Automatic dilution mixing and supply control method and control system for polishing liquid

Abstract

The application relates to a kind of automatic dilution mixing supply control method and control system of polishing liquid, which carries out in-depth analysis to polishing liquid stock solution parameter and mixing process data by artificial intelligence algorithm, accurately calculates theoretical dilution parameter and actual dilution parameter, and realizes quantitative evaluation and automatic adjustment to mixed state according to the deviation threshold dynamically calculated, significantly improves the stability of polishing liquid concentration, reduces the processing quality problem caused by concentration fluctuation, improves product pass rate and reduces production cost, secondly, using real-time monitoring and dynamic feedback mechanism, the system can quickly respond to working condition change, automatically adjust supply parameter, avoid production interruption or product quality decline caused by parameter deviation, significantly improve the timeliness and effectiveness of supply, reduce equipment loss and production delay risk, finally, through closed-loop optimization mechanism, the system can continuously accumulate data and self-learning, adapt to different batches of stock solution parameter difference and complex working condition change, significantly reduce the burden of operator, improve the scientificity and accuracy of control, provide reliable guarantee for realizing intelligent, high-precision industrial production.

Description

Technical Field

[0001] This invention relates to an automatic dilution, mixing, and supply control method and system for grinding slurry, belonging to the field of grinding slurry supply technology. Background Technology

[0002] In modern industrial production, grinding slurry is an important processing auxiliary material, and the accuracy of its dilution and mixing has a crucial impact on processing quality and efficiency. Traditional methods of supplying grinding slurry dilution and mixing mainly rely on manual experience or simple automated equipment. Manual mixing is not only inefficient but also easily affected by the operator's subjective factors and fatigue, leading to large fluctuations in the grinding slurry concentration, making it difficult to meet the requirements of high-precision processing. While existing automated equipment improves production efficiency to some extent, it lacks intelligent analysis and adaptive adjustment capabilities, and can only operate in a fixed mode according to preset parameters. It cannot adjust the dilution and mixing ratio in a timely manner according to changes in actual working conditions, making it difficult to guarantee the quality of grinding slurry supply when faced with differences in parameters between different batches of raw materials or changes in the processing environment.

[0003] In recent years, the application of artificial intelligence (AI) technology in the industrial field has become increasingly widespread, and its powerful data analysis and processing capabilities have brought new possibilities for the precise supply of grinding slurries. However, the technology of applying AI to the automatic dilution, mixing, and supply control of grinding slurries is still in its developmental stage. Most existing technologies can only achieve basic data acquisition and simple logical judgment, lacking the ability to deeply analyze changes in grinding slurry parameters under complex working conditions, and failing to accurately establish the dynamic correlation between theoretical and actual parameters. Furthermore, in terms of real-time monitoring and adaptive control of the grinding slurry mixing process, existing solutions suffer from slow response speed and insufficient adjustment precision, making it difficult to achieve intelligent and precise management of grinding slurry supply.

[0004] In addition, there are many shortcomings in the data interaction and collaborative work between the modules of the grinding fluid supply system. The stability and reliability of data transmission cannot be effectively guaranteed, which limits the optimization and upgrading of the entire supply system. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic dilution, mixing, and supply control method and control system for grinding slurry to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an automatic dilution, mixing, and supply control method for grinding slurry, the control method comprising:

[0007] S1: The sensor module collects the initial parameter data of the grinding slurry stock solution and uploads this data to the cloud server, marking it as basic monitoring data;

[0008] S2: The data analysis module extracts the theoretical process parameters of the grinding fluid to be mixed from the basic monitoring data and calculates the theoretical dilution parameters;

[0009] S3: The sensor module collects real-time process parameters of the grinding fluid in the mixing tank during the grinding fluid dilution and mixing stage, and transmits them to the data analysis module.

[0010] S4: The data analysis module processes real-time process parameters to obtain actual dilution parameters;

[0011] S5: The cloud server compares the actual dilution parameters with the theoretical dilution parameters, calculates the deviation threshold based on artificial intelligence algorithms, determines whether it is within the normal control range, and triggers an early warning when it exceeds the normal range;

[0012] S6: The cloud server generates a control adjustment strategy based on the deviation threshold and evaluates whether the mixing state of the grinding fluid meets the process requirements;

[0013] S7: The dilution and mixing module automatically adjusts the theoretical dilution parameters according to the control and adjustment strategy to achieve precise dilution and mixing supply of the grinding slurry, and feeds back the execution results to the cloud server.

[0014] Furthermore, the theoretical process parameters include the temperature and concentration of the grinding slurry stock solution.

[0015] Furthermore, the theoretical dilution parameters include the final dilution ratio of the grinding slurry and the temperature compensation coefficient of the grinding slurry.

[0016] Furthermore, the formula for calculating the temperature compensation coefficient of the polishing slurry is as follows:

[0017] ;

[0018] Where K is the temperature compensation coefficient, and a is the Arrhenius coefficient, which is calculated using the following formula: Ea is the activation energy, R is the gas constant, Tt is the target temperature, and T0 is the initial temperature of the original grinding slurry.

[0019] Furthermore, the final dilution ratio of the grinding slurry is calculated as follows:

[0020] The formula for calculating the basic dilution ratio of the grinding slurry to be mixed is:

[0021] ;

[0022] Rb is the base dilution ratio, C0 is the concentration of the original grinding slurry, Cr is the residual concentration of the diluent, Ct is the target concentration, and m is the mixing efficiency coefficient.

[0023] The formula for calculating the final dilution ratio of the grinding slurry to be mixed is:

[0024] ;

[0025] Where Rt is the final dilution ratio, Rb is the basic dilution ratio, K is the temperature compensation coefficient, and S is the safety factor.

[0026] Furthermore, the calculation of the deviation threshold includes concentration deviation ΔC, temperature deviation ΔT, rate of change deviation ΔR, concentration deviation threshold ΔCm, temperature deviation threshold ΔTm, and rate of change threshold ΔRm;

[0027] The formula for calculating the concentration deviation ΔC is: ;

[0028] The formula for calculating temperature deviation ΔT is: ;

[0029] The formula for calculating the rate of change deviation ΔR is: ;

[0030] The formula for calculating the concentration deviation threshold ΔCm is: ;

[0031] The formula for calculating the temperature deviation threshold ΔTm is: ;

[0032] The formula for calculating the rate of change threshold ΔRm is: ;

[0033] Where Ca is the actual concentration, Ta is the actual temperature, Ct is the target concentration, Tt is the target temperature, δc and δt are the standard deviations, Qm is the maximum flow rate, and ln(Qm) is the natural logarithm of the maximum flow rate.

[0034] Furthermore, the control adjustment strategy includes:

[0035] If the concentration is too high but within the normal control range, add more diluent;

[0036] If the temperature is low but within the normal control range, start the heating equipment and trigger equipment maintenance checks;

[0037] If the concentration or temperature deviation is severe and exceeds the normal control range, an early warning will be triggered.

[0038] Furthermore, the data analysis module performs noise filtering, trend analysis, and rate of change calculation on the real-time process parameters to obtain the actual dilution parameters.

[0039] Furthermore, the cloud server optimizes the control algorithm and strategy based on the comparison between the execution results and theoretical parameters, forming a closed-loop control.

[0040] This application also provides a control system that applies the control method described above, the control system comprising:

[0041] The sensor module is configured to collect initial parameter data of the grinding slurry stock solution and real-time process parameter data in the mixing tank during the dilution and mixing process;

[0042] The cloud server is configured to receive and store the parameter data collected by the sensor module, send the initial parameter data to the data analysis module, receive the theoretical dilution parameters and actual dilution parameters sent by the data analysis module, compare the actual dilution parameters with the theoretical dilution parameters, calculate the deviation threshold based on the artificial intelligence algorithm, determine whether it is within the normal control range, trigger an early warning when it exceeds the normal range, and generate a control adjustment strategy based on the deviation threshold.

[0043] The data analysis module is configured to obtain the initial parameter data from the cloud server, extract theoretical process parameters to calculate the theoretical dilution parameters, and receive and process the real-time process parameters uploaded by the sensor module to obtain the actual dilution parameters, and send the theoretical dilution parameters and the actual dilution parameters to the cloud server.

[0044] The dilution and mixing module is communicatively connected to the cloud server and is configured to receive and execute the control and adjustment strategies issued by the cloud server, automatically adjust the dilution and mixing parameters to achieve precise supply of grinding fluid, and feed back the execution results to the cloud server.

[0045] The beneficial effects of this invention are as follows: This application uses artificial intelligence algorithms to deeply analyze the parameters of the grinding slurry stock solution and the data of the mixing process, accurately calculates the theoretical and actual dilution parameters, and realizes quantitative evaluation and automatic adjustment of the mixing state based on dynamically calculated deviation thresholds. This significantly improves the stability of the grinding slurry concentration, reduces processing quality problems caused by concentration fluctuations, increases product qualification rate, and reduces production costs. Secondly, by utilizing real-time monitoring and dynamic feedback mechanisms, the system can quickly respond to changes in working conditions and automatically adjust supply parameters, avoiding production interruptions or product quality declines caused by parameter deviations. This significantly improves the timeliness and effectiveness of supply, reduces equipment wear and the risk of production delays. Finally, through a closed-loop optimization mechanism, the system can continuously accumulate data and learn on its own, adapting to differences in stock solution parameters and complex working conditions in different batches. This significantly reduces the burden on operators, improves the scientific nature and accuracy of control, and provides a reliable guarantee for achieving intelligent and high-precision industrial production.

[0046] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0047] Figure 1 This application presents an embodiment of an automatic dilution, mixing, and supply control method for grinding slurry.

[0048] Figure 2 This is a control system described in one embodiment of this application. Detailed Implementation

[0049] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0050] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0051] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0052] Please refer to Figure 1 An embodiment of this application illustrates an automatic dilution, mixing, and supply control method for grinding slurry, the method comprising:

[0053] S1: The sensor module collects initial parameter data from the polishing slurry stock solution and uploads this data to the cloud server, marking it as basic monitoring data. In this embodiment, it should be specifically noted that the sensor module extracts initial parameter data from the polishing slurry stock solution periodically according to a preset collection frequency. After the initial parameter data is collected, the sensor module automatically performs preliminary format conversion to make it conform to a unified data standard. After preliminary processing, the collected data is uploaded to the cloud server through a secure network channel to ensure security during transmission and prevent data from being illegally intercepted or tampered with. After being uploaded to the cloud server, the data is stored in a dedicated database and classified and marked according to predefined rules, serving as basic monitoring data for subsequent steps.

[0054] S2: The data analysis module extracts the theoretical process parameters of the grinding fluid to be mixed from the basic monitoring data and calculates the theoretical dilution parameters.

[0055] S3: During the slurry dilution and mixing execution phase, the sensor module collects real-time process parameters of the slurry in the mixing tank and transmits them to the data analysis module. Specifically, during this phase, the sensor module collects dynamic parameters of the slurry in the mixing tank, which are crucial for monitoring and adjusting the dilution and mixing process. The real-time data undergoes preliminary processing and encryption before being transmitted to the cloud server via a secure network channel. Upon receiving this data, the cloud server stores it in a database and categorizes and tags it, enabling the data analysis module to access and analyze this real-time data promptly. This process ensures the real-time performance and accuracy of the slurry dilution and mixing supply control method, providing a solid data foundation for achieving precise slurry dilution and mixing supply.

[0056] It should be further explained that through this real-time monitoring and data transmission mechanism, the system can dynamically respond to any changes during the grinding slurry mixing process, thereby achieving more precise and efficient control. This not only improves the production quality of the grinding slurry but also helps optimize the production process and reduce production costs.

[0057] S4: The data analysis module processes the real-time process parameters to obtain the actual dilution parameters. Specifically, the real-time process parameters include dynamic indicators such as real-time temperature and concentration during the grinding slurry mixing process. These parameters are collected in real-time by the sensor module at a preset frequency, serving as real-time feedback data on the grinding slurry's dilution and mixing status. In practice, the data analysis module obtains the latest real-time data of the grinding slurry from the sensor module according to the system's set processing cycle. For example, if the system is set to a 100ms data processing cycle, the data analysis module receives the real-time temperature and concentration data of the grinding slurry every 100ms to ensure dynamic monitoring of the mixing process.

[0058] It's important to clarify that actual dilution parameters refer to the optimal dilution control parameters calculated by an algorithm based on real-time collected process parameters during the grinding slurry mixing stage. The calculation of these parameters must comprehensively consider the impact of real-time concentration deviation, temperature fluctuations on the mixing effect, and the response characteristics of the actuator. Unlike theoretical dilution parameters, actual dilution parameters need to adapt to dynamic changes during the mixing process in real time. For example, when a real-time concentration deviation from the target value is detected, the adjusted diluent injection volume is quickly calculated to achieve dynamic compensation.

[0059] S5: The cloud server compares the actual dilution parameters with the theoretical dilution parameters, calculates the deviation threshold based on artificial intelligence algorithms, determines whether it is within the normal control range, and triggers an early warning when it exceeds the normal range;

[0060] S6: The cloud server generates a control adjustment strategy based on the deviation threshold and evaluates whether the mixing state of the grinding fluid meets the process requirements.

[0061] S7: The dilution and mixing module automatically adjusts the theoretical dilution parameters according to the control and adjustment strategy to achieve precise dilution and mixing of the grinding slurry, and feeds the results back to the cloud server. Specifically, the dilution and mixing module receives the control and adjustment strategy from the cloud server and automatically adjusts the relevant parameters during the dilution and mixing process accordingly. If the cloud server indicates that more diluent needs to be added, the module will precisely control the flow rate and addition time of the diluent to ensure that the concentration of the grinding slurry reaches the target value. Similarly, if temperature adjustment is required, the module will adjust the power of the heating or cooling system to achieve the desired temperature change.

[0062] During adjustments, the dilution and mixing module continuously monitors the actual parameters of the grinding fluid in the mixing tank to ensure that these parameters gradually approach the theoretical dilution parameters according to the control strategy. During the adjustment process, the module may employ a gradual approach strategy, making small adjustments first, observing the effects, and then deciding whether further adjustments are needed to avoid instability caused by over-adjustment.

[0063] Once the adjustments are complete, the dilution and blending module will send the actual results back to the cloud server. This feedback is crucial for the cloud server, as it verifies the effectiveness of the adjustment strategy and provides data support for future optimizations.

[0064] After receiving the execution results, the cloud server compares them with the theoretical dilution parameters to evaluate the accuracy of the adjustment strategy and the conformity of the grinding fluid mixing state. If the results meet expectations, the cloud server will record the successful adjustment strategy for future reference. Otherwise, the cloud server will analyze the causes of the deviation and adjust the algorithm or control strategy to optimize future dilution and mixing processes.

[0065] In one embodiment, the theoretical process parameters include the temperature and concentration of the raw grinding slurry. Specifically, these theoretical process parameters include the temperature and concentration of the raw grinding slurry. These parameters are key indicators in the dilution and mixing process of the grinding slurry. In practice, the data analysis module periodically acquires real-time data of the grinding slurry from the sensor module according to a preset analysis frequency. If the system is set to perform data analysis every 5 minutes, the data analysis module will receive the temperature and concentration data of the grinding slurry from the sensor module every 5 minutes.

[0066] It should be explained that the extracted theoretical process parameters refer to the theoretically optimal dilution ratio calculated by an algorithm based on the original characteristics of the grinding slurry and the expected mixing effect before actual mixing. These parameters include, but are not limited to, the initial concentration, target concentration, and temperature range of the grinding slurry, and they are crucial for determining the theoretical dilution parameters for grinding slurry dilution and mixing.

[0067] It should be further explained that the extraction and calculation of these theoretical dilution parameters are performed in real time, ensuring that the data analysis module receives the latest information on the state of the grinding slurry. In this way, the data analysis module can provide a scientific and reasonable theoretical basis for the automatic dilution and mixing supply control method of the grinding slurry, thereby guiding the dilution and mixing module to perform precise dilution and mixing operations.

[0068] In one embodiment, the theoretical dilution parameters include the final dilution ratio of the grinding slurry and the grinding slurry temperature compensation coefficient. The actual dilution parameters include the real-time corrected final dilution ratio of the grinding slurry and the dynamic temperature compensation coefficient. The calculation of the actual final dilution ratio requires adding a real-time concentration deviation correction term and a temperature fluctuation compensation term to the theoretical dilution ratio. For example, when the real-time concentration is higher than the target concentration, the diluent injection ratio is automatically increased; when the real-time temperature is lower than the target temperature, the stirring power is adjusted through the temperature compensation coefficient to maintain mixing uniformity. This process, through a closed-loop feedback mechanism, enables the actual dilution parameters to respond in real-time to parameter fluctuations during the mixing process, providing precise control basis for the dilution mixing module.

[0069] In one embodiment, the formula for calculating the polishing slurry temperature compensation coefficient is as follows:

[0070] ;

[0071] K is the temperature compensation coefficient, used to address thermodynamic imbalances during the dilution and mixing of the grinding slurry. It quantifies the impact of temperature changes on some physicochemical properties of the grinding slurry, and uses this coefficient to compensate and adjust relevant parameters. In actual control, if the calculated K deviates significantly from 1, it indicates a substantial impact from temperature changes. This necessitates adjusting the power of the heating / cooling equipment, the mixing time, etc., to ensure the final mixed grinding slurry meets process requirements. For example, when large temperature changes cause variations in the viscosity of the grinding slurry, affecting mixing uniformity, the stirring power can be adjusted based on K. a is the Arrhenius coefficient, calculated from the activation energy Ea and the gas constant R of the grinding slurry, using the formula: It reflects the thermodynamic characteristics of the polishing slurry and is related to the "sensitivity" of temperature to the properties of the polishing slurry; Ea is the activation energy, an inherent property of the polishing slurry, representing the minimum energy required for the polishing slurry molecules to undergo a chemical reaction. The default value is 8500 J / mol, which is an empirical value summarized for common polishing slurries. The activation energy of polishing slurries with different compositions will vary; R is the gas constant, a physical constant used in thermodynamic calculations to correlate parameters such as energy, temperature, and amount of substance, and is a fixed value; Tt is the target temperature of the polishing slurry after dilution and mixing required by the production process, which is a theoretical process parameter and is the temperature index that the mixed liquid needs to reach; T0 is the initial temperature of the polishing slurry stock solution, which is collected by the sensor module and uploaded to the cloud as basic monitoring data.

[0072] In one embodiment, the final dilution ratio of the grinding slurry is calculated as follows:

[0073] The formula for calculating the basic dilution ratio of the grinding slurry to be mixed is:

[0074] ;

[0075] Rb is the base dilution ratio, reflecting the basic requirements of concentration difference and mixing efficiency for dilution. It does not consider complex factors such as temperature and safety compensation, and embodies the theoretical degree of dilution determined by the original concentration difference and mixing conditions. C0 is the concentration of the original grinding fluid itself, that is, the initial concentration of the grinding fluid before dilution. It is a core input parameter, representing the "original concentration to be diluted". The difference between it and the target concentration determines the "concentration range" of dilution. The larger the difference, the larger the theoretical dilution ratio tends to be. Cr is the residual concentration of the diluent, referring to the concentration of the residual diluent in the pipeline or equipment. In industry, it is used to prevent cross-contamination because the residual concentration is high. Residual liquid will affect the concentration of the next batch of grinding slurry. This "ineffective dilution" is deducted in advance in the formula, and the typical value is 0.05%. Ct is the target concentration, which is the final concentration required by the process. It is also a core input parameter. The difference between the target concentration and the original concentration determines the direction of dilution. The larger the difference, the larger the theoretical dilution ratio is usually. m is the mixing efficiency coefficient, which reflects the influence of the agitator on the uniformity of mixing. Different agitators have different mixing efficiencies, and their values ​​are between 0.85 and 0.98. It is used to correct the deviation between the theoretical calculation value and the actual mixing effect. For a high-efficiency agitator, this coefficient is closer to 0.98.

[0076] The formula for calculating the final dilution ratio of the grinding slurry to be mixed is:

[0077] ;

[0078] Among them, Rt is the final dilution ratio, which is the final dilution ratio that guides actual production. It integrates factors such as basic concentration requirements, temperature effects, and safety redundancy. It is a precise control parameter that can be directly used in production operations and determines the actual amount of diluent added; Rb is the basic dilution ratio; K is the temperature compensation coefficient; and S is the safety factor, which is used to compensate for redundancy caused by process fluctuations, equipment errors, etc.

[0079] In one embodiment, the calculation of the deviation threshold includes concentration deviation ΔC, temperature deviation ΔT, rate of change deviation ΔR, concentration deviation threshold ΔCm, temperature deviation threshold ΔTm, and rate of change threshold ΔRm;

[0080] The formula for calculating the concentration deviation ΔC is: ΔC is the concentration deviation, which directly reflects the degree to which the actual concentration deviates from the target concentration. It is a fundamental indicator for judging whether the concentration is within a reasonable range. For example, if the target concentration Ct is 10% and the actual concentration Ca is 9%, then... This indicates that the concentration is 10% lower than the target concentration. This deviation can be used to determine whether the dilution process needs to be adjusted. Ca is the actual collected concentration of the grinding fluid, and Ct is the target concentration, which is the final concentration of the grinding fluid required by the process.

[0081] The formula for calculating temperature deviation ΔT is: ΔT is the temperature deviation, calculated as the absolute value of the difference between the actual temperature and the target temperature. Temperature has a significant impact on the physicochemical properties of the grinding fluid. This deviation allows us to quickly determine how far the actual temperature is from the target temperature. Ta is the actual collected temperature of the grinding fluid, i.e., the current temperature inside the mixing tank monitored in real time by the sensor; Tt is the target temperature, i.e., the final temperature of the grinding fluid required by the process.

[0082] The formula for calculating the rate of change deviation ΔR is: ΔR is the rate of change deviation, which mainly reflects whether the speed of concentration change meets expectations. For example, if the actual concentration change rate is -2% / minute (the concentration decreases rapidly) and the target concentration change rate is -1% / minute (the expected decrease is slow), then ΔR = 1% / minute, indicating that the actual concentration change rate deviates from the expectation. It may be necessary to adjust the diluent addition rate, etc., to make the concentration change more in line with the process rhythm. ΔCa is the actual change in concentration within time Δt, and ΔCt is the theoretical change in target concentration within time Δt. Δt is the time interval, i.e., the time period for calculating the concentration change rate.

[0083] The formula for calculating the concentration deviation threshold ΔCm is: ΔCm is the concentration deviation threshold, calculated by adding three times the standard deviation of the concentration deviation to the concentration deviation itself. Statistically, approximately 99.7% of data falls within the range of the mean ± three standard deviations. Setting this concentration threshold ensures that the concentration deviation of the current batch remains within a reasonable fluctuation range based on historical data, allowing for assessment of whether the current concentration deviation is excessive. ΔC represents the concentration deviation, and δc represents the standard deviation of the concentration deviation, reflecting the dispersion of concentration deviations across multiple past production batches and indicating the range of concentration deviation fluctuations.

[0084] The formula for calculating the temperature deviation threshold ΔTm is: ; ΔTm is the temperature deviation threshold, ΔT is the temperature deviation, δt is the empirical standard deviation of the temperature deviation, i.e., the empirical standard deviation calculated from historical temperature deviation data; max(2.0…) is to take the larger value between the calculation result in parentheses and 2.0, in order to set a minimum limit for the temperature threshold and avoid the threshold being too low due to special historical data, which would make it impossible to guarantee production safety.

[0085] The formula for calculating the rate of change threshold ΔRm is: ΔRm is the rate of change threshold, calculated using parameters such as theoretical dilution ratio and maximum flow rate. It reflects the reasonable fluctuation range of concentration change rate. Combining these parameters, the rate of change threshold is calculated to determine whether the current concentration change rate deviation is within a reasonable range. Rt is the theoretical dilution ratio, which is the final dilution ratio calculated in the previous steps. It integrates comprehensive parameters such as basic concentration requirements, temperature compensation, and safety factor. Qm is the maximum allowable flow rate of grinding fluid in the pipeline. ln(Qm) is the natural logarithm of the maximum flow rate. It is a mathematical transformation in the formula used to adapt the relationship between flow rate and rate of change threshold.

[0086] It needs to be explained that these formulas and parameters work together to achieve real-time deviation calculation and dynamic threshold judgment of key parameters such as concentration, temperature, and rate of change during the dilution and mixing of the grinding slurry. Based on these judgment results, it is determined whether to trigger an early warning or adjust the control strategy to ensure that the dilution and mixing process of the grinding slurry meets the process requirements and stably produces qualified grinding slurry.

[0087] In one embodiment, the control adjustment strategy includes:

[0088] If the concentration is too high but within the normal control range, add more diluent;

[0089] If the temperature is low but within the normal control range, start the heating equipment and trigger equipment maintenance checks;

[0090] If the concentration or temperature deviation is severe and exceeds the normal control range, an early warning will be triggered.

[0091] It should be noted that the cloud server compares the real-time monitored deviation threshold with the preset normal control range. If all monitored parameters are within the normal range, the current dilution and mixing operation will continue as before. If the monitored parameters such as concentration, temperature, or rate of change deviate from the normal range, the cloud server will first identify the specific type of deviation.

[0092] If the concentration is too high, the cloud server will instruct the dilution and mixing module to increase the amount of diluent. If the temperature is too low, the cloud server will instruct the heating mechanism to be activated and simultaneously trigger the equipment maintenance program to check the equipment status. In the event of a serious deviation, the server will immediately trigger the early warning system.

[0093] Throughout the process, the cloud server continuously tracks the mixing status of the grinding slurry, ensuring that the dilution and mixing module adjusts in a timely manner until all parameters return to the normal control range. All adjustments and results from the dilution and mixing module are recorded by the cloud server for subsequent optimization and analysis. This mechanism guarantees the accuracy and reliability of the grinding slurry dilution and mixing process.

[0094] In one embodiment, the data analysis module performs noise filtering, trend analysis, and rate of change calculation on real-time process parameters to obtain the actual dilution parameters. The processing of the actual dilution parameters involves noise filtering the raw data collected by the sensors to eliminate outliers caused by factors such as electromagnetic interference, ensuring data reliability; simultaneously, it analyzes the changing trends of real-time concentration and temperature, calculates the rate of parameter change, and assesses the stability of the mixing process; then, based on the deviation between the real-time concentration and the target concentration, it calculates the actual dilution ratio and temperature compensation coefficient at the current moment. The temperature compensation coefficient needs to be dynamically adjusted according to the difference between the real-time temperature and the target temperature to correct for mixing efficiency deviations caused by temperature fluctuations.

[0095] In one embodiment, the cloud server optimizes the control algorithm and strategy based on the comparison between the execution results and theoretical parameters, forming a closed-loop control. Through this closed-loop control and feedback mechanism, the dilution and mixing module can achieve precise dilution and mixing of the grinding slurry, ensuring that the quality and performance of the grinding slurry meet production requirements, while improving the automation and intelligence level of the production process.

[0096] Please refer to Figure 2 This application also provides a control system that applies the control method described above, the control system comprising:

[0097] Sensor module 10 is configured to collect initial parameter data of the grinding slurry stock solution and real-time process parameter data in the mixing tank during the dilution and mixing process;

[0098] The cloud server 30 is configured to receive and store the parameter data collected by the sensor module 10, send the initial parameter data to the data analysis module 20, receive the theoretical dilution parameters and actual dilution parameters sent by the data analysis module 20, compare the actual dilution parameters with the theoretical dilution parameters, calculate the deviation threshold based on the artificial intelligence algorithm, determine whether it is within the normal control range, trigger an early warning when it exceeds the normal range, and generate a control adjustment strategy based on the deviation threshold.

[0099] The data analysis module 20 is configured to obtain initial parameter data from the cloud server 30, extract theoretical process parameters to calculate theoretical dilution parameters, and also to receive real-time process parameters uploaded by the sensor module 10, process them to obtain actual dilution parameters, and send the theoretical dilution parameters and actual dilution parameters to the cloud server 30.

[0100] The dilution and mixing module 40 is connected to the cloud server 30 and is configured to receive and execute the control and adjustment strategies issued by the cloud server 30, automatically adjust the dilution and mixing parameters to achieve precise supply of grinding fluid, and feed back the execution results to the cloud server 30.

[0101] This application utilizes artificial intelligence algorithms to deeply analyze the parameters of the grinding slurry stock solution and the data from the mixing process. It accurately calculates theoretical and actual dilution parameters and, based on dynamically calculated deviation thresholds, achieves quantitative evaluation and automatic adjustment of the mixing state. This significantly improves the stability of the grinding slurry concentration, reduces processing quality issues caused by concentration fluctuations, increases product qualification rates, and lowers production costs. Secondly, by employing real-time monitoring and dynamic feedback mechanisms, the system can quickly respond to changes in operating conditions and automatically adjust supply parameters, avoiding production interruptions or product quality degradation due to parameter deviations. This significantly improves the timeliness and effectiveness of supply, reduces equipment wear and the risk of production delays. Finally, through a closed-loop optimization mechanism, the system can continuously accumulate data and learn from itself, adapting to differences in stock solution parameters and complex operating conditions across different batches. This significantly reduces the burden on operators, improves the scientific nature and accuracy of control, and provides a reliable guarantee for achieving intelligent and high-precision industrial production.

[0102] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for automatically diluting, mixing, and supplying grinding slurry, characterized in that, The control method includes: S1: The sensor module collects the initial parameter data of the grinding slurry stock solution and uploads this data to the cloud server, marking it as basic monitoring data; S2: The data analysis module extracts the theoretical process parameters of the grinding slurry to be mixed from the basic monitoring data and calculates the theoretical dilution parameters. The theoretical process parameters include the original temperature and concentration of the grinding slurry. The theoretical dilution parameters include the final dilution ratio and the grinding slurry temperature compensation coefficient. The calculation formula for the grinding slurry temperature compensation coefficient is as follows: ； Where K is the temperature compensation coefficient, and a is the Arrhenius coefficient, which is calculated using the following formula: Ea is the activation energy, R is the gas constant, Tt is the target temperature, and T0 is the initial temperature of the original grinding slurry. The calculation process for the final dilution ratio of the grinding slurry is as follows: The formula for calculating the basic dilution ratio of the grinding slurry to be mixed is: ； Rb is the base dilution ratio, C0 is the concentration of the original grinding slurry, Cr is the residual concentration of the diluent, Ct is the target concentration, and m is the mixing efficiency coefficient. The formula for calculating the final dilution ratio of the grinding slurry to be mixed is: ； Where Rt is the final dilution ratio, Rb is the basic dilution ratio, K is the temperature compensation coefficient, and S is the safety factor; S3: The sensor module collects real-time process parameters of the grinding fluid in the mixing tank during the grinding fluid dilution and mixing stage, and transmits them to the data analysis module. S4: The data analysis module processes real-time process parameters to obtain actual dilution parameters; S5: The cloud server compares the actual dilution parameters with the theoretical dilution parameters, calculates the deviation threshold based on artificial intelligence algorithms, determines whether it is within the normal control range, and triggers an early warning when it exceeds the normal range; S6: The cloud server generates a control adjustment strategy based on the deviation threshold and evaluates whether the mixing state of the grinding fluid meets the process requirements; S7: The dilution and mixing module automatically adjusts the relevant parameters in the dilution and mixing process according to the control and adjustment strategy, so as to achieve precise dilution and mixing supply of grinding slurry, and feeds back the execution results to the cloud server.

2. The automatic dilution, mixing, and supply control method for grinding slurry as described in claim 1, characterized in that, The calculation of the deviation threshold includes concentration deviation. Temperature deviation Concentration change rate deviation Concentration deviation threshold Temperature deviation threshold and concentration change rate threshold ; Concentration deviation The calculation formula is: ; Temperature deviation The calculation formula is: ; Concentration change rate deviation The calculation formula is: ; Concentration deviation threshold The calculation formula is: ; Temperature deviation threshold The calculation formula is: ; Concentration change rate threshold The calculation formula is: ; Where Ca is the actual concentration, Ta is the actual temperature, Ct is the target concentration, and Tt is the target temperature. , Here, σconcentration and σtemperature are the standard deviations, respectively; Qm is the maximum flow rate; and ln(Qm) is the natural logarithm of the maximum flow rate. ΔCt is the actual change in concentration over time Δt, while ΔCt is the theoretical change in target concentration over time Δt. Δt is the time interval, i.e., the time period for calculating the concentration change rate.

3. The automatic dilution, mixing, and supply control method for grinding slurry as described in claim 2, characterized in that, The control adjustment strategy includes: If the concentration is too high but within the normal control range, add more diluent; If the temperature is low but within the normal control range, start the heating equipment and trigger equipment maintenance checks; If the concentration or temperature deviation is severe and exceeds the normal control range, an early warning will be triggered.

4. The automatic dilution, mixing, and supply control method for grinding slurry as described in claim 1, characterized in that, The data analysis module performs noise filtering, trend analysis, and rate of change calculation on real-time process parameters to obtain the actual dilution parameters.

5. The automatic dilution, mixing, and supply control method for grinding slurry as described in claim 1, characterized in that, The cloud server optimizes the control algorithm and strategy based on the comparison between the execution results and theoretical parameters, forming a closed-loop control.

6. A control system applying the control method as described in any one of claims 1 to 5, characterized in that, The control system includes: The sensor module is configured to collect initial parameter data of the grinding slurry stock solution and real-time process parameter data in the mixing tank during the dilution and mixing process; The cloud server is configured to receive and store the parameter data collected by the sensor module, send the initial parameter data to the data analysis module, receive the theoretical dilution parameters and actual dilution parameters sent by the data analysis module, compare the actual dilution parameters with the theoretical dilution parameters, calculate the deviation threshold based on the artificial intelligence algorithm, determine whether it is within the normal control range, trigger an early warning when it exceeds the normal range, and generate a control adjustment strategy based on the deviation threshold. The data analysis module is configured to obtain the initial parameter data from the cloud server, extract theoretical process parameters to calculate the theoretical dilution parameters, and receive and process the real-time process parameters uploaded by the sensor module to obtain the actual dilution parameters, and send the theoretical dilution parameters and the actual dilution parameters to the cloud server. The dilution and mixing module is communicatively connected to the cloud server and is configured to receive and execute the control and adjustment strategies issued by the cloud server, automatically adjust the dilution and mixing parameters to achieve precise supply of grinding fluid, and feed back the execution results to the cloud server.

Images