Method and system for monitoring and automatically adjusting cyclohexanone concentration in real time

Abstract

The invention relates to a cyclohexanone concentration real-time monitoring and automatic adjusting method and system in the technical field of semiconductors, and the method comprises the steps: comparing a smoothed concentration value with a preset threshold value, if the concentration value exceeds a safety range, generating an abnormal signal, recording a timestamp, and determining a condition for triggering subsequent operation through the abnormal signal; after the abnormal signal is obtained, an instruction is sent to the control module, the water supplementing and liquid discharging device is activated according to instruction content, whether operation is successfully executed or not is judged from device state feedback, and execution result data is obtained; analyzing response time and flow change of the water replenishing and liquid discharging device through execution result data, updating control parameters by adopting a dynamic adjustment algorithm, and determining an accurate instruction of next operation according to the updated parameters; and extracting continuous concentration change trend data from the concentration monitoring module, judging whether the system is recovered to a safe range or not according to the change trend, and obtaining a current stability evaluation value.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, specifically to semiconductor cleaning technology, and particularly to a method and system for real-time monitoring and automatic adjustment of cyclohexanone concentration. Background Art

[0002] As a core link in modern microelectronics manufacturing, semiconductor cleaning technology directly affects device performance and reliability, and its importance is self-evident. In this field, the selection and management of chemical solutions are crucial. Especially, cyclohexanone, as an environmentally friendly solvent, is widely used in the cleaning process due to its good biodegradability, low volatility, and high safety. However, the potential safety hazards during the use of cyclohexanone cannot be ignored. Its leakage may cause spontaneous combustion or pose a threat to human health, which makes the real-time monitoring and effective management of its concentration a key to technological progress.

[0003] Currently, the detection of cyclohexanone concentration in semiconductor cleaning mostly relies on manual measurement or regular inspection. This method has problems such as response lag, insufficient accuracy, and high operation risks. Especially in a high-precision production environment, manual intervention is not only inefficient but also difficult to cope with sudden leakage situations, resulting in an increased risk of potential safety accidents. These limitations indicate that the traditional passive management method can no longer meet the requirements of modern semiconductor manufacturing for safety and automation.

[0004] Despite many advantages of cyclohexanone, significant technical challenges still exist during its use. First, the technology for real-time concentration detection needs to have high sensitivity and fast response capabilities to capture the minute changes at the initial stage of leakage. Second, the detection system must be seamlessly integrated with the water replenishment and liquid drainage devices to achieve active regulation when the concentration is abnormal. Finally, how to ensure the stable operation of the system in a complex process environment and avoid false alarms or failures is another difficult problem to be solved urgently. These unsolved technical factors directly lead to the difficulty of eradicating the leakage risk, and further give rise to the unique technical problem of how to achieve precise monitoring and timely response in a dynamic environment.

[0005] Therefore, how to design a system that can detect the cyclohexanone concentration in real time and automatically trigger the water replenishment and liquid drainage operations when the concentration exceeds the standard to ensure the safety and stability of the cleaning process has become the key problem that needs to be overcome urgently in this research. The solution to this problem will provide a new safety guarantee path for semiconductor cleaning technology. Summary of the Invention

[0006] The present invention provides a method for real-time monitoring and automatic adjustment of cyclohexanone concentration, including the following steps:

[0007] Collect cyclohexanone concentration data through a sensor array and transmit it to a processing unit to obtain real-time change signals in the environment, and perform filtering processing on the collected original data to obtain a smoothed concentration value;

[0008] Compare the smoothed concentration value with a preset threshold. If the concentration value exceeds the safe range, generate an abnormal signal and record the timestamp, and determine the conditions for triggering subsequent operations based on the abnormal signal;

[0009] After obtaining the abnormal signal, send an instruction to the control module, activate the water replenishment and drainage device according to the instruction content, and judge whether the operation is successfully executed from the device status feedback to obtain the execution result data;

[0010] Analyze the response time and flow rate change of the water replenishment and drainage device through the execution result data, update the control parameters using a dynamic adjustment algorithm, and determine the precise instruction for the next operation based on the updated parameters;

[0011] Extract continuous concentration change trend data from the concentration monitoring module, and judge whether the system has returned to the safe range based on the change trend to obtain the current stability evaluation value;

[0012] Compare the stability evaluation value with a preset stability threshold. If the evaluation value is lower than the threshold, optimize the sensor data processing flow through an adaptive filtering algorithm to obtain the optimized concentration monitoring result;

[0013] After obtaining the optimized concentration monitoring result, transmit it to the central processing unit, generate a real-time regulation strategy for the result data, and judge the operating frequency and duration of the water replenishment and drainage device through the regulation strategy to obtain the adjusted operating parameters;

[0014] Drive the water replenishment and drainage device to execute operations through the adjusted operating parameters, extract the actual execution effect data from the device operation log, and judge the stable state of the system in the dynamic environment based on the effect data to obtain the final operation optimization plan;

[0015] Update the control logic of the entire system according to the final operation optimization plan, use a predictive analysis algorithm to estimate future concentration changes, and judge the probability of potential abnormalities based on the estimation result to obtain the regulation basis for long-term operation.

[0016] The present invention provides a cyclohexanone concentration real-time monitoring and automatic regulation system, which mainly includes:

[0017] A data acquisition and transmission module for collecting cyclohexanone concentration data through a sensor array and transmitting it to the processing unit to obtain real-time change signals in the environment;

[0018] A filtering processing module for filtering the collected original data to obtain a smoothed concentration value;

[0019] Anomaly detection and recording module, which is used to compare the smoothed concentration value with a preset threshold. If the concentration value exceeds the safe range, an anomaly signal is generated and the timestamp is recorded, and the conditions for triggering subsequent operations are determined through the anomaly signal;

[0020] Control instruction generation module, which is used to send an instruction to the control module after obtaining the anomaly signal, activate the water replenishment and drainage device according to the instruction content, and judge whether the operation is successfully executed from the device status feedback to obtain the execution result data;

[0021] Dynamic parameter adjustment module, which is used to analyze the response time and flow rate change of the water replenishment and drainage device through the execution result data, update the control parameters using a dynamic adjustment algorithm, and determine the precise instruction for the next operation according to the updated parameters;

[0022] Stability evaluation module, which is used to extract continuous concentration change trend data from the concentration monitoring module, judge whether the system has returned to the safe range according to the change trend, and obtain the current stability evaluation value;

[0023] Data processing optimization module, which is used to compare the stability evaluation value with a preset stability threshold. If the evaluation value is lower than the threshold, the sensor data processing process is optimized through an adaptive filtering algorithm to obtain the optimized concentration monitoring result;

[0024] Real-time regulation strategy module, which is used to transmit the optimized concentration monitoring result to the central processing unit after obtaining it, generate a real-time regulation strategy for the result data, judge the operation frequency and duration of the water replenishment and drainage device through the regulation strategy, and obtain the adjusted operation parameters;

[0025] Operation optimization and prediction module, which is used to drive the water replenishment and drainage device to execute operations through the adjusted operation parameters, extract the actual execution effect data from the operation log of the device, judge the stable state of the system in the dynamic environment according to the effect data, and obtain the final operation optimization plan; update the control logic of the entire system according to the final operation optimization plan, use a predictive analysis algorithm to estimate the future concentration change, and judge the probability of potential anomalies occurring according to the estimation result to obtain the regulation basis for long-term operation.

[0026] The technical solution provided by the embodiment of the present invention may include the following beneficial effects:

[0027] The present invention discloses a method and system for real-time monitoring and automatic adjustment of cyclohexanone concentration. The system collects cyclohexanone concentration data in the environment through a sensor array, compares it with a preset threshold after filtering. When the concentration exceeds the safe range, the system generates an abnormal signal and triggers a water replenishment and liquid drainage device. The present invention dynamically adjusts control parameters according to the execution results and optimizes the data processing flow using an adaptive filtering algorithm. By analyzing the concentration change trend, the system evaluates stability and generates a real-time regulation strategy to drive the water replenishment and liquid drainage device to perform corresponding operations. The present invention also uses a predictive analysis algorithm to estimate future concentration changes and provides a regulation basis for long-term operation. The system realizes intelligent monitoring and precise adjustment of cyclohexanone concentration, effectively ensuring the safety and stability of the production environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of a method for real-time monitoring and automatic adjustment of cyclohexanone concentration according to the present invention.

[0029] Figure 2 It is a schematic diagram of a method and system for real-time monitoring and automatic adjustment of cyclohexanone concentration according to the present invention.

[0030] Figure 3 It is another schematic diagram of a method and system for real-time monitoring and automatic adjustment of cyclohexanone concentration according to the present invention.

[0031] Figure 4 It is a structural schematic diagram of a method and system for real-time monitoring and automatic adjustment of cyclohexanone concentration according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] To further understand the content of the present invention, the present invention will be described in detail in combination with the drawings and embodiments. The following further describes the present application in detail with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention and are not intended to limit the invention. In addition, it should be noted that only parts related to the invention are shown in the drawings for the convenience of description.

[0033] As Figures 1-4 , a method for real-time monitoring and automatic adjustment of cyclohexanone concentration in this embodiment specifically includes:

[0034] Step S101, collect cyclohexanone concentration data through a sensor array and transmit it to a processing unit, obtain real-time change signals in the environment, and perform filtering processing on the collected original data to obtain a smoothed concentration value.

[0035] Obtaining raw concentration data in an environment; processing the raw concentration data with a mean filtering algorithm to obtain a preliminary concentration value; if the preliminary concentration value exceeds a preset threshold, performing secondary filtering on the preliminary concentration value to obtain adjusted concentration data; obtaining a real-time changing trend signal based on the adjusted concentration data; comparing the trend signal with a pre-established reference signal to determine an abnormal state of concentration change; determining the dynamic characteristics of the environmental signal based on the abnormal state; obtaining a final output value of the smoothed concentration based on the dynamic characteristics; wherein the raw concentration data is acquired by collecting the raw concentration data through a sensor array and transmitting it to a processing unit; the mean filtering algorithm, the secondary filtering processing, the preset threshold, and the reference signal are all pre-set; the trend signal represents a real-time changing trend of the concentration data; the abnormal state includes a sudden change in concentration, a continuous increase or decrease in concentration; the dynamic characteristics include a concentration change rate and a change amplitude.

[0036] Exemplary, when collecting cyclohexanone concentration data by sensor array, the sensor array is composed of 16 high-precision electrochemical sensors, and the sampling frequency of each sensor is 10 Hz, which can monitor the cyclohexanone concentration change in the environment in real time. The collected raw data is sent to the processing unit at a rate of 100 data points per second by the wireless transmission module. After the processing unit receives the data, it first performs data preprocessing to remove abnormal values, such as removing data points exceeding the sensor range (0-1000ppm). Then, the Kalman filter algorithm is used to smooth the data, and the state transfer matrix of the Kalman filter is set to the unit matrix, the process noise covariance matrix is 0.01, and the observation noise covariance matrix is 0.1. By iterative calculation, the smoothed concentration value is obtained. The filtered data is further secondary smoothed by a moving average algorithm, and the window size is 5 data points, to reduce the influence of random noise. Finally, the processing unit stores the smoothed concentration value in the database and displays the changing trend of cyclohexanone concentration in real time through a visual interface. For example, the concentration change over the past 10 minutes is displayed in the form of a line graph in the interface. At the same time, an alarm threshold is set. When the concentration exceeds 50ppm, the alarm mechanism is triggered to remind relevant personnel to take corresponding measures.

[0037] Step S102, comparing the smoothed concentration value with a preset threshold value, if the concentration value exceeds a safe range, generating an abnormal signal and recording a timestamp, and determining the conditions for triggering subsequent operations through the abnormal signal.

[0038] Acquire the collected concentration value data, process the concentration value data using the moving average method, and obtain smoothed concentration data. Compare the smoothed concentration data with the preset threshold value, and if it exceeds the safety range, generate an abnormal signal. For the abnormal signal, use a timestamp recording tool to record the current time to obtain signal data with a time stamp. Use a decision tree algorithm to judge the trigger conditions through the signal data with a time stamp to determine whether the subsequent operation requirements are met. Acquire signal data that meets the conditions, match subsequent operations through a preset rule table, and obtain operation instructions. According to the operation instructions, use a log recording tool to store the execution process and obtain an operation execution record. Use a data verification method to judge data consistency through the operation execution record to determine the final processing result.

[0039] For example, in the gas concentration monitoring system, the sliding average algorithm is used to smooth the original sampled data, the window size is set to 5 data points, and the weight coefficient is

[0040] [0.1, 0.2, 0.4, 0.2, 0.1]. For example, if the current sampling sequence is [45, 47, 52, 49, 48] ppm, the smoothed value obtained by weighted calculation is 45×0.1+47×0.2+52×0.4+49×0.2+48×0.1=49.3ppm. The system presets the CO2 safety threshold to 30-50ppm. When the smoothed value is 49.3ppm, it is close to the upper limit but no alarm is triggered. If the subsequent smoothing value exceeds the threshold for three consecutive times (such as 51.2ppm, 53.6ppm, and 54.1ppm), the abnormal judgment logic is activated, and the 3σ principle in time series analysis is used to calculate the standard deviation σ=2.8ppm and the mean μ=48.5ppm of the data in the last hour. The current value of 54.1ppm exceeds the μ+3σ control limit. The system automatically generates an abnormal record containing the exceeded value 54.1ppm and the timestamp 2023-11-15T14:23:41.352 accurate to milliseconds. The abnormal signal triggers the linkage control module, and through the preset IF-THEN rules (such as IF concentration>50ppm for 3 minutes THEN start the exhaust equipment), the device control API is called to send a speed command of 2000rpm, and the abnormal data packet is pushed to the cloud monitoring platform through the MQTT protocol. The data packet structure contains

[0041] {timestamp:ISO8601,value:54.1,threshold:50,location:“A-12"}, etc. The system continuously monitors the trend of the smoothed value. When it detects that the 10-minute moving average falls below 48ppm, the alarm state is automatically lifted and the recovery timestamp is recorded.

[0042] In step S103, after obtaining an abnormal signal, send an instruction to the control module, activate the water replenishment and drainage device according to the instruction content, and judge whether the operation is successfully executed from the device status feedback to obtain the execution result data.

[0043] Obtain the sensor data stream, where the sensor data stream includes a plurality of continuously collected signal data; process the sensor data stream through an anomaly detection algorithm to obtain an abnormal signal pattern; according to the abnormal signal pattern, obtain the corresponding control instruction from a preset instruction library, where the preset instruction library includes the mapping relationship between a preset number of types of abnormal signal patterns and control instructions; send the control instruction to the control module of the replenishment and drainage device, where the control instruction is used to drive the execution unit of the replenishment and drainage device; collect the real-time status data of each execution unit of the replenishment and drainage device, where the real-time status data reflects the current operating status of the execution unit; compare the real-time status data with the expected status data through a status matching algorithm to obtain a status consistency result; if the status consistency result indicates that the current status is consistent with the expected status, judge that the instruction is successfully executed and record the execution result of the control instruction; if the status consistency result indicates that the current status is inconsistent with the expected status, regenerate a correction instruction according to the real-time status data; update the control instruction with the correction instruction and send the updated control instruction to the control module of the replenishment and drainage device; repeatedly collect the real-time status data and perform comparison through the status matching algorithm to obtain a new status consistency result until it is judged that the instruction is successfully executed.

[0044] Exemplarily, when the sensor detects that the liquid level in the storage tank is lower than the threshold of 30%, the system calculates the water replenishment demand through the PID control algorithm, where the proportional coefficient Kp is set to 0.8, the integral time Ti is 120 seconds, and the derivative time Td is 15 seconds. The output command is adjusted in real time according to the deviation e(t) = set value - current value. The control module sends the hexadecimal command code 0x01 0x06 0x00 0x0A 0x27 0x10 to the actuator according to the ModbusRTU protocol, where 0x00 0x0A represents the address of the water replenishment valve, and 0x27 0x10 corresponds to the opening degree of 10000 (decimal). After the actuator responds, the flowmeter collects the instantaneous flow at a frequency of 1 Hz. When the cumulative flow reaches the calculated value Q = π×(pipe diameter 0.1m)²×flow velocity 2m / s×time t, the PLC compares the preset value 0xA001 with the feedback value through the CRC-16 check algorithm. If the correct response frame is not received within 5 seconds, the system automatically resends the command and triggers an overtime alarm, recording the event code E-205. When the liquid discharge device starts synchronously, the pressure sensor feeds back the pipeline pressure with a 0-10V analog signal. The AD converter quantifies the voltage value into the pressure value P = (sampling value 4095 / reference voltage 5V)×range 1MPa with a 12-bit resolution. When P is continuously lower than 0.2MPa for 10 seconds, it is determined that the emptying is completed, and the status register 0x4002 bit is set to 1. All process data is uploaded to the cloud platform in JSON format through the MQTT protocol, including the timestamp, device ID, operation type, and 32-bit check code. After verifying the data integrity with the SHA-256 algorithm on the server side, it is stored in the time series database.

[0045] Step S104, analyze the response time and flow rate change of the water replenishment and drainage device through the execution result data, update the control parameters using the dynamic adjustment algorithm, and determine the precise instruction for the next operation based on the updated parameters.

[0046] Obtain the execution result data of the water replenishment and drainage device, where the execution result data includes response time and flow rate change information; calculate the characteristic values of the response time and flow rate change based on the execution result data to obtain a preliminary analysis result; process the preliminary analysis result using the dynamic adjustment algorithm, update the control parameters, and determine the adjusted parameter set; if the adjusted parameter set exceeds the preset threshold, correct the adjusted parameter set through secondary analysis to obtain a stable parameter value; generate a precise instruction for the next operation based on the stable parameter value, and determine whether the precise instruction meets the device operation conditions; verify the precise instruction through simulation operation to obtain the response time and flow rate change trend after operation; adjust the parameter weights of the dynamic adjustment algorithm according to the flow rate change trend to determine an optimized control strategy; recalculate the execution result data using the optimized control strategy to obtain the final operation instruction.

[0047] Exemplarily, by monitoring the response time and flow rate changes of the water replenishment and drainage device in real time, a set of data is collected. Among them, the response time fluctuates between 0.5 seconds and 2.3 seconds, and the flow rate change range is from 10 liters per minute to 50 liters per minute. Using the dynamic adjustment algorithm, first preprocess the data, remove outliers and smooth it, and then use the least squares method to fit the relationship curve between the response time and the flow rate, and obtain the fitting equation as y = 0.02x + 0.8, where y is the response time and x is the flow rate. Based on this model, use the PID control algorithm to dynamically adjust the control parameters, set the proportional coefficient Kp to 0.5, the integral time Ti to 0.2 seconds, and the differential time Td to 0.1 seconds. Through simulation verification, the adjusted control parameters make the response time stable at about 1.2 seconds, and the flow rate fluctuation is controlled within the range of ±2 liters. According to the updated parameters, the system generates an accurate instruction for the next operation, sets the target flow rate to 30 liters per minute, and calculates the corresponding control signal to be 15.6 mA to ensure that the device reaches a stable state in the shortest time. At the same time, the system continuously monitors the deviation between the actual flow rate and the target flow rate. If the deviation exceeds ±1 liter, the parameter fine-tuning mechanism is automatically triggered to further optimize the control effect.

[0048] Step S105, extract continuous concentration change trend data from the concentration monitoring module, and judge whether the system has returned to the safe range according to the change trend to obtain the current stability evaluation value.

[0049] Obtain continuous concentration change data from the monitoring module and use time series analysis to obtain trend data. For the trend data, calculate the change rate through a sliding window to judge the change trend. If the change rate is lower than a preset threshold, determine the system recovery state through mean calculation. Compare the system recovery state with the safe range to obtain a range determination result. Through the range determination result, use the weighted average method to calculate the stability value to obtain the current evaluation data. Extract the stability characteristics from the current evaluation data to judge whether the system is in a stable state. If the stability characteristics meet the preset conditions, obtain the final confirmation result by recording the log.

[0050] Exemplarily, when extracting continuous concentration change trend data from the concentration monitoring module, sensor data can be obtained at a frequency of once per second through a time series acquisition algorithm. For example, 600 data points are continuously collected for 10 minutes, and the data range is between 0 - 100 ppm. The sliding window average method is used to smooth the original data. The window size is 30 seconds, and the standard deviation of the data within each window is calculated. If the standard deviation is less than 2.5, it is determined as a stable segment. For trend judgment, the linear regression algorithm is used to calculate the slope of the data in the most recent 3 minutes. When the absolute value of the slope is less than 0.1 ppm / second and the current concentration value is within the safe range of 50 - 60 ppm, the system recovery determination is triggered. The stability evaluation value is calculated using a weighted method, and the degree of deviation of the current concentration from the safety median value of 55 ppm (absolute value of the difference) and the absolute value of the trend slope are combined in a 7:3 ratio. For example, when the current concentration is 58 ppm and the slope is -0.05, the evaluation value is (3×0.7)+(0.05×0.3×100) = 2.25. When the evaluation value is continuously lower than 3.0 for 5 times, a system stability signal is generated. During the process, historical evaluation values are synchronously recorded to form a circular buffer, and the stability persistence is confirmed by comparing the coefficient of variation of the most recent 10 evaluation values (required to be less than 15%). When a concentration mutation exceeding 10 ppm / second is detected, the abnormal interruption mechanism is immediately triggered, the current evaluation process is paused, and the emergency protocol is started.

[0051] Step S106, compare the stability evaluation value with a preset stability threshold. If the evaluation value is lower than the threshold, optimize the sensor data processing flow through an adaptive filtering algorithm to obtain an optimized concentration monitoring result.

[0052] Obtain sensor data, calculate the stability evaluation value of the sensor data; compare the stability evaluation value with a preset threshold to determine whether the sensor data meets the stability standard; if the stability evaluation value is lower than the preset threshold, process the sensor data using an adaptive filtering algorithm to obtain preliminary optimized data; execute a data processing flow through the preliminary optimized data to obtain smoothed concentration-related data; determine whether there is abnormal fluctuation based on the smoothed concentration-related data to judge the data quality; correct the abnormal fluctuation data using a mean calculation method to obtain stable concentration data; generate a concentration monitoring result through the stable concentration data to obtain the final output data; compare the final output data with historical data to judge the concentration change trend.

[0053] Exemplarily, during the sensor data processing, first monitor the operating state of the system by calculating the stability evaluation value.

[0054] For example, the sliding window method is used to perform real-time analysis on sensor data. The window size is 100 data points, and the standard deviation of the data within the window is calculated as the stability evaluation value. Suppose the preset stability threshold is 0.5. If the calculated evaluation value is 0.6, it indicates that the system stability is lower than the threshold and optimization processing is required. At this time, an adaptive filtering algorithm is used to optimize the sensor data. Specifically, the Kalman filtering algorithm is used, with a state transition matrix of [1,0; 0,1], an observation matrix of [1,0], a process noise covariance of 0.1, and an observation noise covariance of 0.2. After being processed by the filtering algorithm, the noise of the sensor data is significantly reduced, and the optimized concentration monitoring results are more stable.

[0055] For example, the concentration at a certain moment in the original data is 50.3, and after filtering, it is optimized to 50.1. The error is reduced from 0.3 to 0.1, significantly improving the monitoring accuracy. This process is achieved through an automated algorithm without manual intervention, ensuring the efficiency and accuracy of data processing.

[0056] In step S107, after obtaining the optimized concentration monitoring result, it is transmitted to the central processing unit, and a real-time control strategy is generated for the result data. The operating frequency and duration of the water supply and drainage device are judged through the control strategy to obtain the adjusted operating parameters.

[0057] Obtain the concentration monitoring data. The concentration monitoring data is processed by a preset filtering algorithm to obtain the optimized monitoring result. The optimized monitoring result is sent to the central processing unit through a data transmission channel, and the transmission completion status is determined. In the central processing unit, a machine learning algorithm is used to analyze the optimized monitoring result to obtain a real-time control strategy. The operating requirements of the water supply device and the drainage device are judged according to the real-time control strategy to obtain the preliminary instructions for the device operation. The operating frequency and operating duration are calculated through the preliminary instructions to determine the adjusted operating parameters. The device operation status is updated according to the adjusted operating parameters, and new concentration monitoring data is obtained. If the new concentration monitoring data exceeds the preset threshold, the real-time control strategy is optimized through an iterative algorithm to judge the final operating parameters.

[0058] Exemplarily, during the concentration monitoring process, the sensor collects solution concentration data at a frequency of once per second, and performs noise reduction processing on the original data through the Kalman filtering algorithm. For example, when the fluctuations of 5 consecutive sampling values exceed ±0.5%, anomaly detection is triggered. The optimized concentration data is transmitted to the central processing unit via industrial Ethernet using the Modbus-TCP protocol, and the transmission delay is controlled within 50 ms. The central processing unit uses the PID control algorithm to generate a regulation strategy. When the concentration deviates from the set value by 0.8%, the algorithm calculates the opening degree of the water replenishment valve based on the cumulative value of the deviation integral term (such as the integral time constant is set to 120 seconds). For example, when the deviation is +1.2%, an opening degree command of 45% is output. The regulation strategy engine also dynamically adjusts in combination with historical operation data (such as the average drainage frequency in the past 1 hour). If the detected concentration change rate exceeds 0.1% / second, the emergency drainage mode is activated. The water replenishment and drainage device is controlled by receiving 4-20 mA analog signals, and the actuator precisely adjusts the operation duration according to the regulation command. For example, when 3.5 L of solution needs to be replenished, the opening duration of 105 seconds is calculated based on a flow rate of 2 L / min. All operation parameters are written into the time series database in real time, including 23 process indicators such as valve opening degree (recording accuracy 0.1%) and cumulative flow (resolution 0.01 L), providing data support for subsequent analysis.

[0059] Step S108, drive the water replenishment and drainage device to perform operations with the adjusted operation parameters, extract the actual execution effect data from the operation log of the device, and judge the stable state of the system in the dynamic environment based on the effect data to obtain the final operation optimization plan.

[0060] Obtain the operation log data of the water replenishment and drainage device. Extract the actual execution effect data from the operation log data. Process the execution effect data to obtain the system operation characteristic values. Use the system operation characteristic values to analyze the change trend of the dynamic environment and judge the system response state. If the system response state exceeds the preset threshold, adjust the operation parameters through the regression algorithm to obtain the optimized parameter set. Use the optimized parameter set to drive the water replenishment and drainage device to perform operations and obtain new effect data. Compare the new effect data with the historical effect data to judge the system stable state. Adjust the optimization plan according to the system stable state to determine the final operation strategy. Use the final operation strategy to drive the water replenishment and drainage device to perform operations.

[0061] Exemplarily, when adjusting the operating parameters of the water replenishment and drainage device, first, the set value of the water replenishment flow rate is dynamically adjusted from the initial 10 L / min to 15 L / min through the PID control algorithm. The proportional coefficient Kp is set to 0.8, the integral time Ti is set to 120 seconds, and the derivative time Td is set to 30 seconds to respond to the real-time data fluctuations of the liquid level sensor. The operation log record shows that after adjustment, the liquid level is stable within the range of ±2 cm of the set value, and the fluctuation amplitude is significantly reduced compared with ±5 cm before adjustment. The variance of the liquid level fluctuation is calculated using the sliding window algorithm for the last 100 log data, and the liquid level fluctuation variance is 0.04 cm 2 , which is lower than the threshold of 0.1 cm 2 , indicating that the system reaches a stable state. Further, the data of the flow meter is denoised through the Kalman filter algorithm, and the standard deviation of the filtered flow data is 0.3 L / min, meeting the upper limit of 0.5 L / min required by the process. Based on these analysis results, the optimization plan adjusts the PID parameters to Kp = 1.0, Ti = 90 seconds, and Td = 20 seconds, and sets a water replenishment flow compensation value of 15.2 L / min to cope with the pipeline pressure loss. In the simulation environment test of the system, the liquid level control accuracy is improved to ±1.5 cm, and the energy consumption is reduced by 8%.

[0062] Step S109, update the control logic of the entire system according to the final operation optimization plan, use the predictive analysis algorithm to estimate the future concentration change, judge the probability of potential abnormalities based on the estimation result, and obtain the regulation basis for long-term operation.

[0063] Process the concentration change data through the predictive analysis algorithm to obtain the future trend estimation result. Analyze the abnormal probability distribution for the future trend estimation result to determine the abnormal probability distribution characteristics. If the abnormal probability distribution characteristics exceed the preset threshold, adjust the control logic through the algorithm model to obtain the updated system parameters. Run the long-term regulation using the updated system parameters to obtain the regulation basis data. Analyze the potential risks for the regulation basis data to determine the risk distribution characteristics. Optimize the predictive analysis algorithm through the risk distribution characteristics to obtain the improved concentration change estimation result. Update the judgment basis according to the improved concentration change estimation result to obtain the stable regulation plan for long-term operation.

[0064] Exemplarily, the update of the system control logic first establishes a time series model based on historical concentration data, and uses the ARIMA algorithm to predict the pollutant concentration. The parameter combination of p = 2, d = 1, and q = 1 is optimized and determined by the AIC criterion. The model uses a 5-minute sampling interval and inputs the historical data of the past 24 hours (such as the PM2.5 concentration sequence [35, 37, 41...]), and outputs the predicted value sequence of the next 1 hour [45.2, 47.8, 49.3...]. The prediction module corrects the deviation in real time through Kalman filtering. When the root mean square error between the predicted value and the real-time monitoring value exceeds the threshold of 3 μg / m 3 it triggers model retraining. The abnormal probability calculation uses a Bayesian network. Ten-dimensional features such as the predicted concentration, meteorological data (such as wind speed of 4.5 m / s and humidity of 62%), and equipment status are input into the network, and the probability of concentration exceeding the standard within the next 30 minutes is output. When the probability value exceeds the preset threshold of 0.7, the system automatically generates a control instruction. For example, the ventilation volume is increased from 8000 m 3 / h to 12000 m 3 / h, and at the same time, the standby purification unit is activated. The long-term regulation basis analyzes quarterly data through an LSTM neural network. When training, the sliding window length is 720 (30 days × 24 hours), there are 64 nodes in the hidden layer, and the concentration trend and confidence interval of the next 7 days are output (such as the 95% confidence interval of the predicted value [38.6, 42.1] on the 3rd day is ±2.3). All algorithm modules are deployed through Docker containerization. Data interaction uses Apache Kafka to achieve millisecond-level real-time transmission, and control instructions are sent to the on-site PLC device through the ModbusTCP protocol.

[0065] The present invention provides a cyclohexanone concentration real-time monitoring and automatic adjustment system, which mainly includes:

[0066] A data acquisition and transmission module for collecting cyclohexanone concentration data through a sensor array and transmitting it to a processing unit to obtain real-time change signals in the environment;

[0067] A filtering processing module for filtering the collected original data to obtain a smoothed concentration value;

[0068] An anomaly detection and recording module for comparing the smoothed concentration value with a preset threshold. If the concentration value exceeds the safe range, an anomaly signal is generated and the timestamp is recorded, and the condition for triggering subsequent operations is determined through the anomaly signal;

[0069] A control instruction generation module for sending an instruction to a control module after obtaining an anomaly signal, activating a water replenishment and drainage device according to the instruction content, and judging whether the operation is successfully executed from the device status feedback to obtain execution result data;

[0070] The dynamic parameter adjustment module is used to analyze the response time and flow rate change of the water replenishment and drainage device through the execution result data, update the control parameters using a dynamic adjustment algorithm, and determine the precise instruction for the next operation based on the updated parameters;

[0071] The stability evaluation module is used to extract continuous concentration change trend data from the concentration monitoring module, judge whether the system has returned to the safe range according to the change trend, and obtain the current stability evaluation value;

[0072] The data processing optimization module is used to compare the stability evaluation value with a preset stability threshold. If the evaluation value is lower than the threshold, it optimizes the sensor data processing flow through an adaptive filtering algorithm to obtain the optimized concentration monitoring result;

[0073] The real-time regulation strategy module is used to transmit the optimized concentration monitoring result to the central processing unit after obtaining it, generate a real-time regulation strategy for the result data, judge the operation frequency and duration of the water replenishment and drainage device through the regulation strategy, and obtain the adjusted operation parameters;

[0074] The operation optimization and prediction module is used to drive the water replenishment and drainage device to execute operations through the adjusted operation parameters, extract the actual execution effect data from the operation log of the device, judge the stable state of the system in a dynamic environment for the effect data, and obtain the final operation optimization plan; update the control logic of the entire system according to the final operation optimization plan, use a prediction analysis algorithm to estimate future concentration changes, and judge the probability of potential anomalies occurring for the estimation result to obtain the regulation basis for long-term operation.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. The present invention has only been described in detail with reference to the preferred embodiments. Those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and should all be covered within the scope of the claims of the present invention.

Claims

1. A real-time monitoring and automatic adjustment method for cyclohexanone concentration, characterized in that The method includes the following steps: Step S101: Collect cyclohexanone concentration data through a sensor array and transmit it to a processing unit, obtain real-time change signals in the environment, perform filtering processing on the collected original data, and obtain a smoothed concentration value; Step S102: Compare the smoothed concentration value with a preset threshold. If the concentration value exceeds the safe range, generate an abnormal signal and record the timestamp, and determine the conditions for triggering subsequent operations through the abnormal signal; Step S103: After obtaining the abnormal signal, send an instruction to a control module, activate a water replenishment and drainage device according to the instruction content, judge whether the operation is successfully executed from the device status feedback, and obtain execution result data; Step S104: Analyze the response time and flow rate change of the water replenishment and drainage device through the execution result data, update the control parameters using a dynamic adjustment algorithm, and determine an accurate instruction for the next operation according to the updated parameters; Step S105: Extract continuous concentration change trend data from a concentration monitoring module, judge whether the system has returned to the safe range according to the change trend, and obtain the current stability evaluation value; Step S106: Compare the stability evaluation value with a preset stability threshold. If the evaluation value is lower than the threshold, optimize the sensor data processing flow through an adaptive filtering algorithm to obtain an optimized concentration monitoring result; Step S107: After obtaining the optimized concentration monitoring result, transmit it to a central processing unit, generate a real-time regulation strategy for the result data, judge the operation frequency and duration of the water replenishment and drainage device through the regulation strategy, and obtain adjusted operation parameters; Step S109: Drive the water replenishment and drainage device to execute an operation through the adjusted operation parameters, extract actual execution effect data from the operation log of the device, judge the stable state of the system in a dynamic environment according to the effect data, and obtain a final operation optimization plan; Step S109: Update the control logic of the entire system according to the final operation optimization plan, use a predictive analysis algorithm to estimate future concentration changes, judge the probability of potential abnormalities according to the estimation result, and obtain a regulation basis for long-term operation.

2. The real-time monitoring and automatic adjustment method of cyclohexanone concentration according to claim 1, wherein, Step S101 includes: Obtain the original concentration data in the environment; Process the original concentration data using a mean filtering algorithm to obtain a preliminary concentration value; If the preliminary concentration value exceeds a preset threshold, perform secondary filtering processing on the preliminary concentration value to obtain adjusted concentration data; According to the adjusted concentration data, obtain a trend signal of real-time change; Compare the trend signal with a pre-established reference signal to judge the abnormal state of concentration change; According to the abnormal state, determine the dynamic characteristics of the environmental signal; According to the dynamic characteristics, obtain the final output value of the smoothed concentration; Among them, the original concentration data is collected through a sensor array and transmitted to a processing unit; The mean filtering algorithm, the secondary filtering processing, the preset threshold, and the reference signal are all preset; The trend signal represents the real-time change trend of concentration data; The abnormal state includes concentration mutation and continuous increase or decrease of concentration; The dynamic characteristics include concentration change rate and change amplitude.

3. The real-time monitoring and automatic adjustment method for cyclohexanone concentration according to claim 1, characterized in that The step S102 includes: Obtain the collected concentration value data, process the concentration value data by using the moving average method to obtain smoothed concentration data; Compare the smoothed concentration data with a preset threshold value. If it exceeds the safety range, generate an abnormal signal; For the abnormal signal, use a timestamp recording tool to record the current time to obtain signal data with a time mark; Based on the signal data with a time mark, use a decision tree algorithm to judge the trigger condition and determine whether it meets the requirements of subsequent operations; Obtain the signal data that meets the conditions, match subsequent operations through a preset rule table to obtain an operation instruction; According to the operation instruction, use a log recording tool to store the execution process to obtain an operation execution record; Based on the operation execution record, use a data verification method to judge data consistency and determine the final processing result.

4. A method for real-time monitoring and automatic adjustment of cyclohexanone concentration according to any one of claims 1-3, characterized in that, The step S103 includes: Obtain a sensor data stream, where the sensor data stream includes a plurality of continuously collected signal data; Process the sensor data stream through an anomaly detection algorithm to obtain an abnormal signal pattern; According to the abnormal signal pattern, obtain a corresponding control instruction from a preset instruction library, where the preset instruction library includes a mapping relationship between a preset number of types of abnormal signal patterns and control instructions; Send the control instruction to a make-up and drainage device control module, and the control instruction is used to drive the execution unit of the make-up and drainage device; Collect the real-time status data of each execution unit of the make-up and drainage device, and the real-time status data reflects the current operating status of the execution unit; Compare the real-time status data with the expected status data through a status matching algorithm to obtain a status consistency result; If the status consistency result indicates that the current status is consistent with the expected status, judge that the instruction execution is successful and record the execution result of the control instruction; If the status consistency result indicates that the current status is inconsistent with the expected status, regenerate a correction instruction according to the real-time status data; Update the control instruction through the correction instruction and send the updated control instruction to the make-up and drainage device control module; Repeat collecting the real-time status data and comparing through the status matching algorithm to obtain a new status consistency result until it is judged that the instruction execution is successful.

5. A method for real-time monitoring and automatic adjustment of cyclohexanone concentration according to any one of claims 1-3, characterized in that, The step S104 includes: Obtain the execution result data of the make-up and drainage device, where the execution result data includes response time and flow rate change information; Calculate the characteristic values of the response time and flow rate change according to the execution result data to obtain a preliminary analysis result; Process the preliminary analysis result by using a dynamic adjustment algorithm, update the control parameters, and determine an adjusted parameter set; If the adjusted parameter set exceeds a preset threshold value, correct the adjusted parameter set through secondary analysis to obtain a stable parameter value; Generate an accurate instruction for the next operation according to the stable parameter value and judge whether the accurate instruction meets the device operation conditions; Verify the accurate instruction through simulation operation to obtain the response time and flow rate change trend after operation; Adjust the parameter weight of the dynamic adjustment algorithm according to the flow rate change trend to determine an optimized control strategy. Recalculate the execution result data using the optimized control strategy to obtain the final operation instruction.

6. A method for real-time monitoring and automatic adjustment of cyclohexanone concentration according to any one of claims 1-3, characterized in that, The step S106 includes: Obtain sensor data and calculate the stability evaluation value of the sensor data; Compare the stability evaluation value with a preset threshold to determine whether the sensor data meets the stability standard; If the stability evaluation value is lower than the preset threshold, process the sensor data using an adaptive filtering algorithm to obtain preliminary optimized data; Execute a data processing flow through the preliminary optimized data to obtain smoothed concentration-related data; Determine whether there is abnormal fluctuation based on the smoothed concentration-related data and judge the data quality; Use a mean calculation method to correct the abnormal fluctuation data to obtain stable concentration data; Generate a concentration monitoring result through the stable concentration data to obtain the final output data; Judge the concentration change trend based on the comparison between the final output data and historical data.

7. A method for real-time monitoring and automatic adjustment of cyclohexanone concentration according to any one of claims 1-3, characterized in that, The step S107 includes: Obtain concentration monitoring data, and process the concentration monitoring data through a preset filtering algorithm to obtain an optimized monitoring result; Send the optimized monitoring result to the central processing unit through a data transmission channel to determine the transmission completion status; Analyze the optimized monitoring result using a machine learning algorithm in the central processing unit to obtain a real-time regulation strategy; Judge the operation requirements of the water replenishing device and the liquid discharging device according to the real-time regulation strategy to obtain a preliminary instruction for device operation; Calculate the operation frequency and operation duration through the preliminary instruction to determine the adjusted operation parameters; Update the device operation status according to the adjusted operation parameters to obtain new concentration monitoring data; If the new concentration monitoring data exceeds the preset threshold, optimize the real-time regulation strategy through an iterative algorithm to judge the final operation parameters.

8. A method for real-time monitoring and automatic adjustment of cyclohexanone concentration according to any one of claims 1-3, characterized in that, The step S108 includes: Obtain the operation log data of the water replenishing and liquid discharging device; Extract the actual execution effect data from the operation log data; Process the execution effect data to obtain the system operation characteristic value; Analyze the dynamic environment change trend using the system operation characteristic value to judge the system response status; If the system response status exceeds the preset threshold, adjust the operation parameters through a regression algorithm to obtain an optimized parameter set; Drive the water replenishing and liquid discharging device to execute an operation using the optimized parameter set to obtain new effect data; Compare the new effect data with the historical effect data to judge the system stable state; Adjust the optimization scheme according to the system stable state to determine the final operation strategy; Drive the water replenishing and liquid discharging device to execute an operation using the final operation strategy.

9. A real-time monitoring and automatic adjustment system for cyclohexanone concentration, characterized in that, This system is used to implement the method for real-time monitoring and automatic regulation of cyclohexanone concentration according to any one of claims 1-8. The system includes: A data acquisition and transmission module for collecting cyclohexanone concentration data through a sensor array and transmitting it to the processing unit to obtain real-time change signals in the environment; A filtering processing module for filtering the collected original data to obtain smoothed concentration values; Anomaly Detection and Recording Module, which is used to compare the smoothed concentration value with a preset threshold. If the concentration value exceeds the safe range, an anomaly signal is generated and the timestamp is recorded, and the conditions for triggering subsequent operations are determined through the anomaly signal; Control Instruction Generation Module, which is used to send an instruction to the control module after obtaining the anomaly signal, activate the water replenishment and drainage device according to the instruction content, and judge whether the operation is successfully executed from the device status feedback to obtain the execution result data; Dynamic Parameter Adjustment Module, which is used to analyze the response time and flow rate change of the water replenishment and drainage device through the execution result data, update the control parameters using a dynamic adjustment algorithm, and determine the precise instruction for the next operation for the updated parameters; Stability Evaluation Module, which is used to extract continuous concentration change trend data from the concentration monitoring module, and judge whether the system has returned to the safe range according to the change trend to obtain the current stability evaluation value; Data Processing Optimization Module, which is used to compare the stability evaluation value with a preset stability threshold. If the evaluation value is lower than the threshold, the sensor data processing process is optimized through an adaptive filtering algorithm to obtain the optimized concentration monitoring result; Real-time Regulation Strategy Module, which is used to transmit the optimized concentration monitoring result to the central processing unit after obtaining it, generate a real-time regulation strategy for the result data, and judge the operation frequency and duration of the water replenishment and drainage device through the regulation strategy to obtain the adjusted operation parameters; Operation Optimization and Prediction Module, which is used to drive the water replenishment and drainage device to execute operations through the adjusted operation parameters, extract the actual execution effect data from the operation log of the device, judge the stable state of the system in the dynamic environment according to the effect data to obtain the final operation optimization plan; update the control logic of the entire system according to the final operation optimization plan, use the prediction analysis algorithm to estimate the future concentration change, and judge the probability of potential anomalies occurring according to the estimation result to obtain the regulation basis for long-term operation.

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