters, such as path width and ion flow inlet angle, are adjusted until the energy and gradient are properly matched, 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, set the thermal uniformity analysis standard, which includes specific indicators such as the maximum temperature difference allowed on the platinum surface, the upper limit of the frequency of temperature fluctuations, etc. Combined with the dynamic changes in the process of equipment operation, such as periodic fluctuations in energy output, phased adjustments in ion flow rate, etc., evaluate the energy distribution under different flow rate conditions. For example, evaluate the energy distribution of each region of the platinum surface when the ion flow rate is 10 m / s, 15 m / s, and 20 m / s, respectively. Compare the thermal uniformity indicators under different flow rate conditions, such as calculating the standard deviation and range of temperature in each region, and optimize the flow rate conditions according to the comparison results. If the temperature standard deviation is the smallest at a flow rate of 15 m / s and meets the thermal uniformity analysis standard, then 15 m / s is used as the optimized flow rate condition. Through the above process, the quantitative results of the adaptation degree of energy distribution and thermal uniformity are finally obtained, which are presented in the form of adaptation index, and the higher the index, the better the adaptation degree. The calculation of the adaptation index takes into account the uniformity of energy distribution, the stability of temperature gradient, and the matching degree with the flow rate condition, etc.

[0071] Example 2:

[0072] The process of obtaining the set of energy distribution control parameters is based on the adaptation degree of energy distribution and thermal uniformity. First, analyze the energy transmission and uniformity changes of the device under differentiated operating conditions. Differentiated operating conditions include the device running at different power levels, different processing stages, and different environmental parameters. For example, when the device is running at low power, energy transmission is mainly concentrated in the core processing area, and the energy coverage range of the edge area is small. When running at high power, the energy transmission range expands, but the energy density in different areas will differ. The difference in processing stages is reflected in the stepwise growth of energy transmission in the startup stage, the constant fluctuation of energy transmission in the stable running stage, and the gradual decay of energy transmission in the shutdown stage. Changes in environmental parameters include the high and low of the internal pressure of the cavity, the difference in inert gas purity, etc., which will affect the transmission efficiency and distribution form of energy inside the device. By analyzing these differentiated conditions one by one, record the changes in energy transmission path, the proportion of energy loss, and the fluctuation of thermal uniformity indicators, and then perform weighted calculation on the energy distribution of the device. In the weighted calculation, according to the influence degree of different regions on the processing quality of the platinum surface, the weight value of the core processing area is higher than that of the edge area, so as to obtain the preliminary energy regulation demand of the device, which clearly indicates the general range of energy output that needs to be increased or decreased in each region.

[0073] Based on the preliminary energy regulation requirements of the device, further analyze the energy balance between devices. Here, the devices refer to the various sub-devices that make up the platinum surface treatment system, including energy supply devices, ion generation devices, temperature regulation components, etc. During the analysis process, the energy transmission efficiency between each sub-device needs to be identified, i.e. the effective utilization rate of energy from one sub-device to another, as well as the load distribution relationship of each sub-device, i.e. the proportion of the energy processing load of each sub-device to the total load of the system. For example, when the output power of the energy supply device is increased, it needs to be detected whether the ion generation device can timely receive and convert the energy. If there is a lag in energy transmission, it indicates that the energy transmission efficiency between the two is low. At the same time, if the load proportion of a certain sub-device continuously exceeds 80% of its rated load, while the load proportion of other sub-devices is less than 50%, it indicates that the load distribution is unbalanced. In response to these situations, the device energy regulation parameters are modified, including adjusting the energy transmission interface parameters between sub-devices, optimizing the operating thresholds of energy conversion modules, etc., so that the energy transmission efficiency of each sub-device remains within a reasonable range and the load distribution tends to be balanced.

[0074] Based on the device energy regulation data set and the thermal uniformity adaptation result, the energy between devices is distributed. During the distribution process, the thermal uniformity adaptation result is used as a benchmark to ensure that the energy distribution meets the requirements of thermal uniformity for platinum surface treatment. For example, if the thermal uniformity adaptation result shows that the temperature uniformity in a certain area is insufficient, 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. At the same time, the required uniformity requirements are matched, i.e. to ensure that after energy distribution, the operating parameters of each sub-device do not exceed the safety threshold, and the energy interaction between them is in a stable state. Through repeated iterative optimization, the energy output parameters of each sub-device, the distribution ratio of energy transmission paths, and the control values of energy conversion efficiency are adjusted, and finally the energy distribution control parameter set is obtained. This parameter set includes specific energy output values of each sub-device, time node control of energy transmission, energy distribution coefficients in different operating stages, etc., and can be directly used to guide the energy regulation operation of the device, so that the entire system meets the requirements of thermal uniformity while achieving balanced operation of each sub-device.

[0075] Example 3:

[0076] The acquisition of the electric field intensity and gradient changes within the device cavity begins with the analysis of the energy distribution control parameter set. Temperature data within the device cavity is extracted from this parameter set. This data is collected by temperature sensors located at different locations within the cavity, including the top, bottom, and sidewalls of the cavity, as well as multiple monitoring points near the platinum surface. The temperature points within each time period are screened. Time periods can be divided according to the stages of the treatment process, such as collecting a temperature point every 10 seconds during the pretreatment stage and a temperature point every 5 seconds during the core treatment stage. The temperature fluctuations are analyzed by combining the temperature change trends at different locations within the cavity. For example, the temperature increase or decrease at a certain monitoring point over multiple consecutive time periods, as well as the temperature difference changes between adjacent monitoring points, is observed. This allows for more detailed temperature data within the device cavity, which reflects the temperature status of different areas of the cavity at different times.

[0077] Based on the temperature data within the device cavity, the electric field intensity corresponding to each temperature point is calculated. Using long-term experimental data, a correlation model between temperature and electric field is established. This model reflects how temperature changes affect electric field intensity under specific device structures. Using this model, the corresponding electric field intensity value is calculated based on the measured value at each temperature point, and the electric field change at each measurement point is identified—the difference in electric field intensity between two adjacent time points. Combined with device structural parameters such as the cavity geometry, the arrangement of internal electrodes, and the distribution of insulating materials, the electric field variations at different locations are compared. For example, the electric field intensity changes at the center and edges of the cavity are analyzed to determine whether they are synchronized, and whether there are significant differences in the electric field gradients between areas near the electrodes and those far from them. This yields electric field distribution and gradient distribution data. The electric field distribution data records the absolute value of the electric field intensity at each location within the cavity, while the gradient distribution data records the rate of change of the electric field intensity with spatial position.

[0078] Based on the electric field distribution and gradient distribution data, the overall electric field distribution in the device cavity is analyzed to determine whether the electric field strength shows a regular distribution and whether there are areas where the local electric field is too strong or too weak. The electric field gradient is optimized in combination with temperature data. For example, when the temperature in a certain area is high and the electric field gradient is large, the electric field gradient is reduced by adjusting the electrode parameters corresponding to that area to reduce the impact of temperature on the electric field. The impact of electric field changes on device performance is analyzed, such as whether the instability of the electric field strength will cause the ion motion trajectory to deviate from the preset path, thereby affecting the adsorption effect of ions on the platinum surface. Based on the analysis results, the stable electric field configuration under different operating conditions is determined. Different operating conditions include adjustments to energy output parameters, changes in ion flow rate, etc. For each operating condition, a corresponding set of electric field strength values ​​and gradient change ranges are determined, ultimately forming complete data on the electric field strength values ​​and gradient changes in the device cavity.

[0079] To obtain the optimized electric field control parameter set, the device cavity's electric field strength and gradient changes are analyzed. The time series of electric field changes is first determined, and the electric field strength and gradient changes at each moment are arranged in chronological order to form a continuous curve. The current electric field strength value is compared with the original electric field data (which represents the device's baseline 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 determine the rate and direction of gradient change. The cavity is divided into different regions based on the device's state, such as the near-pole, mid-pole, and far-pole regions based on their distance from the electrodes. Electric field strength and gradient change thresholds are defined for each region. When the electric field parameters in a region exceed the threshold, control is initiated. This process generates a preliminary electric field change parameter set, which contains the electric field strength, gradient change, and deviation from the threshold for each region at different moments.

[0080] An initial set of electric field variation parameters was analyzed to investigate the impact of the intracavity electric field on the device's power output stability. By comparing the electric field intensity fluctuation curves with the power output fluctuation curves, correlation patterns between the two were identified. Correlations between the electric field and power output were identified, such as whether high-frequency fluctuations in electric field intensity would induce co-frequency fluctuations in power output, and whether electric field anomalies in a specific area would lead to a decrease in overall power output. The power stability impact coefficients of different sections were analyzed. This coefficient quantifies the impact of electric field variations in different regions on power stability; a larger coefficient indicates a more significant impact on power stability. Based on the cavity electric field variation parameters, the balance of the electric field distribution was adjusted, prioritizing regions with the largest power stability impact coefficients. The electric field intensity in these regions was stabilized by varying the electrode voltage output and adjusting the electrode's spatial position. The electric field control data was optimized, including by modifying the output parameters of the electrode control module and adjusting the sampling frequency of the electric field monitoring system. This ultimately generated an optimized electric field control parameter set, which contained the target electric field control values ​​for each region, the time points for control execution, and the corresponding electrode operating parameters.

[0081] Among them, the calculation method of the section power stability influence coefficient is:

[0082]

[0083] Where K is the section power stability influence coefficient, E i is the measured value of the electric field strength at the i-th moment in a certain section, E0 is the standard value of the electric field strength in the section, P i is the device power output value corresponding to the segment at the i-th moment, and n is the total number of sampling times in the time period.

[0084] Example 4:

[0085] The acquisition of the results of dynamic ion control is based on the electric field control optimization parameter set. The ion adsorption data on the platinum surface are extracted from the parameter set. These data are collected by an ion sensor installed near the platinum surface, including the number of ions adsorbed per unit area, the proportion of adsorbed ion types, etc. At the same time, the adsorption rate of ions on surfaces of different materials is monitored, such as the difference in adsorption rate between the platinum surface and the stainless steel inner wall of the cavity, and the change in the adsorption rate of ions in different areas of the platinum surface (such as the edge area and the center area). Combined with time factors, such as different time nodes such as 10 minutes, 20 minutes, and 30 minutes after the start of treatment, and temperature factors, such as the platinum surface temperature maintained at different conditions such as 150°C, 200°C, and 250°C, the migration characteristics of the ions are inferred. For example, at higher temperatures, ions may be more likely to migrate from the edge area of ​​the platinum surface to the center area, and after the treatment time is extended, the overall migration speed of the ions may gradually slow down. 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, an adsorption and migration dynamic parameter set is generated. This parameter set covers detailed data such as the adsorption amount, adsorption rate, migration direction, and migration speed of ions on platinum surfaces and other material surfaces under different time and temperature conditions.

[0086] Analyze the impact of the adsorption and migration dynamic parameter set on ion flow rate and distribution. For example, when the ion adsorption rate in a certain area is high, the ion flow rate near this area will decrease, which in turn affects the overall ion distribution. Based on the requirements of the 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, optimize the ion input rate and flow path distribution ratio. If the ion concentration in the central area of ​​the platinum surface needs to be increased, increase the flow rate proportion of the ion flow path leading to this area and appropriately increase the overall ion input rate.

[0087] Analyze the results of ion concentration control, that is, check whether the ion concentration of each area of ​​the platinum surface meets the preset requirements after optimization. If it is found that the ion concentration of a certain area is still lower than the target value, further adjust the proportional relationship between the ion input rate and the flow velocity path corresponding to that area, such as increasing the flow velocity proportion of that path from 20% to 30%, and at the same time fine-tune the proportion of other paths to maintain the balance of the overall ion flow. Combined with the adsorption migration parameters and the adjustment coefficient, the adjustment coefficient is used to fine-tune the concentration distribution according to the adsorption and migration characteristics of the ions. For example, for areas with strong adsorption capacity, the adjustment coefficient is appropriately reduced to avoid excessive ion aggregation. Finally, the result of ion dynamic control is obtained, which includes information such as the adjusted ion input rate, the flow velocity distribution ratio of each path, and the expected ion concentration in each area of ​​the platinum surface.

[0088] The acquisition of the abnormal intervention adjustment dataset is based on the ion dynamic regulation result. The temperature fluctuation rate and the electric field offset in the processing process are monitored in real time through the temperature sensor and the electric field sensor distributed inside the cavity. The temperature fluctuation rate is the ratio of the maximum change value of the temperature per unit time to the average temperature, and the electric field offset is the difference between the actual measured electric field strength and the set target electric field strength. The range of identifying these fluctuations and offsets, such as the temperature fluctuation rate within ±2% and the electric field offset within ±5%, belongs to the normal range. The abnormal values caused by equipment failure, such as temperature sudden rise or sudden drop data caused by poor contact of sensor line, and electric field strength mutation to zero data caused by electrode short circuit, are excluded. The average fluctuation rate of the remaining valid data is calculated to obtain the temperature and electric field fluctuation data that can reflect the stability of the processing process.

[0089] The influence of the temperature and electric field fluctuation range on the platinum surface modification rate is analyzed. When the temperature fluctuation rate exceeds ±3%, the modification rate may fluctuate significantly; when the electric field offset is too large, it may cause the modification rate to decrease. The known platinum modification rate data, i.e. the modification rate values under different temperature and electric field conditions recorded in the historical processing process, are used to analyze the relationship between the electric field, temperature and modification rate, such as temperature rise within a certain range will accelerate the modification rate, while the electric field offset will inhibit the modification rate. According to this relationship, the modification rate under different fluctuation ranges is calculated, such as when the temperature fluctuation rate is ±4% and the electric field offset is ±6%, the corresponding modification rate specific value.

[0090] According to the calculated modification rate, the ion distribution path and the temperature field ratio in the target range are dynamically adjusted. If the modification rate is lower than expected, it is analyzed whether it is caused by unreasonable ion distribution path or unbalanced temperature field ratio in a certain area, and then the ion flow path in that area is adjusted, such as changing the ion flow injection angle to increase the ion coverage in that area, and adjusting the temperature field ratio of that area and the surrounding area, such as increasing the temperature of that area by 5%. According to the relationship between the modification rate and the temperature and electric field fluctuation range, the adjustment amplitude of the ion flow rate and the specific range of the temperature control are determined, such as adjusting the ion flow rate from 15m / s to 18m / s, and setting the temperature control range to 200℃±5℃. Finally, the abnormal intervention adjustment dataset is generated, which contains the ion distribution path adjustment scheme, the temperature field ratio adjustment parameter, the ion flow rate control value and the temperature control range for different fluctuation conditions.

[0091] Example 5:

[0092] The acquisition of platinum target surface treatment data tables is based on the abnormal intervention adjustment dataset. The platinum target surface distribution and treatment time are analyzed. Surface characteristic data is collected at regular intervals during the treatment process. This interval can be set based on the duration of the treatment process, such as every 15 minutes. The collected surface characteristic data includes surface roughness, nanohardness, and elemental composition. These data are acquired using high-precision surface inspection instruments and cover different areas of the platinum surface. This data is organized into a surface time distribution, representing the surface characteristic values ​​for each area at different time points. Trends in these characteristics are analyzed, such as whether surface roughness gradually decreases or initially decreases and then increases with treatment time, and how the proportion of platinum to other elements in the elemental composition changes. The data is categorized, with regions with similar trends grouped together and time points at the same treatment stage grouped together. This results in platinum surface distribution data, which contains various surface characteristic parameters for different categories and at different time points.

[0093] Based on the platinum surface distribution data, the target treatment path parameters were adjusted. The relationship between the optimal treatment time and the characteristic distribution of the platinum surface was analyzed, that is, at which point in time the surface characteristics of each area were closest to the expected target was determined. The characteristic changes under different treatment conditions were compared, including different treatment conditions such as different treatment temperatures (such as 180°C, 200°C, and 220°C), different treatment times (such as 60 minutes, 90 minutes, and 120 minutes), and different ion concentrations (such as 1×10 15 ions / cm 2 , 3×10 15 ions / cm 2 , 5×10 15 ions / cm 2 For example, consider comparing the hardness and roughness of platinum surfaces treated at 200°C for 90 minutes versus 220°C for 60 minutes. Based on the comparison, the treatment temperature, time, and ion concentration are adjusted. For example, if the surface hardness is closer to the target value when treated at 200°C for 90 minutes, this temperature and time are prioritized, and the ion concentration is adjusted accordingly to match this condition. This results in the target treatment path parameters, which include the specific temperature setting, time, ion concentration, and the adjustment range for each parameter.

[0094] Based on the target processing path parameters, the processing conditions are adjusted according to the current operating parameters. The variable relationship of processing time, temperature, and ion concentration is controlled, such as when the processing temperature is increased, the processing time is appropriately shortened to avoid over-processing; when the ion concentration is increased, the temperature is fine-tuned to maintain the stability of the reaction rate. Real-time processing is carried out according to the adjusted parameters, and the actual values of each parameter and the corresponding surface property data are continuously recorded during the processing. After the processing is completed, these data are sorted and summarized to form a platinum target surface processing data table, which details the time nodes in the processing process, the temperature, ion concentration at each time point, and the corresponding surface property detection results.

[0095] The adjustment of the target processing path parameters is based on the platinum surface distribution data. The temperature threshold and ion concentration critical value of different processing stages are extracted, and the processing stages can be divided into initial stage, reaction stage, and stable stage. The temperature threshold of each stage is the highest and lowest temperature allowed in that stage, and the ion concentration critical value is the minimum value that the ion concentration needs to reach or the maximum value that the ion concentration cannot exceed. Combined with the equipment operation history data, i.e. the recorded parameters and corresponding surface property results in the past similar processing process, the influence weight of each parameter on the surface property is analyzed, such as the influence weight of temperature on surface hardness, the influence weight of ion concentration on surface roughness, etc.

[0096] By comparing the surface property data under different processing conditions, the combination of temperature, time, and ion concentration that can make each surface property reach an optimal state is found. The fluctuation range of each parameter is adjusted, such as adjusting the temperature fluctuation range from ±5℃ to ±3℃ to reduce the characteristic fluctuation; the change rate of the parameter is adjusted, such as adjusting the temperature rising rate from 10℃ / min to 5℃ / min to allow the surface property to have enough time to adapt to the parameter change. The adjustment results of each parameter are integrated to generate the target processing path parameters, which comprehensively consider the threshold requirements of each stage, the influence law in the historical data, and the stability after the parameter adjustment.

[0097] The ion distribution path and the temperature field ratio within the target range are dynamically adjusted based on the modification rate influence data. The adjustment threshold of the ion distribution path is set, i.e. when the deviation of the ion flow rate on a certain path from the target flow rate exceeds the threshold, the path adjustment is started; the fluctuation range of the temperature field ratio is set, i.e. the ratio of the temperature in different regions needs to be maintained within the range.

[0098] The ion flow rate is monitored by flow rate sensors installed on the path, and the temperature change is obtained by temperature sensors distributed on the platinum surface. When the parameters are detected to be beyond the set threshold, the ion input rate is adjusted step by step, such as increasing the ion input rate of the path by 5% when the flow rate of the path is 10% lower than the target value; the temperature control parameters are adjusted, such as fine-tuning the heating power of the region when the temperature ratio of the region to other regions is out of range. Through these adjustments, the ion distribution path and the temperature field ratio gradually return to the target range.

[0099] The parameter change data during the adjustment process are recorded, including the ion flow rate, temperature value, adjustment amplitude and time point before and after the adjustment, etc. These data are analyzed to understand the effect of the adjustment measures on the parameter regression, such as the length of time for a certain adjustment measure to make the parameter return to the target range. According to the analysis results, the adjustment strategy is optimized, such as the adjustment amplitude or frequency, so that the subsequent adjustment is more accurate and effective.

[0100] It should be noted that the relational terms herein, 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 or among the entities or operations. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover non-exclusive inclusions, so that a process, method, article, or apparatus that comprises a list of elements does not only include those elements, but also includes other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0101] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A platinum nano-scale surface treatment intelligent control system, characterized in that: The system comprises: The energy distribution control module uses the operating status information of the platinum surface treatment equipment, the equipment's energy output parameters, the platinum surface temperature gradient, and the ion flow path velocity data to analyze the degree of adaptation between energy distribution and thermal uniformity, allocate equipment energy control values ​​and ion flow balance parameters, and generate an energy distribution control parameter set; The electric field environment optimization module extracts the electric field intensity value and gradient change in the device cavity based on the energy distribution control parameter set, analyzes the impact of the electric field device power output on stability, adjusts the cavity electric field distribution balance, and generates an electric field control 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 the ion concentration, and generates an ion dynamic adjustment result; 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 impact 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 data set; The surface treatment quality improvement module analyzes the distribution ratio and treatment time of the platinum target surface based on the abnormal intervention adjustment data set, adjusts the parameters of the target treatment path, and generates a platinum target surface treatment data table.

2. The platinum nanoscale surface treatment intelligent control system according to claim 1, characterized in that: The steps for obtaining the degree of adaptation between energy distribution and thermal uniformity are specifically 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. The time window is set, and the time points are selected to match the data. By comparing the data correlation and performing data screening, the energy output parameters and gradient data are obtained. Based on the energy output parameters and gradient data, the path is matched and checked, the difference between energy and gradient is calculated, the energy distribution and gradient distribution are corrected in combination with the flow rate change, and the path parameters are adjusted according to the influence of the flow rate on the data to obtain the energy and gradient matching situation; Based on the energy and gradient matching, a thermal uniformity analysis is performed, and the thermal uniformity analysis standard is set. Combined with the dynamic changes in equipment operation, the energy distribution under differentiated flow rate conditions is evaluated, the uniformity indicators are compared, and the flow rate conditions are optimized to obtain the degree of adaptation between energy distribution and thermal uniformity.

3. The platinum nanoscale surface treatment intelligent control system according to claim 2, characterized in that: The steps for obtaining the energy distribution control parameter set are specifically 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 weightedly calculated to obtain the preliminary energy control requirements of the equipment; Based on the preliminary energy control requirements of the equipment, analyze the energy balance between the equipment, identify the relationship between energy transmission efficiency and load distribution between the equipment, and modify the equipment energy control parameters; Combining the energy control data set between the devices with the thermal uniformity adaptation result, the energy between the devices is distributed, the required balance and uniformity requirements are optimized and matched, and the energy distribution control parameter set is obtained.

4. The platinum nanoscale surface treatment intelligent control system according to claim 3, characterized in that: The steps for obtaining the electric field intensity value and gradient variation in the device cavity are specifically as follows: Based on the energy distribution control parameter set, extract the temperature data in the device cavity, screen the temperature points in each time period, combine the temperature change trends of different positions in the cavity, analyze the temperature fluctuation, and obtain the temperature data in the device cavity; Based on the temperature data in the device cavity, the corresponding electric field strength value is calculated for each temperature point. By analyzing the relationship between temperature and electric field, the electric field change at each measurement point is identified. Combined with the device structural parameters, the electric field changes at different positions 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 in the device cavity is analyzed, the electric field gradient is optimized in combination with the temperature data, the impact of electric field changes on device performance is analyzed, the stable electric field configuration under differentiated operating conditions is determined, and the electric field strength value and gradient change in the device cavity are obtained.

5. The platinum nanoscale surface treatment intelligent control system according to claim 4, characterized in that: The steps for obtaining the electric field control optimization parameter set are specifically as follows: Based on the electric field intensity value and gradient change in the device cavity, determine the time series of the electric field change, compare the current electric field intensity value with the original electric field data, analyze the electric field gradient at each moment, and define corresponding thresholds according to the device state partition to generate a preliminary electric field change parameter set; Analyzing the preliminary electric field variation parameter set, analyzing the effect of the electric field in the cavity on the power output stability of the device, and identifying the correlation between the electric field and the power output; By analyzing the power stability influence coefficient of the section and combining the cavity electric field change parameters, the electric field distribution balance is adjusted, the electric field control data is optimized, and the electric field control optimization parameter set is generated.

6. The platinum nanoscale surface treatment intelligent control system according to claim 5, characterized in that: The steps for obtaining the ion dynamic control results are specifically as follows: Based on the electric field control optimization parameter set, ion adsorption data on the platinum surface is extracted, the adsorption rate of ions on the surfaces of different materials is monitored, and the migration characteristics of the ions are inferred by combining external environmental factors such as time and temperature. The adsorption and migration rate coefficients are defined to generate a dynamic parameter set for adsorption and migration; Analyze the effect of the adsorption migration dynamic parameter set on ion flow rate and distribution, and optimize the ratio between the flow rate path and the ion input rate based on the requirements of the ion concentration distribution on the platinum surface; The ion concentration control results are analyzed, the proportional relationship between the ion input rate and the flow path is adjusted, the distribution trend of the ion concentration is allocated, and the adsorption migration parameters and the adjustment coefficient are combined to obtain the ion dynamic control results.

7. The platinum nanoscale surface treatment intelligent control system according to claim 6, characterized in that: The steps for obtaining the abnormal intervention adjustment data set are specifically as follows: Based on the results of the dynamic ion control, the monitoring device monitors the temperature fluctuation rate and electric field offset in real time during the treatment process, identifies the fluctuation range, eliminates abnormal values ​​of equipment failure, analyzes the average fluctuation rate of the data, and obtains temperature and electric field fluctuation data; Analyze the effects of the temperature and electric field fluctuation range on the platinum surface modification rate, use the known platinum modification rate, analyze the relationship between the electric field and temperature, and calculate the modification rate under the differentiated fluctuation range; According to the modification rate, the ion distribution path and temperature field ratio within the target range are dynamically adjusted. According to the relationship between the modification rate impact data and the temperature and electric field fluctuation range, the ion flow rate and temperature control range are allocated to generate an abnormal intervention adjustment data set.

8. The platinum nanoscale surface treatment intelligent control system according to claim 7, characterized in that: The steps for obtaining the platinum target surface treatment data table are as follows: Based on the abnormal intervention adjustment data set, the platinum target surface distribution and processing time analysis is performed, surface characteristic data at differentiated processing time points are collected, the surface time distribution is sorted, the characteristic change trend is analyzed and the data is classified to obtain platinum surface distribution data; Based on the platinum surface distribution data, target treatment path parameters are adjusted, the optimal treatment time and characteristic distribution of the platinum surface are analyzed, and by comparing characteristic changes under differentiated treatment conditions, the operating conditions of treatment temperature, time, and ion concentration are adjusted to obtain target treatment path parameters; Based on the target processing path parameters, the processing conditions are adjusted according to the current operating parameters, the variable relationship between processing time, temperature, and ion concentration is controlled, and real-time processing is performed according to the adjusted parameters to obtain a platinum target surface processing data table.

9. The platinum nanoscale surface treatment intelligent control system according to claim 8, characterized in that: The steps for adjusting the target processing path parameters are specifically as follows: Based on the platinum surface distribution data, the temperature thresholds and ion concentration critical values ​​of different processing stages are extracted, and combined with the equipment operation history data, the influence weight of each parameter on the surface characteristics is analyzed; 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 change rate of each parameter are adjusted to ensure the stability of the treatment process; The adjustment results of each parameter are integrated to generate the target processing path parameters.

10. The platinum nanoscale surface treatment intelligent control system according to claim 9, characterized in that: The steps of dynamically adjusting the ion distribution path and the temperature field ratio within the target range are specifically as follows: Based on the modification rate impact data, setting the adjustment threshold of the ion distribution path and the fluctuation range of the temperature field ratio; Monitor the ion flow rate and temperature changes during real-time processing. When it is detected that the parameters exceed the set threshold, the ion input rate and temperature control parameters are gradually adjusted to return the ion distribution path and temperature field ratio to the target range; Record parameter change data during the adjustment process, analyze the adjustment effect and optimize the adjustment strategy to ensure the stability of the treatment process and the quality of surface treatment.

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