Method and system for predicting use state of grinding fluid

Abstract

The invention relates to the technical field of data prediction, in particular to a method and system for predicting the use state of grinding fluid, and the method comprises the following steps: based on the data of the grinding fluid, analyzing the data, carrying out data normalization processing, transmitting the data to a grinding fluid state monitoring center, and generating a real-time monitoring data set of the grinding fluid. According to the method, the temperature, flow, pressure and concentration data of the grinding fluid are analyzed and normalized, so that the monitoring precision and data comparability are improved, temperature and flow time sequence data are analyzed, the change rate is recognized, abnormal data are extracted, the anomaly detection capability is improved, the weight is dynamically adjusted, and pressure and flow influence calculation is optimized; the service life attenuation rate of the grinding fluid is accurately identified, the future trend is analyzed by adopting a nonlinear prediction technology, the state prediction accuracy is improved, and through refined data monitoring and optimization, the grinding fluid management level is improved, the service life is prolonged, the production stability and efficiency are improved, and the maintenance cost and resource waste are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and particularly to a method and system for predicting the usage status of grinding fluid. Background Art

[0002] The technical field of data prediction includes data analysis, the establishment and application of prediction models. This field mainly involves collecting, processing, and analyzing historical data, and combining appropriate mathematical models and algorithms to predict future trends or states. Data prediction technology is widely applied in multiple industrial fields, including finance, healthcare, manufacturing, etc., to improve the accuracy and effectiveness of decision-making. Its core content includes data collection, processing, model establishment, optimization, and the analysis and application of prediction results, covering a variety of data modeling and algorithm optimization technologies, aiming to provide accurate prediction results for users and support decision-making.

[0003] Among them, the method for predicting the usage status of grinding fluid refers to analyzing relevant data during the use of grinding fluid to establish a prediction model, thereby accurately predicting the usage status of grinding fluid. This method mainly covers various influencing factors during the use of grinding fluid, such as the temperature, pressure, concentration, and flow rate of the liquid, etc. Parameter data is used for monitoring and modeling to determine the usage condition of grinding fluid. By collecting and processing key factors in real time, a mathematical model is used to predict the performance change of grinding fluid to ensure the stability of the grinding process and production efficiency.

[0004] There are multiple deficiencies in the prior art for predicting the status of grinding fluid. During the data collection and processing process, there is a lack of systematic normalization steps, resulting in deviations in data from different sources, affecting the accuracy of prediction results. Anomaly detection mainly relies on fixed threshold judgment, making it difficult to effectively identify complex fluctuations, resulting in some abnormal states not being detected in a timely manner. The calculation of the grinding fluid life attenuation does not fully consider the dynamic changes of multiple factors, and the weight adjustment is relatively fixed, unable to meet the requirements of different working conditions, resulting in insufficient life prediction accuracy. The control strategies for temperature and flow rate are not dynamically optimized according to real-time monitoring data, resulting in situations of lagged adjustment or over-adjustment, affecting the stability of the grinding fluid. Future trend prediction relies on simple linear or empirical models, and fails to fully utilize the deep features of historical data, making it difficult to accurately reflect the status change of the grinding fluid, affecting the reliability of prediction and the rationality of production scheduling, resulting in the grinding fluid management strategy being difficult to accurately match the actual demand, increasing maintenance costs, and at the same time affecting production efficiency and product quality. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a method and system for predicting the usage status of grinding fluid.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for predicting the use state of a grinding fluid, comprising the following steps:

[0007] S1: Based on the data of the grinding fluid, analyze the data, perform data normalization processing and transmit it to the grinding fluid state monitoring center to generate a real-time monitoring dataset of the grinding fluid;

[0008] S2: Based on the real-time monitoring dataset of the grinding fluid, extract the temperature and flow time series data, identify the change rate, extract the flow anomaly and temperature anomaly detection data, and judge the fluctuation situation to generate a temperature and flow fluctuation determination result;

[0009] S3: Call the real-time monitoring dataset of the grinding fluid, extract the temperature, pressure, flow, and concentration parameters, set the weight adjustment interval, analyze the influence of the parameters on the performance decay rate of the grinding fluid, identify the current life decay rate of the grinding fluid, and obtain the grinding fluid life decay coefficient;

[0010] S4: According to the temperature and flow fluctuation determination result, extract the temperature adjustment control parameters and flow optimization parameters, analyze and adjust the flow range and temperature range according to the flow data and temperature data, and obtain the grinding fluid temperature and flow adjustment result;

[0011] S5: Based on the grinding fluid life decay coefficient and the grinding fluid temperature and flow adjustment result, extract the pressure and concentration change trend data, and perform trend analysis in the future time period through a non-linear state prediction unit to obtain the grinding fluid state prediction result.

[0012] As a further solution of the present invention, the real-time monitoring dataset of the grinding fluid includes temperature data, flow data, pressure data, and concentration data, the temperature and flow fluctuation determination result includes flow anomaly data, temperature anomaly data, flow fluctuation situation, and temperature fluctuation situation, the grinding fluid life decay coefficient includes temperature influence weight, pressure influence weight, flow influence weight, and concentration influence weight, the grinding fluid temperature and flow adjustment result includes temperature adjustment parameters, flow optimization parameters, temperature adjustment range, and flow adjustment range, and the grinding fluid state prediction result includes pressure change trend, concentration change trend, and state trend data.

[0013] As a further solution of the present invention, the acquisition steps of the real-time monitoring dataset of the grinding fluid are specifically as follows:

[0014] S111: Based on the data of the grinding fluid, including the measured values of a temperature sensor, a flow monitor, a pressure sensing device, and a concentration analyzer, collect the temperature, flow, pressure, and concentration data of the grinding fluid, calculate the instantaneous change rate, screen the fluctuation interval, and obtain the key fluctuation interval of the grinding fluid;

[0015] S112: Call the key fluctuation range of the grinding fluid, normalize the temperature, flow rate, pressure, and concentration data, and use the formula:

[0016]

[0017] Calculate the normalized deviation degree of the grinding fluid;

[0018] Among them, D norm represents the normalized deviation degree of the grinding fluid, X j represents a single measured value after normalization, μ represents the mean of the normalized data, σ represents the variance of the normalized data, n represents the total number of measurement points, and j represents the index of the measurement point;

[0019] S113: Based on the normalized deviation degree of the grinding fluid, set a deviation threshold, screen the measurement points exceeding the threshold, and combine the timestamp information to establish a real-time monitoring dataset of the grinding fluid.

[0020] As a further solution of the present invention, the steps for obtaining the determination result of the temperature and flow rate fluctuation are specifically as follows:

[0021] S211: Based on the real-time monitoring dataset of the grinding fluid, extract the temperature and flow rate time series data, identify the increments of the flow rate and temperature at adjacent times, and obtain the temperature and flow rate change rate;

[0022] S212: Call the temperature and flow rate change rate, screen the time points whose change rate exceeds the set range, calculate the abnormal flow deviation, and obtain the flow anomaly analysis data;

[0023] S213: Based on the flow anomaly analysis data, analyze the flow deviation and the temperature change trend, and use the formula:

[0024]

[0025] Calculate the temperature anomaly detection value, compare it with the set threshold, and obtain the temperature and flow rate fluctuation determination result;

[0026] Among them, V f represents the temperature anomaly detection value, F i represents the flow rate value at the i-th time point, represents the average flow rate value, m represents the number of abnormal time points, T i represents the temperature value, represents the average temperature value.

[0027] As a further solution of the present invention, the steps for obtaining the grinding fluid life attenuation coefficient are specifically as follows:

[0028] S311: Call the real-time monitoring data set of the grinding fluid, extract the parameters of temperature, pressure, flow rate, and concentration, set the weight adjustment interval, analyze the trend changes of each parameter, and obtain the parameter change trend value;

[0029] S312: Based on the parameter change trend value, analyze the influence degree of the parameter on the performance attenuation rate of the grinding fluid, the adjustment unit adjusts the weight, analyze the influence of pressure and flow rate for high-pressure and high-flow conditions, and use the formula:

[0030]

[0031] Combine the concentration and temperature parameters to obtain the influence value of the grinding fluid performance attenuation;

[0032] Among them, Q represents the influence value of the grinding fluid performance attenuation, W p represents the pressure influence weight, P t represents the pressure value at the current moment, P0 represents the reference pressure value, W f represents the flow rate influence weight, F t represents the flow rate value at the current moment, F0 represents the reference flow rate value, C t represents the concentration value at the current moment, T t represents the temperature value at the current moment;

[0033] S313: Call the influence value of the grinding fluid performance attenuation, combine with the original attenuation data of the grinding fluid, identify the current life attenuation rate, and obtain the grinding fluid life attenuation coefficient.

[0034] As a further solution of the present invention, the steps for obtaining the adjustment result of the grinding fluid temperature and flow rate are specifically as follows:

[0035] S411: According to the determination result of the temperature and flow rate fluctuation, calculate the temperature change rate, flow rate change rate, and temperature and flow rate deviation ratio, screen the data points exceeding the threshold, and obtain the over-limit temperature and flow rate data points;

[0036] S412: Analyze the distribution of the over-limit temperature and flow rate data points, extract the temperature adjustment control parameters and flow rate optimization parameters, adjust the temperature range and flow rate range, and obtain the optimized temperature and flow rate parameter set;

[0037] S413: Call the optimized temperature and flow rate parameter set, analyze the flow rate adjustment ratio and temperature adjustment amplitude according to the temperature range and flow rate range, and use the formula:

[0038]

[0039] Calculate the temperature and flow rate adjustment amount, adjust the temperature and flow rate parameters, and obtain the adjustment result of the grinding fluid temperature and flow rate;

[0040] Among them, V ΔRepresents the temperature and flow adjustment amount, T adj Represents the adjusted temperature value, T ref Represents the reference temperature value, L adj Represents the adjusted flow value, L opt Represents the optimized flow value, T b Represents the b-th temperature data point, T set Represents the set temperature reference, B represents the total number of temperature data points.

[0041] As a further solution of the present invention, the steps for obtaining the prediction result of the grinding fluid state are specifically as follows:

[0042] S511: Based on the grinding fluid life attenuation coefficient and the grinding fluid temperature and flow adjustment results, extract the pressure and concentration change trend data, calculate the concentration change rate, screen the characteristic inflection points, and obtain the grinding fluid concentration change rate value;

[0043] S512: Call the grinding fluid concentration change rate value, calculate the parameters of the trend changing with time, identify the extreme value interval of the trend slope, extract the trend characteristics, and obtain the grinding fluid trend characteristic parameters;

[0044] S513: Based on the grinding fluid trend characteristic parameters, identify the trend change amount, adjust the trend change rate, set the prediction interval, normalize the trend variable, and use the formula:

[0045]

[0046] Calculate the adjusted value of the trend change rate to obtain the prediction result of the grinding fluid state;

[0047] Among them, S represents the adjusted value of the trend change rate, C a Represents the starting value of the trend characteristic parameters in the future time period, C b Represents the ending value of the trend characteristic parameters in the future time period, T d Represents the trend change time interval, C h Represents the slope of the trend change rate, C d Represents the amplitude of the trend change, C e Represents the deviation amount corresponding to the extreme point of the trend change, C f Represents the trend prediction increment.

[0048] The grinding fluid usage state prediction system is used to execute the above-mentioned grinding fluid usage state prediction method, and the system includes:

[0049] The data normalization module collects temperature, flow, pressure, and concentration data based on the data of the grinding fluid, performs normalization processing, and transmits it to the grinding fluid state monitoring center to generate a real-time monitoring data set of the grinding fluid;

[0050] The fluctuation determination module extracts the temperature and flow curve data based on the real-time monitoring dataset of the grinding fluid, analyzes the abnormal flow points and the temperature deviation range, and combines the supply status of the grinding fluid to obtain the temperature and flow fluctuation determination result;

[0051] The performance decay analysis module calls the real-time monitoring dataset of the grinding fluid, extracts four parameters of temperature, pressure, flow rate, and concentration, sets the ratio interval of pressure and concentration, analyzes the influence on the viscosity decay of the grinding fluid, combines the dynamics of the high-pressure liquid supply pipeline, calculates the pressure and flow rate action coefficient, analyzes the wetting ability of the grinding fluid, and obtains the life decay coefficient of the grinding fluid;

[0052] The parameter adjustment module extracts the temperature control interval and flow optimization parameters according to the temperature and flow fluctuation determination result, combines the cooling mode of the grinding fluid, adjusts the flow rate and temperature range, and obtains the temperature and flow adjustment result of the grinding fluid;

[0053] The trend prediction module extracts the dynamic data of pressure and concentration based on the life decay coefficient of the grinding fluid and the temperature and flow adjustment result of the grinding fluid, combines the supply stability of the grinding fluid, analyzes the change trend in the future time period, and obtains the state prediction result of the grinding fluid.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0055] In the present invention, by analyzing and normalizing the temperature, flow rate, pressure, and concentration data of the grinding fluid, the data consistency is ensured, the monitoring accuracy and comparability are improved, the time-series data of temperature and flow rate are analyzed, the change rate is identified and abnormal data is extracted, the abnormal detection ability is improved, the accurate judgment of the fluctuation situation is enhanced, the influence on the performance decay rate of the grinding fluid is deeply analyzed by combining the parameter setting weight adjustment interval, the weight is dynamically adjusted according to different working conditions, the influence calculation of pressure and flow rate is optimized, the life decay rate of the grinding fluid is accurately identified, the flow rate and temperature parameters are dynamically adjusted according to the temperature and flow rate fluctuation situation, the control strategy is optimized, the stability of the grinding fluid under different environmental conditions is ensured, the future trend is analyzed by combining the life decay of the grinding fluid and the temperature and flow adjustment data, the accuracy of the grinding fluid state prediction is improved, effective support is provided for the maintenance of the grinding fluid and the process optimization, and the management level of the grinding fluid is improved, the service life is optimized, the stability and efficiency of the production process are improved, and the maintenance cost and resource waste are reduced through a series of refined data monitoring, analysis, and optimization means. Description of the Drawings

[0056] Figure 1 It is a schematic diagram of the working process of the present invention;

[0057] Figure 2 It is a flowchart for obtaining the real-time monitoring dataset of the grinding fluid in the present invention;

[0058] Figure 3 It is a flowchart for obtaining the determination result of temperature and flow rate fluctuations in the present invention;

[0059] Figure 4 It is a flowchart for obtaining the attenuation coefficient of the life of the grinding fluid in the present invention;

[0060] Figure 5 It is a flowchart for obtaining the adjustment result of the temperature and flow rate of the grinding fluid in the present invention;

[0061] Figure 6 It is a flowchart for obtaining the prediction result of the state of the grinding fluid in the present invention. Detailed implementation manners

[0062] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0064] Embodiment 1

[0065] Please refer to Figure 1 , the present invention provides a technical solution: a method for predicting the use state of a grinding fluid, including the following steps:

[0066] S1: Based on the data of the grinding fluid, including the measured values of the temperature sensor, flow rate monitor, pressure sensing device, and concentration analyzer, analyze the data, perform data normalization processing and transmit it to the grinding fluid state monitoring center to generate a real-time monitoring dataset of the grinding fluid;

[0067] S2: Based on the real-time monitoring dataset of the grinding fluid, extract the temperature and flow rate time series data, identify the change rate, extract the flow anomaly analysis data and temperature anomaly detection unit, and judge the fluctuation situation to generate the determination result of temperature and flow rate fluctuations;

[0068] S3: Call the real-time monitoring data set of the grinding fluid, extract the parameters of temperature, pressure, flow rate, and concentration, set the weight adjustment interval, analyze the influence degree of the parameters on the performance decay rate of the grinding fluid, adjust the weights by the adjustment unit, dynamically calculate the influence weights of pressure and flow rate for high-pressure and high-flow conditions, identify the current life decay rate of the grinding fluid, and obtain the life decay coefficient of the grinding fluid;

[0069] S4: According to the determination result of temperature and flow rate fluctuations, extract the temperature adjustment control parameters and flow rate optimization parameters, analyze and adjust the flow rate range and temperature range based on the flow rate data and temperature data, and obtain the temperature and flow rate adjustment result of the grinding fluid;

[0070] S5: Based on the life decay coefficient of the grinding fluid and the temperature and flow rate adjustment result of the grinding fluid, extract the data of the pressure and concentration change trends, and perform trend analysis for the future time period through the non-linear state prediction unit to obtain the state prediction result of the grinding fluid.

[0071] The real-time monitoring data set of the grinding fluid includes temperature data, flow rate data, pressure data, and concentration data. The determination result of temperature and flow rate fluctuations includes abnormal flow rate data, abnormal temperature data, flow rate fluctuation conditions, and temperature fluctuation conditions. The life decay coefficient of the grinding fluid includes the temperature influence weight, pressure influence weight, flow rate influence weight, and concentration influence weight. The temperature and flow rate adjustment result of the grinding fluid includes temperature adjustment parameters, flow rate optimization parameters, temperature adjustment range, and flow rate adjustment range. The state prediction result of the grinding fluid includes pressure change trend, concentration change trend, and state trend data.

[0072] Please refer to Figure 2 , and the specific steps for obtaining the real-time monitoring data set of the grinding fluid are as follows:

[0073] S111: Based on the data of the grinding fluid, including the measured values of the temperature sensor, flow rate monitor, pressure sensing device, and concentration analyzer, collect the temperature, flow rate, pressure, and concentration data of the grinding fluid, calculate the instantaneous change rate, screen the fluctuation interval, and obtain the key fluctuation interval of the grinding fluid;

[0074] First, perform real-time monitoring of data through the sensors installed in the grinding equipment. The monitor records data once per second. The recorded data is sent to the central processing unit through data acquisition. The data within every five minutes is accumulated and its average value and volatility are calculated. By comparing the data fluctuations of two consecutive five-minute intervals, determine which time intervals have significant fluctuations. The process is applicable to adjusting machine parameters in a timely manner during the processing of precision components to avoid material damage caused by excessive temperature or pressure of the grinding fluid. In this way, obtain the key fluctuation interval of the grinding fluid, that is, the time period with significant fluctuations during the processing.

[0075] S112: Call the key fluctuation range of the grinding fluid, normalize the temperature, flow rate, pressure, and concentration data, and use the formula:

[0076]

[0077] Calculate the normalized deviation degree of the grinding fluid;

[0078] where D norm represents the normalized deviation degree of the grinding fluid, X j represents a single measured value after normalization, μ represents the mean of the normalized data, σ represents the variance of the normalized data, n represents the total number of measurement points, and j represents the index of the measurement point;

[0079] In actual application scenarios, such as in the semiconductor manufacturing process, parameters such as the temperature, flow rate, pressure, and concentration of the grinding fluid need to be maintained within strict control ranges to ensure the uniformity of the wafer surface. After normalization, the mean and variance of the parameters need to be calculated, and high-deviation measurement points are screened based on the calculation results to judge the stability of the grinding fluid state. Normalization is to standardize the dimensions of different physical quantities so that they can be compared on the same numerical scale. The specific method is to subtract the mean of the data set from each data point and then divide by the standard deviation, which can remove the influence of dimensions and enable the fluctuations of each parameter to be evaluated on the same scale. For example, during the wafer grinding process, if there are abnormal fluctuations in the flow rate of the grinding fluid (such as an instantaneous increase of more than 20%), it will lead to uneven grinding and affect the quality of the finished product. It is necessary to perform normalization calculations on the temperature, flow rate, pressure, and concentration of the grinding fluid and further calculate the deviation degree to identify high-deviation data points;

[0080] When calculating the normalized deviation degree of the grinding fluid, substitute specific data for calculation:

[0081] Suppose the parameter data (already normalized) of the grinding fluid collected at a certain moment is: X j = [0.85, 1.10, 0.92, 1.25, 0.89];

[0082] First step, calculate the mean μ:

[0083] Second step, calculate the standard deviation σ:

[0084]

[0085] Third step, calculate the normalized deviation degree D norm of each data point. Taking X4 = 1.25 as an example:

[0086]

[0087]

[0088] D norm = 1.647+(0.152 + 0.098 + 0.082 + 0.248 + 0.112);

[0089] D norm = 1.647 + 0.692 = 2.339;

[0090] For the D of the measurement points norm After performing the same calculation on the values, a set of normalized deviation degree data of the polishing liquid can be obtained. If the D norm value of a certain measurement point exceeds the set deviation threshold, for example, 2.0, it is considered that the measurement data at this point deviates from the normal range, and it is necessary to further investigate the state of the polishing liquid. Finally, the measurement points with high deviation degrees are screened, and the calculated normalized deviation degree of the polishing liquid can provide a basis for the quality control of the polishing process, so as to detect the stability of the state of the polishing liquid in high-precision processing such as semiconductor manufacturing.

[0091] S113: Based on the normalized deviation degree of the polishing liquid, set a deviation threshold, screen the measurement points exceeding the threshold, and combine the timestamp information to establish a real-time monitoring dataset of the polishing liquid;

[0092] First, determine a reasonable deviation threshold. Based on past historical data and engineering experience, for example, when producing high-performance optical lenses, any slight polishing deviation will result in unqualified finished products. By setting a strict deviation threshold, the data points that cause production problems can be automatically identified and marked. Subsequently, combined with the timestamp information of the data points, the specific time and location of the problem occurrence can be accurately traced, and a real-time monitoring dataset of the polishing liquid is established, providing reliable data support and quality control means for the production process.

[0093] Please refer to Figure 3 , and the steps for obtaining the determination result of the temperature and flow rate fluctuation are specifically as follows:

[0094] S211: Based on the real-time monitoring dataset of the polishing liquid, extract the temperature and flow rate time series data, identify the increments of the flow rate and temperature at adjacent moments, and obtain the temperature and flow rate change rate;

[0095] Obtain the sequential data of the temperature and flow rate of the polishing liquid. The data is collected in real time by temperature sensors and flow meters installed on the polishing equipment. For example, use high-precision thermocouple sensors to measure temperature and turbine flow meters to measure flow rate. Assume that at a certain point in time, the temperature recorded by the temperature sensor is 75 °C and the flow rate recorded by the flow meter is 50 liters per minute. Calculate the change rates of flow rate and temperature at adjacent moments. Set a fixed time interval, for example, record data every minute. Calculate the change amounts of temperature and flow rate per minute. Assume that the temperature in the previous minute was 74 °C and the current temperature is 75 °C, then the temperature change amount is 1 °C. The flow rate in the previous minute was 48 liters per minute and the current flow rate is 50 liters per minute, so the flow rate change amount is 2 liters per minute. Compare the change amounts with the set thresholds. For example, set the temperature change threshold to 2 °C / minute and the flow rate change threshold to 5 liters per minute. If the change amount exceeds the threshold, mark it as an abnormal point. Summarize the data of all abnormal points to obtain the temperature and flow rate change rates.

[0096] S212: Call the temperature and flow rate change rates, screen the time points whose change rates exceed the set range, calculate the abnormal flow rate deviation, and obtain the flow rate anomaly analysis data;

[0097] Screen out all the time points marked as abnormal. For example, at a certain moment, the temperature change amount is 3 °C / minute, which exceeds the set threshold of 2 °C / minute and is thus marked as abnormal. Extract the flow rate data corresponding to the abnormal time points. Assume that at the abnormal time points, the flow rate data are 55 liters per minute, 60 liters per minute, and 58 liters per minute respectively. Calculate the flow rate deviation at the abnormal points, calculate the average value of the flow rate values, and then find the difference between each value and the average value. Assume that the average flow rate is 57.7 liters per minute, then the deviations are -2.7, 2.3, and 0.3 respectively. Analyze the deviation data to judge the degree of flow rate fluctuation and obtain the flow rate anomaly analysis data.

[0098] S213: Based on the flow rate anomaly analysis data, analyze the flow rate deviation and the temperature change trend, using the formula:

[0099]

[0100] Calculate the temperature anomaly detection value, compare it with the set threshold, and obtain the temperature and flow rate fluctuation determination result;

[0101] Among them, V f represents the temperature anomaly detection value, F i represents the flow rate value at the i-th time point, represents the average flow rate value, m represents the number of abnormal time points, T i represents the temperature value, represents the average temperature value;

[0102] First, calculate the absolute value of the flow deviation at each abnormal time point. For example, the absolute values of deviations of -2.7, 2.3, and 0.3 are 2.7, 2.3, and 0.3 respectively. Sum up the absolute values to get a total of 5.3. Then, calculate the sum of the squares of the temperature change amounts at the abnormal time points. Assume the temperature change amounts are 3°C, 2.5°C, and 2.8°C respectively, and their squares are 9, 6.25, and 7.84 respectively, with a total of 23.09;

[0103] Substitute the above calculation results into the formula. Assume m = 3,

[0104] Then:

[0105] Finally, compare the calculated temperature anomaly detection value V f with a preset threshold value. Assume the threshold value is 5. If V f is greater than 5, it is determined that there are abnormal fluctuations in temperature and flow, and a temperature - flow fluctuation determination result is generated.

[0106] Please refer to Figure 4 for the specific steps to obtain the attenuation coefficient of the grinding fluid:

[0107] S311: Call the real - time monitoring data set of the grinding fluid, extract parameters such as temperature, pressure, flow rate, and concentration, set the weight adjustment interval, analyze the trend changes of each parameter, and obtain the parameter change trend value;

[0108] Set the weight adjustment intervals for the temperature, pressure, flow rate, and concentration of the grinding machine under different industrial environments. Obtain real - time data through monitoring devices such as temperature sensors, pressure gauges, and flow meters. For example, during a high - temperature and high - pressure grinding process, the monitored temperature is 150°C, the pressure is 10 MPa, the flow rate is 200 L / min, and the concentration of corundum is 15%. The real - time acquisition and analysis of data are particularly crucial for setting weights. Based on the real - time change data of the parameters, quantitatively analyze the change trend through calculation software, and further obtain the change trend values of each parameter. The change trend values evaluate their actual impact on the grinding efficiency by comparing with historical data under different working conditions, providing a basis for the optimization and adjustment of the grinding machine, which is particularly important in the processing of high - precision aviation materials, and finally generate the parameter change trend values.

[0109] S312: Based on the parameter change trend values, analyze the influence degree of the parameters on the attenuation rate of the grinding fluid performance. The adjustment unit adjusts the weights, analyzes the influence of pressure and flow rate for high - pressure and high - flow working conditions, and uses the formula:

[0110]

[0111] Combine the concentration and temperature parameters to obtain the grinding fluid performance attenuation influence value;

[0112] Among them, Q represents the influence value of the performance attenuation of the grinding fluid, W p represents the pressure influence weight, P t represents the pressure value at the current moment, P0 represents the reference pressure value, W f represents the flow rate influence weight, F t represents the flow rate value at the current moment, F0 represents the reference flow rate value, C t represents the concentration value at the current moment, T t represents the temperature value at the current moment;

[0113] Determine the pressure and flow rate influence weights under high-pressure and high-flow conditions. The specific method includes extracting the real-time measured pressure and flow rate, and comparing them with the reference values to ensure the rationality of the calculation. Under a certain typical working condition, the real-time monitored pressure is 12 MPa, the flow rate is 250 L / min, the reference pressure is set at 10 MPa, the reference flow rate is set at 200 L / min, the current temperature is 150 °C, and the current concentration is 15%;

[0114] Assume the pressure influence weight W p = 0.6, the flow rate influence weight W f = 0.4, substitute the actual values for calculation:

[0115]

[0116] The calculation result shows that the current attenuation influence value of the grinding fluid is 0.141, which means that under the current working condition, the performance of the grinding fluid is decaying relatively fast. Compared with the historical average value, if this value is too high, it indicates that the grinding efficiency has decreased, and it is necessary to adjust the concentration or flow rate of the grinding fluid. The finally calculated attenuation influence value of the grinding fluid performance can be used as an important basis for adjusting the grinding parameters to ensure the stability of the grinding process.

[0117] S313: Call the influence value of the performance attenuation of the grinding fluid, combine it with the original attenuation data of the grinding fluid, identify the current life attenuation rate, and obtain the life attenuation coefficient of the grinding fluid;

[0118] The specific process of comparing the influence value of the performance attenuation of the grinding fluid with the historical attenuation data to identify the current life attenuation rate of the grinding fluid includes calling the historical data from the database, such as the life attenuation records under the same parameters in the past year, and judging whether the performance of the grinding fluid is within the expected range by comparing the current influence value with the historical average value. For example, if the current life attenuation rate is 0.05% / day and the historical average rate is 0.03% / day, it means that the performance of the grinding fluid is decaying relatively fast, and it is necessary to take measures to adjust the grinding parameters or replace the grinding fluid. The finally calculated life attenuation coefficient of the grinding fluid will be used as the key data for further optimizing the grinding process.

[0119] Please refer toFigure 5 , the steps for obtaining the grinding fluid temperature and flow rate adjustment results are specifically as follows:

[0120] S411: According to the temperature and flow rate fluctuation determination results, calculate the temperature change rate, flow rate change rate, and temperature-flow rate deviation ratio, screen out the data points exceeding the threshold, and obtain the over-limit temperature-flow rate data points;

[0121] By real-time monitoring of the temperature and flow rate of the fluid, the risks of equipment overheating or excessive pressure can be effectively prevented. The calculated temperature change rate is the difference between the current temperature and the initial temperature divided by time, and the flow rate change rate is the ratio of the current flow rate to the set flow rate. The accurate calculation of these two parameters provides data support for safe drilling. The formula for the temperature-flow rate deviation ratio is the temperature change rate divided by the flow rate change rate. The ratio reflects the degree of deviation between temperature and flow rate. By comparing the temperature-flow rate deviation ratio with the set threshold, the data points exceeding the threshold are screened out. The data points indicate equipment failures or changes in environmental factors and must be analyzed in detail. During the screening process, the set threshold is 0.1, and any data points exceeding this threshold will be marked and further analyzed to quickly respond to abnormal situations, obtaining the over-limit temperature-flow rate data points, which will be used to generate maintenance or adjustment reports.

[0122] S412: Analyze the distribution of the over-limit temperature-flow rate data points, extract the temperature adjustment control parameters and flow rate optimization parameters, and adjust the temperature range and flow rate range to obtain the optimized temperature-flow rate parameter set;

[0123] Call the over-limit temperature-flow rate data points and analyze their distribution. Since abnormal fluctuations in temperature and flow rate lead to a decrease in production efficiency or product quality problems, by analyzing the distribution of the over-limit data points, the areas or time periods where problems occur most frequently can be identified, and the corresponding temperature adjustment control parameters and flow rate optimization parameters can be extracted. For example, if it is found that the temperature frequently exceeds the limit in a certain specific reactor, the heating element settings of the reactor need to be adjusted or the material input speed needs to be improved. By adjusting the temperature range and flow rate range, the performance of the entire equipment can be optimized to ensure the stability of the production process and the consistency of product quality, obtaining the optimized temperature-flow rate parameter set, which will directly affect the settings and control strategies of the next production cycle.

[0124] S413: Call the optimized temperature-flow rate parameter set, and based on the temperature range and flow rate range, analyze the flow rate adjustment ratio and temperature adjustment amplitude, using the formula:

[0125]

[0126] Calculate the temperature-flow rate adjustment amount, adjust the temperature-flow rate parameters, and obtain the grinding fluid temperature-flow rate adjustment results;

[0127] Among them, V ΔRepresents the temperature and flow rate adjustment amount, T adj Represents the temperature value after adjustment, T ref Represents the reference temperature value, L adj Represents the flow rate value after adjustment, L opt Represents the optimized flow rate value, T b Represents the b-th temperature data point, T set Represents the set temperature reference, B represents the total number of temperature data points;

[0128] For a high-precision semiconductor manufacturing environment, the precise control of temperature and flow rate has a direct impact on the product yield. For example, in the wafer wet etching process, the temperature and flow rate of the chemical solution directly affect the etching rate and uniformity. If the solution temperature deviates from the set value by more than ±0.5°C, an unacceptable deviation in the etching depth will occur. Therefore, it is necessary to calculate the flow rate adjustment ratio and the temperature adjustment range to ensure the stability and consistency of process parameters. First, calculate the temperature adjustment range, using the adjusted temperature value T adj and the reference temperature value T ref The difference between them is: |T adj -T ref |;

[0129] Then calculate the flow rate adjustment ratio, considering the adjusted flow rate value L adj and the optimized flow rate value L opt The relationship between them, set the reference temperature value T ref = 25.0°C, the adjusted temperature T adj = 26.2°C, set the temperature reference value T set = 25.0°C, the flow rate adjustment value L adj = 8.5 L / min, the optimized flow rate value L opt = 9.0 L / min, select B = 3 temperature measurement points, and their specific measurement values are T1 = 26.1°C, T2 = 26.3°C, T3 = 26.0°C;

[0130] Substitute into the calculation as follows:

[0131] Calculate each part:

[0132] Temperature adjustment range calculation: |26.2 - 25.0| = 1.2;

[0133] Flow rate adjustment ratio calculation:

[0134] Temperature measurement point deviation calculation:

[0135] |26.1 - 25.0| = 1.1, |26.3 - 25.0| = 1.3, |26.0 - 25.0| = 1.0;

[0136]

[0137] Final calculated temperature and flow rate adjustment amount: V Δ = 0.137 + 1.13 = 1.267;

[0138] The calculated temperature and flow rate adjustment amount V Δ = 1.267. The value indicates that the adjustment ranges of the current temperature and flow rate are within the acceptable range, but the temperature is slightly on the high side. Therefore, during the etching process, it is necessary to slightly reduce the heating power or increase the solution circulation flow rate to maintain the etching uniformity and precision. Finally, the adjustment result of the polishing liquid temperature and flow rate is obtained, and this value will be used as the basis for process optimization to further adjust and control the equipment to ensure the stability of the production process.

[0139] Please refer to Figure 6 , and the steps for obtaining the prediction result of the polishing liquid state are specifically as follows:

[0140] S511: Based on the polishing liquid life attenuation coefficient and the polishing liquid temperature and flow rate adjustment result, extract the pressure-concentration change trend data, calculate the concentration change rate, screen the characteristic inflection points, and obtain the polishing liquid concentration change rate value;

[0141] First, obtain the life attenuation coefficient of the polishing liquid. This coefficient reflects the degree of performance degradation of the polishing liquid during use. For example, after a certain polishing liquid is continuously used for 100 hours, its polishing efficiency decreases by 20%, then its life attenuation coefficient is 0.2. Adjust the temperature and flow rate of the polishing liquid. Assume that during the polishing process, it is found that the temperature of the polishing liquid rises to 40°C, resulting in a decrease in the polishing efficiency. Use a cooling system to control the temperature at 25°C and adjust the flow rate of the polishing liquid to keep it at 2 liters per minute to ensure stable polishing effect. After the above adjustments are completed, start monitoring the concentration change trend of the polishing liquid under different pressures. For example, when the pressure is 1.5 bar, the concentration of solid particles in the polishing liquid is measured to be 30%, and when the pressure increases to 2.0 bar, the concentration rises to 35%. By analyzing the data, the relationship curve between pressure and concentration can be plotted to obtain the polishing liquid concentration change rate value.

[0142] S512: Call the polishing liquid concentration change rate value, calculate the parameters of the trend changing with time, identify the extreme value interval of the trend slope, extract the trend characteristics, and obtain the polishing liquid trend characteristic parameters;

[0143] Based on the pressure concentration change trend data, calculate the rate of change of concentration over time. For example, in the first 10 minutes after the start of grinding, the concentration increases from 30% to 35%, then the rate of change of concentration is (35% - 30%) / 10 = 0.5% per minute. For the rate data, use the non-linear fitting method to fit the trend curve of concentration change. This can use methods such as polynomial fitting and exponential fitting to find the most suitable data model. After fitting, analyze the slope change of the curve to identify the extreme points of the rate of change. For example, if it is found that the slope reaches the maximum value at a certain moment, it means that the concentration changes fastest at this time. The extreme points and the corresponding slope values are the trend characteristic parameters of the grinding fluid.

[0144] S513: Based on the trend characteristic parameters of the grinding fluid, identify the trend change amount, adjust the trend change rate, set the prediction interval, normalize the trend variable, and use the formula:

[0145]

[0146] Calculate the adjusted value of the trend change rate to obtain the prediction result of the grinding fluid state;

[0147] Among them, S represents the adjusted value of the trend change rate, C a represents the starting value of the trend characteristic parameters in the future time period, C b represents the ending value of the trend characteristic parameters in the future time period, T d represents the time interval of the trend change, C h represents the slope of the trend change rate, C d represents the amplitude of the trend change, C e represents the deviation amount corresponding to the extreme point of the trend change, C f represents the trend prediction increment;

[0148] Determine the predicted time interval. For example, predict the change of the grinding fluid state within the next 30 minutes. Using the trend characteristic parameters, calculate the trend change amount within this time interval. Suppose through calculation, the trend change amount is obtained as 0.8%, which means that within the next 30 minutes, the concentration is expected to increase by 0.8%. Calculate the adjusted value of the trend change rate, set the length of the prediction interval to 30 minutes, and assume the starting concentration is 35% and the ending concentration is 35.8%;

[0149] Then the change amount is: |C a -C b | = |35 - 35.8| = 0.8;

[0150] The time interval is 30 minutes, and the rate of change is calculated as follows:

[0151] Combined with the slope of the rate of change, the amplitude of the change, the deviation amount of the extreme point, and the prediction increment, etc. Suppose the slope of the trend change rate Ch = 0.02, the trend change amplitude C d = 0.8, the deviation of the extreme point of the trend change C e = 0.1, the trend prediction increment C f = 0.8, Substitute the value into the formula:

[0152]

[0153] S = 0.0267 + 0.1265 - 0.125;

[0154] S = 0.0282;

[0155] Finally, the adjusted value of the trend change rate S = 0.0282 is obtained. Combine this adjusted value with the trend prediction increment and perform normalization processing to calculate the confidence interval of the future state change of the grinding fluid, and obtain the prediction result of the grinding fluid state.

[0156] The grinding fluid usage state prediction system is used to execute the above-mentioned grinding fluid usage state prediction method. The system includes:

[0157] The data normalization module collects temperature, flow rate, pressure, and concentration data based on the data of the grinding fluid, performs normalization processing, transmits it to the grinding fluid state monitoring center, and generates a real-time monitoring dataset of the grinding fluid;

[0158] The fluctuation determination module extracts the temperature and flow rate curve data based on the real-time monitoring dataset of the grinding fluid, analyzes the abnormal points of the flow rate and the temperature deviation range, and combines the supply state of the grinding fluid to obtain the temperature and flow rate fluctuation determination result;

[0159] The performance decay analysis module calls the real-time monitoring dataset of the grinding fluid, extracts four parameters of temperature, pressure, flow rate, and concentration, sets the ratio interval of pressure and concentration, analyzes the impact on the viscosity decay of the grinding fluid, combines the dynamics of the high-pressure liquid supply pipeline, calculates the pressure and flow rate action coefficient, and analyzes the wetting ability of the grinding fluid to obtain the life decay coefficient of the grinding fluid;

[0160] The parameter adjustment module extracts the temperature control interval and flow rate optimization parameters according to the temperature and flow rate fluctuation determination result, combines the cooling mode of the grinding fluid, and adjusts the flow rate and temperature range to obtain the temperature and flow rate adjustment result of the grinding fluid;

[0161] The trend prediction module extracts the dynamic data of pressure and concentration based on the life decay coefficient of the grinding fluid and the temperature and flow rate adjustment result of the grinding fluid, combines the supply stability of the grinding fluid, analyzes the change trend in the future time period, and obtains the prediction result of the grinding fluid state.

[0162] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for predicting the usage state of a polishing liquid, characterized in that, It includes the following steps: S1: Based on the data of the grinding fluid, parse the data, perform data normalization processing and transmit it to the grinding fluid status monitoring center to generate a real-time monitoring dataset of the grinding fluid; S2: Based on the real-time monitoring dataset of the grinding fluid, extract the temperature and flow time series data, identify the change rate, extract the abnormal flow and abnormal temperature detection data, and judge the fluctuation situation to generate a temperature and flow fluctuation determination result; S3: Call the real-time monitoring dataset of the grinding fluid, extract the temperature, pressure, flow, and concentration parameters, set the weight adjustment interval, analyze the influence of the parameters on the performance attenuation rate of the grinding fluid, identify the current life attenuation rate of the grinding fluid, and obtain the grinding fluid life attenuation coefficient; S4: According to the temperature and flow fluctuation determination result, extract the temperature adjustment control parameter and the flow optimization parameter, analyze and adjust the flow range and temperature range according to the flow data and temperature data to obtain the grinding fluid temperature and flow adjustment result; S5: Based on the grinding fluid life attenuation coefficient and the grinding fluid temperature and flow adjustment result, extract the pressure and concentration change trend data, and perform trend analysis in the future time period through the non-linear state prediction unit to obtain the grinding fluid status prediction result.

2. The method for predicting the usage state of the polishing liquid according to claim 1, wherein The real-time monitoring dataset of the grinding fluid includes temperature data, flow data, pressure data, and concentration data. The temperature and flow fluctuation determination result includes abnormal flow data, abnormal temperature data, flow fluctuation situation, and temperature fluctuation situation. The grinding fluid life attenuation coefficient includes temperature influence weight, pressure influence weight, flow influence weight, and concentration influence weight. The grinding fluid temperature and flow adjustment result includes temperature adjustment parameter, flow optimization parameter, temperature adjustment range, and flow adjustment range. The grinding fluid status prediction result includes pressure change trend, concentration change trend, and status trend data.

3. The method for predicting the use state of the abrasive liquid according to claim 1, wherein, The specific steps for obtaining the real-time monitoring dataset of the grinding fluid are as follows: S111: Based on the data of the grinding fluid, including the measured values of the temperature sensor, flow monitor, pressure sensing device, and concentration analyzer, collect the temperature, flow, pressure, and concentration data of the grinding fluid, calculate the instantaneous change rate, screen the fluctuation interval, and obtain the key fluctuation interval of the grinding fluid; S112: Call the key fluctuation interval of the grinding fluid, normalize the temperature, flow, pressure, and concentration data, and use the formula: Calculate the normalized deviation degree of the grinding fluid; Among them, D norm represents the normalized deviation degree of the grinding fluid, X j represents a single measured value after normalization, μ represents the mean of the normalized data, σ represents the variance of the normalized data, n represents the total number of measurement points, and j represents the index of the measurement point; S113: Based on the normalized deviation degree of the grinding fluid, set the deviation threshold, screen the measurement points exceeding the threshold, and establish a real-time monitoring dataset of the grinding fluid in combination with the timestamp information.

4. The abrasive liquid usage state prediction method according to claim 1, characterized in that, The specific steps for obtaining the temperature and flow fluctuation determination result are as follows: S211: Based on the real-time monitoring dataset of the grinding fluid, extract the temperature and flow time series data, identify the increments of the flow and temperature at adjacent moments, and obtain the temperature and flow change rate; S212: Call the temperature and flow change rate, screen the time points where the change rate exceeds the set range, calculate the abnormal flow deviation, and obtain the abnormal flow analysis data; S213: Based on the abnormal flow analysis data, analyze the flow deviation and the temperature change trend, and use the formula: Calculate the temperature anomaly detection value, compare it with the set threshold, and obtain the temperature and flow fluctuation determination result; Among them, V f represents the temperature anomaly detection value, F i represents the flow value at the i-th time point, represents the average flow value, m represents the number of anomaly time points, T i represents the temperature value, represents the average temperature value.

5. The method for predicting the usage state of the grinding fluid according to claim 1, wherein The specific steps for obtaining the grinding fluid life attenuation coefficient are as follows: S311: Call the real-time monitoring data set of the grinding fluid, extract the parameters of temperature, pressure, flow rate, and concentration, set the weight adjustment interval, analyze the trend changes of each parameter, and obtain the parameter change trend value; S312: Based on the parameter change trend value, analyze the influence degree of the parameter on the performance attenuation rate of the grinding fluid, the adjustment unit adjusts the weight, analyzes the influence of pressure and flow rate for high-pressure and high-flow conditions, and uses the formula: Combined with the concentration and temperature parameters, obtain the grinding fluid performance attenuation influence value; Among them, Q represents the influence value of the performance attenuation of the grinding fluid, and W p represents the pressure influence weight, and P t represents the pressure value at the current moment, P0 represents the reference pressure value, and W f represents the flow rate influence weight, and F t represents the flow rate value at the current moment, F0 represents the reference flow rate value, and C t represents the concentration value at the current moment, and T t represents the temperature value at the current moment; S313: Call the grinding fluid performance attenuation influence value, combine it with the original attenuation data of the grinding fluid, identify the current life attenuation rate, and obtain the grinding fluid life attenuation coefficient.

6. The method for predicting the usage state of the polishing liquid according to claim 1, wherein The specific steps for obtaining the grinding fluid temperature and flow adjustment result are as follows: S411: According to the temperature and flow fluctuation determination result, calculate the temperature change rate, flow rate change rate, and temperature and flow deviation ratio, screen the data points exceeding the threshold, and obtain the over-limit temperature and flow data points; S412: Analyze the distribution of the over-limit temperature and flow data points, extract the temperature adjustment control parameters and flow optimization parameters, adjust the temperature range and flow range, and obtain the optimized temperature and flow parameter set; S413: Call the optimized temperature and flow parameter set, analyze the flow adjustment ratio and temperature adjustment amplitude according to the temperature range and flow range, and use the formula: Calculate the temperature and flow adjustment amount, adjust the temperature and flow parameters, and obtain the grinding fluid temperature and flow adjustment result; Among them, V Δ represents the temperature flow adjustment amount, T adj represents the adjusted temperature value, T ref represents the reference temperature value, L adj represents the adjusted flow value, L opt represents the optimized flow value, T b represents the b-th temperature data point, T set represents the set temperature reference, and B represents the total number of temperature data points.

7. The method for predicting the usage state of the grinding fluid according to claim 1, wherein The specific steps for obtaining the grinding fluid state prediction result are as follows: S511: Based on the grinding fluid life attenuation coefficient and the grinding fluid temperature and flow adjustment result, extract the pressure and concentration change trend data, calculate the concentration change rate, screen the characteristic inflection points, and obtain the grinding fluid concentration change rate value; S512: Call the grinding fluid concentration change rate value, calculate the parameter of the trend changing with time, identify the extreme value interval of the trend slope, extract the trend characteristics, and obtain the grinding fluid trend characteristic parameter; S513: Based on the grinding fluid trend characteristic parameter, identify the trend change amount, adjust the trend change rate, set the prediction interval, and normalize the trend variable, and use the formula: Calculate the trend change rate adjustment value to obtain the grinding fluid state prediction result; Among them, S represents the trend change rate adjustment value, C a represents the starting value of the trend characteristic parameter in the future time period, C b represents the ending value of the trend characteristic parameter in the future time period, T d represents the trend change time interval, C h represents the slope of the trend change rate, C d represents the amplitude of the trend change, C e represents the deviation amount corresponding to the extreme point of the trend change, C f represents the trend prediction increment.

8. A prediction system for the usage state of a grinding fluid, characterized in that, According to the grinding fluid usage state prediction method according to any one of claims 1-7, the system includes: The data normalization module collects the data of temperature, flow rate, pressure, and concentration based on the data of the grinding fluid, performs normalization processing, transmits it to the grinding fluid state monitoring center, and generates a real-time monitoring data set of the grinding fluid; The fluctuation determination module extracts the temperature and flow curve data based on the real-time monitoring data set of the grinding fluid, analyzes the flow anomaly points and the temperature offset range, and combines the supply state of the grinding fluid to obtain the temperature and flow fluctuation determination result; The performance attenuation analysis module calls the real-time monitoring dataset of the grinding fluid, extracts four parameters of temperature, pressure, flow rate, and concentration, sets the ratio interval of pressure and concentration, analyzes the influence on the viscosity attenuation of the grinding fluid, combines the dynamics of the high-pressure liquid supply pipeline, calculates the pressure and flow rate action coefficient, analyzes the wetting ability of the grinding fluid, and obtains the life attenuation coefficient of the grinding fluid; The parameter adjustment module extracts the temperature control interval and flow rate optimization parameters according to the determination result of the temperature and flow rate fluctuation, combines the cooling mode of the grinding fluid, adjusts the flow rate and temperature range, and obtains the adjustment result of the grinding fluid temperature and flow rate; Based on the life attenuation coefficient of the grinding fluid and the adjustment result of the grinding fluid temperature and flow rate, the trend prediction module extracts the dynamic data of pressure and concentration, combines the supply stability of the grinding fluid, analyzes the change trend in the future time period, and obtains the prediction result of the grinding fluid state.

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