Emulsion paint production temperature control management system and method

Abstract

The invention discloses an emulsion paint production temperature control management system and method, and relates to the technical field of automatic temperature control. According to the emulsion paint production temperature control management system and method, by arranging the emulsion paint temperature monitoring module, the temperature abnormity module and the temperature adjusting module, accurate temperature control in the emulsion paint production process is achieved. The temperature change of the latex paint in different production stages is monitored in real time through a temperature sensor, a temperature fluctuation curve is generated through time sequence analysis, periodic fluctuation and amplitude characteristics of temperature are recognized based on a spectrum similarity analysis technology, a temperature fluctuation intensity index is calculated, and whether temperature abnormity exists or not is judged by comparing the temperature fluctuation intensity index with a preset threshold value; when the temperature abnormity is identified, the temperature adjusting module dynamically adjusts the power output of the temperature control equipment through a PID control algorithm, and precise temperature control is achieved. The temperature control precision and the stability in the production process are obviously improved, and the quality consistency and the production efficiency of the latex paint are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of automatic temperature control, and in particular to a temperature control management system and method for latex paint production. Background Art

[0002] With the rapid development of the building decoration industry, latex paint, as a commonly used environmentally friendly paint, is widely used in indoor and outdoor decoration, and the market demand continues to grow. Consumers have higher requirements for the quality and environmental performance of paints, especially in terms of precise temperature control, uniformity of ingredients and stability. Accurate temperature control has an important impact on the chemical reaction in latex paint production and the quality of the finished product. Therefore, an efficient temperature control management system has become a key technology for manufacturers to improve product quality and competitiveness.

[0003] Existing temperature control systems for latex paint production mostly use traditional manual or simple automated control modes, lacking real-time monitoring and dynamic adjustment of key temperature parameters, making it difficult to effectively control sudden temperature fluctuations during the production process. Due to system response delays or insufficient data collection accuracy, uneven temperature control or untimely response may occur, resulting in unstable performance of paint products and affecting the consistency of finished products. In addition, it is difficult for traditional systems to flexibly adjust temperature control strategies according to the characteristics of different formulas, thus limiting the adaptability of latex paint in different usage scenarios and further optimization of product quality. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a temperature control management system and method for latex paint production, which monitors the temperature changes in the latex paint production process in real time, combines the temperature fluctuation characteristics analysis, and uses spectrum similarity analysis to obtain the temperature fluctuation intensity index, so as to accurately judge the temperature abnormality. At the same time, the power of the temperature control equipment is dynamically adjusted through the PID control algorithm, which solves the problems of slow response, uneven temperature control and lack of flexible adjustment ability of the traditional temperature control system, and improves the temperature control accuracy, stability and adaptability in the latex paint production process.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a latex paint production temperature control management system, comprising the following modules: a latex paint temperature monitoring module, a temperature anomaly module, and a temperature adjustment module; the latex paint temperature monitoring module is used to monitor the temperature changes of latex paint in the three stages of polymerization reaction, dispersion mixing, and cooling through a temperature sensor, and analyze the temperature fluctuation characteristics through a time series to generate a temperature fluctuation curve; the temperature anomaly module is used to obtain historical temperature data of the latex paint stage, perform spectrum similarity analysis on the temperature fluctuation curve, identify temperature periodic fluctuations and amplitude characteristics, obtain a temperature fluctuation intensity index, and determine whether the temperature of the latex paint production process is abnormal; the temperature adjustment module is used to identify abnormal temperatures in the latex paint production stage, and dynamically adjust the power of the latex paint temperature control equipment through a PID control algorithm.

[0006] Furthermore, the temperature fluctuation characteristics are analyzed through time series, and the specific process of generating a temperature fluctuation curve is as follows: real-time temperature data of the three stages of polymerization reaction, dispersion mixing, and cooling are obtained from the latex paint temperature monitoring module, and continuous sampling is performed at set time intervals; the collected temperature data is denoised and filtered to remove outliers and noise, and the temperature data is modeled as a time series through an autoregressive moving average model to identify temperature change trends and periodic fluctuations; the autoregressive moving average model is analyzed to extract the characteristics of temperature fluctuations, including the amplitude, period, and frequency of the fluctuations, and a temperature fluctuation curve is constructed.

[0007] Furthermore, the specific process of performing spectrum similarity analysis on the temperature fluctuation curve and identifying the periodic temperature fluctuation and amplitude characteristics is as follows: performing fast Fourier transform (FFT) on the temperature fluctuation curve to convert it into frequency domain representation, analyzing the main frequency components in the spectrum diagram, and identifying the frequency and amplitude of the periodic fluctuation; comparing the spectrum characteristics of different stages to extract the periodic change pattern of temperature fluctuation, judging the deviation between the intensity of temperature fluctuation and process requirements based on the peak amplitude analysis of the spectrum, and classifying and marking the periodic fluctuation and amplitude characteristics.

[0008] Furthermore, the specific process of obtaining the temperature fluctuation intensity index is as follows: according to the spectrum analysis results, the amplitude information in the spectrum is extracted, expressed as A(f), where A(f) is the amplitude value at frequency f, indicating the intensity of the frequency component; the energy distribution of each frequency band in the spectrum is calculated, which is defined as E(f) = A(f) 2 , where E(f) is the energy value at frequency f, which is the square of the amplitude A(f), indicating the contribution of the frequency component to the overall signal energy; the spectrum signal is divided into multiple frequency bands f1, f2, …, f n , and calculate the total energy E in each frequency band total , the formula is as follows: In the formula, E total Indicates the frequency band [ f1,f n] The total energy within the frequency band reflects the importance of the frequency band in the total energy. n are the starting frequency and ending frequency of the frequency band, respectively, to determine the range of the frequency band. The energy of all frequency components in the frequency band is summed to obtain the total energy of the frequency band; the temperature fluctuation intensity I is obtained by combining the fluctuation intensity of each frequency band. temp , defined as the weighted average of the energy of each frequency band, the formula is as follows: In the formula, E i : The energy of the ith frequency band, reflecting the contribution of the frequency components of this band to the temperature fluctuation, w i: Weight factor, used to indicate the relative contribution of the ith frequency band to the overall temperature fluctuation intensity, usually adjusted based on the importance of the frequency band, n: total number of frequency bands, indicating the number of frequency bands divided in the spectrum analysis; according to the fluctuation intensity index I temp , the temperature fluctuation intensity index is further normalized to obtain the normalized temperature fluctuation intensity index ξ temp , the formula is as follows: In the formula, ξ temp : Normalized temperature fluctuation intensity index, indicating the intensity of temperature fluctuation, I min : The lowest temperature fluctuation intensity in the historical data, used in the normalization process to ensure that the lower limit of the index is 0, I max : The highest temperature fluctuation intensity in the historical data, used in the normalization process to ensure that the upper limit of the index is 1.

[0009] Furthermore, the specific process of judging whether the temperature of the latex paint production process is abnormal is as follows: based on the temperature fluctuation intensity index, the temperature fluctuation intensity index is compared with the set temperature fluctuation intensity index threshold. When the temperature fluctuation intensity index exceeds the set threshold, it indicates that the temperature of the latex paint production process is abnormal.

[0010] Furthermore, the specific process of identifying abnormal temperature in the latex paint production stage is as follows: obtain the temperature fluctuation intensity index of each stage in the latex paint production process, determine whether the temperature of the latex paint production process is abnormal based on the threshold of the temperature fluctuation intensity index of each stage in the latex paint production process, and identify abnormal temperature in the latex paint production stage.

[0011] Furthermore, the logic of dynamically adjusting the power of the latex paint temperature control equipment through the PID control algorithm is as follows: real-time detection of the deviation between the temperature fluctuation intensity index of each stage of the latex paint production process and the set temperature fluctuation intensity index threshold value, as the input signal of the PID control; according to the size of the deviation, calculate the proportional term, that is, the product of the current deviation and the proportional gain, to reflect the immediate impact of the current deviation on the system control; calculate the integral term, that is, the product of the accumulated value of historical errors and the integral gain, to eliminate long-term errors and gradually correct the temperature; calculate the differential term, that is, the product of the error change rate and the differential gain, to predict future temperature change trends; add the calculation results of the proportional, integral and differential terms to generate a comprehensive control signal, which indicates the power output of the temperature control equipment that needs to be adjusted.

[0012] A temperature control management method for latex paint production includes the following steps: S1. monitoring the temperature changes of latex paint in the three stages of polymerization reaction, dispersion mixing and cooling through a temperature sensor, and analyzing the temperature fluctuation characteristics through a time series to generate a temperature fluctuation curve; S2. acquiring historical temperature data of the latex paint stage, performing spectrum similarity analysis on the temperature fluctuation curve, identifying temperature periodic fluctuations and amplitude characteristics, acquiring a temperature fluctuation intensity index, and judging whether the temperature in the latex paint production process is abnormal; S3. identifying abnormal temperatures in the latex paint production stage, and dynamically adjusting the power of the latex paint temperature control equipment through a PID control algorithm.

[0013] The present invention has the following beneficial effects:

[0014] (1) This kind of latex paint production temperature control management system monitors the temperature changes of latex paint in the three key production stages of polymerization reaction, dispersion mixing and cooling in real time through the temperature sensor of the latex paint temperature monitoring module, and generates a temperature fluctuation curve based on time series analysis, which can effectively capture the characteristics of temperature changes over time. It provides basic information for subsequent abnormal judgment and temperature control, and improves the accuracy of temperature control and the timeliness of temperature data in the latex paint production process. The temperature anomaly module further identifies the periodicity and amplitude characteristics of temperature fluctuations through spectrum similarity analysis of the temperature fluctuation curve, and obtains a representative temperature fluctuation intensity index. The system can accurately judge the temperature stability in the latex paint production process, so as to quickly identify abnormal temperature when the temperature exceeds the set threshold, which helps to ensure the quality stability of latex paint products.

[0015] (2) This method of temperature control management for latex paint production dynamically adjusts the power of the temperature control equipment through a PID control algorithm, so that the system can adjust the control signal in real time according to the temperature deviation. This control method ensures the system's rapid response to temperature changes, significantly reduces the problem of uneven quality of latex paint caused by abnormal temperature, realizes flexible adaptation to different temperature control requirements in the latex paint production process, effectively improves the temperature control accuracy and process adaptability of the production process, and provides strong support for the consistency of latex paint product quality.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of a temperature control management system for latex paint production.

[0018] Figure 2 The present invention is a flow chart of a temperature control management method for latex paint production. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the problems of slow response, uneven temperature control and lack of flexible adjustment capability of traditional temperature control systems through a temperature control management system and method for latex paint production.

[0020] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0021] The temperature sensor is used to monitor the temperature changes of latex paint in the three stages of polymerization reaction, dispersion mixing and cooling. The temperature fluctuation characteristics are analyzed through time series to generate a temperature fluctuation curve.

[0022] Obtain historical temperature data of the latex paint stage, perform spectrum similarity analysis on the temperature fluctuation curve, identify temperature periodic fluctuations and amplitude characteristics, obtain the temperature fluctuation intensity index, and determine whether the temperature of the latex paint production process is abnormal.

[0023] Identify abnormal temperatures during the latex paint production stage and dynamically adjust the power of latex paint temperature control equipment through PID control algorithm.

[0024] See also Figure 1 The embodiment of the present invention provides a technical solution: a latex paint production temperature control management system, comprising the following modules: a latex paint temperature monitoring module, a temperature anomaly module, and a temperature adjustment module; the latex paint temperature monitoring module is used to monitor the temperature changes of the latex paint in the three stages of polymerization reaction, dispersion mixing, and cooling through a temperature sensor, and analyze the temperature fluctuation characteristics through a time series to generate a temperature fluctuation curve; the temperature anomaly module is used to obtain historical temperature data of the latex paint stage, perform spectrum similarity analysis on the temperature fluctuation curve, identify temperature periodic fluctuations and amplitude characteristics, obtain a temperature fluctuation intensity index, and determine whether the temperature of the latex paint production process is abnormal; the temperature adjustment module is used to identify abnormal temperature in the latex paint production stage, and dynamically adjust the power of the latex paint temperature control equipment through a PID control algorithm.

[0025] In the present embodiment, the latex paint temperature monitoring module monitors the temperature changes of different stages (such as polymerization, dispersion mixing and cooling stages) of the latex paint in the production process in real time through a temperature sensor. By performing time series analysis on the temperature data, the fluctuation characteristics of the temperature can be identified and a temperature fluctuation curve can be generated. The curve reflects the regularity of temperature fluctuations in different stages and provides a data basis for subsequent temperature anomaly identification. The temperature anomaly module uses a spectrum similarity analysis method to analyze the temperature fluctuation curve based on the historical temperature data of each production stage of the latex paint, and extracts the periodic changes and amplitude characteristics of the temperature fluctuation. By calculating the temperature fluctuation intensity index, it is determined whether there is temperature anomaly. When the temperature fluctuation exceeds the set threshold, the system identifies it as temperature anomaly, which provides a basis for the next step of temperature adjustment. The temperature adjustment module dynamically adjusts the power output of the latex paint temperature control device through the PID control algorithm. The PID algorithm calculates the proportional (P), integral (I) and differential (D) items according to the temperature error (i.e., the deviation between the set temperature and the actual temperature) detected in real time, generates a control signal, and adjusts the equipment power, thereby achieving accurate temperature control. The PID control can effectively correct the temperature fluctuation and ensure the stability and uniformity of the temperature in the latex paint production process. A temperature sensor is a device used to detect temperature and can convert temperature information into electrical signals for subsequent system processing. It can monitor the temperature changes of latex paint at different stages in the production process in real time and provide data support. Time series analysis is an analysis method in statistics that is used to analyze data that changes over time. By analyzing the temperature fluctuation curve in the production process of latex paint, time series analysis can identify the regularity and trend of temperature changes, providing a basis for subsequent abnormal temperature identification. Spectral similarity analysis is a signal processing technology that mainly converts time domain signals into frequency domain signals and analyzes the spectral characteristics of signals. This process converts the temperature fluctuation curve into frequency domain data through fast Fourier transform (FFT), and then identifies the periodic changes and amplitude characteristics of temperature fluctuations. By analyzing the peak value, fluctuation frequency and other information of the spectrum, it is determined whether there is abnormal temperature fluctuation. The temperature fluctuation intensity index is a value obtained through spectrum analysis, which is used to quantitatively describe the intensity of temperature fluctuations. The index combines the temperature fluctuation energy in different frequency bands and reflects the amplitude and change trend of temperature fluctuations. If the index exceeds the set threshold, it means that there may be abnormal temperature fluctuations in the production process of latex paint. PID control (proportional-integral-differential control) is a commonly used automatic control algorithm for automatically adjusting the output of the system to achieve the set target. In the present invention, the PID algorithm controls the power output of the latex paint temperature control equipment according to the temperature deviation calculated by the temperature anomaly detection module to ensure temperature stability. The PID algorithm consists of three parts: Proportional term (P): Adjusted according to the current temperature error to control the rate of system reaction. Integral term (I): Adjusted according to the accumulation of errors over time to eliminate long-term system deviations.Differential term (D): Adjusts based on the rate of temperature change to prevent over-adjustment or oscillation of the system. By adjusting these three items, the PID algorithm can dynamically and accurately control the temperature control equipment to ensure that the temperature remains within a predetermined range during the production process. The power of the temperature control equipment refers to the energy output of the heating or cooling equipment in the temperature control system. The PID control algorithm adjusts the output of the equipment power to compensate for the deviation caused by temperature fluctuations to ensure that the temperature remains stable during the latex paint production process. The temperature fluctuation curve is a graph that shows the change of temperature over time, which can reflect the fluctuation characteristics of the temperature during the latex paint production process. By performing time series analysis and spectrum analysis on the curve, the law of temperature change during the production process can be revealed, and whether there are abnormal fluctuations can be identified.

[0026] Specifically, the temperature fluctuation characteristics are analyzed through time series, and the specific process of generating a temperature fluctuation curve is as follows: real-time temperature data of the three stages of polymerization reaction, dispersion mixing, and cooling are obtained from the latex paint temperature monitoring module, and continuous sampling is performed at set time intervals; the collected temperature data is denoised and filtered to remove outliers and noise, and the temperature data is modeled as a time series through an autoregressive moving average model to identify temperature change trends and periodic fluctuations; the autoregressive moving average model is analyzed to extract the characteristics of temperature fluctuations, including the amplitude, period, and frequency of the fluctuations, and a temperature fluctuation curve is constructed.

[0027] In this embodiment, the latex paint temperature monitoring module is responsible for real-time collection of temperature data in the latex paint production process, covering three key stages: polymerization, dispersion mixing, and cooling. The temperature data of each stage will be continuously sampled according to the set time interval, so that the temperature changes in each stage can be recorded in real time. The collected temperature data usually contains noise or outliers, and these inaccurate data may affect the accuracy of subsequent analysis. Therefore, firstly, outliers and noise in the data are eliminated by denoising and filtering to ensure the smoothness and accuracy of the data. The denoising process can use a simple filtering algorithm (such as low-pass filtering, moving average filtering, etc.) to reduce the impact of noise on the analysis results. The autoregressive moving average (ARMA) model is a statistical method commonly used in time series analysis. It establishes the relationship between current data and past data by analyzing the correlation of historical data. By using the ARMA model, the temperature data in the latex paint production process can be modeled, the future temperature change trend can be predicted, and the periodic fluctuations in the data can be identified. This modeling process helps us capture the dynamic characteristics of temperature changes, such as temperature rise, fall or periodic fluctuations. On the basis of the ARMA model, the key features of temperature fluctuations are further extracted. These features include: Amplitude: Indicates the intensity or amplitude of temperature fluctuations, reflecting the severity of temperature fluctuations. Cycle: Indicates the time period of repeated temperature fluctuations, helping us understand the regularity of temperature fluctuations. Frequency: The frequency of temperature fluctuations, reflecting the speed of temperature changes. Through these features, we can fully understand the temperature change pattern during the production process of latex paint. Based on the extracted features (amplitude, cycle, frequency, etc.), a temperature fluctuation curve is constructed. This curve can clearly show the temperature change trend and fluctuation characteristics during the production process of latex paint, and serve as the basis for subsequent abnormal temperature judgment and adjustment.

[0028] Specifically, the specific process of performing spectrum similarity analysis on the temperature fluctuation curve and identifying the temperature periodic fluctuation and amplitude characteristics is as follows: performing fast Fourier transform (FFT) on the temperature fluctuation curve, converting it into frequency domain representation, analyzing the main frequency components in the spectrum diagram, and identifying the frequency and amplitude of the periodic fluctuation; comparing the spectrum characteristics of different stages, extracting the periodic change pattern of temperature fluctuation, judging the deviation of the intensity of temperature fluctuation from the process requirements based on the peak amplitude analysis of the spectrum, and classifying and marking the periodic fluctuation and amplitude characteristics.

[0029] In this embodiment, fast Fourier transform (FFT): Since the temperature change in the latex paint production process usually has a certain periodic fluctuation, and the Fourier transform can effectively convert the time domain signal into the frequency domain signal, we use the fast Fourier transform (FFT) to convert the temperature fluctuation curve. Through FFT, the time variation data of temperature (temperature fluctuation curve) will be converted into a frequency domain signal. FFT can convert the signal from the time domain representation to the frequency domain representation and display the frequency components of the signal. Specifically, FFT can decompose the original temperature fluctuation curve into a series of sine waves of different frequencies, each sine wave represents a frequency component in the temperature change. Through this conversion process, we can extract its frequency characteristics from the periodic changes of temperature fluctuations. After FFT conversion, we will get a spectrum diagram that shows the different frequency components of temperature fluctuations and their corresponding amplitudes. The "peak" part in the spectrum diagram represents the main periodic variation components of temperature fluctuations. These peaks show the dominant frequency and fluctuation amplitude of temperature fluctuations in the latex paint production process. By identifying the main frequency components in the spectrum diagram, we can clearly identify the periodic characteristics of temperature fluctuations. For example, if the amplitude of a frequency component in the spectrum is large, it means that the frequency component has a significant impact on the temperature fluctuation, which may represent a fixed temperature fluctuation cycle in the production process. Extracting the periodic change pattern of temperature fluctuation: By comparing the spectrum characteristics of different stages, we can extract the temperature fluctuation pattern of each stage in the latex paint production process. For example, the temperature fluctuation pattern in the polymerization stage may be different from the fluctuation pattern in the cooling stage. Through spectrum analysis, the periodic change pattern of temperature fluctuation in each production stage, such as the frequency and period of temperature fluctuation, can be identified. Comparing the spectrum characteristics of different stages can help determine the temperature control requirements and characteristics of each stage, thereby providing valuable information for subsequent temperature control adjustments. By analyzing the amplitude of the peak in the spectrum, the deviation between the intensity of temperature fluctuation and the production process requirements can be determined. If the amplitude of the temperature fluctuation exceeds the set process requirement range, it may indicate that the temperature control is abnormal and corresponding adjustment measures need to be taken. For example, if the temperature fluctuation amplitude of a certain production stage is large and the peak amplitude in the spectrum exceeds the set threshold, it means that the temperature fluctuation intensity of this stage is high, which may affect the quality of latex paint. Classify and label periodic fluctuations and amplitude characteristics: After analyzing and extracting the periodic characteristics and amplitude characteristics of temperature fluctuations, these characteristics will be classified and labeled. The purpose of classification and labeling is to systematically identify different types of temperature fluctuations and associate them with different stages or process requirements in the production process. In this way, specific types of fluctuations can be prioritized during the temperature control process and the temperature control strategy can be adjusted in a timely manner.

[0030] Specifically, the specific process of obtaining the temperature fluctuation intensity index is as follows: According to the spectrum analysis results, the amplitude information in the spectrum is extracted and expressed as A ( f ) , where A ( f ) is the amplitude value at frequency f, indicating the intensity of the frequency component; calculate the energy distribution of each frequency band in the spectrum, defined as E ( f ) =A ( f ) 2 , where E ( f ) is the energy value at frequency f, which is the amplitude A ( f ) The square of the frequency component represents the contribution of the overall signal energy; the spectrum signal is divided into multiple frequency bands f1, f2, ..., f n , and calculate the total energy E in each frequency band total , the formula is as follows: In the formula, E total Indicates the frequency band [ f1,f n] The total energy within the frequency band reflects the importance of the frequency band in the total energy. n are the starting frequency and ending frequency of the frequency band, respectively, to determine the range of the frequency band. The energy of all frequency components in the frequency band is summed to obtain the total energy of the frequency band; the temperature fluctuation intensity I is obtained by combining the fluctuation intensity of each frequency band. temp , defined as the weighted average of the energy of each frequency band, the formula is as follows: In the formula, E i : The energy of the ith frequency band, reflecting the contribution of the frequency components of this band to the temperature fluctuation, w i : Weight factor, used to indicate the relative contribution of the ith frequency band to the overall temperature fluctuation intensity, usually adjusted based on the importance of the frequency band, n: total number of frequency bands, indicating the number of frequency bands divided in the spectrum analysis; according to the fluctuation intensity index I temp , the temperature fluctuation intensity index is further normalized to obtain the normalized temperature fluctuation intensity index ξ temp , the formula is as follows: In the formula, ξ temp : Normalized temperature fluctuation intensity index, indicating the intensity of temperature fluctuation, I min : The lowest temperature fluctuation intensity in the historical data, used in the normalization process to ensure that the lower limit of the index is 0, I max : The highest temperature fluctuation intensity in the historical data, used in the normalization process to ensure that the upper limit of the index is 1.

[0031] In this implementation scheme, the frequency components in the temperature fluctuation curve are extracted by spectrum analysis, including the amplitude and energy information of each frequency. The amplitude reflects the intensity of different frequency components, and the energy represents the contribution of these components to the overall fluctuation. Calculate the energy distribution: divide the spectrum signal into multiple frequency bands, and calculate the total energy in each frequency band respectively. Each frequency band represents a specific temperature fluctuation period interval, and the energy of different frequency bands reflects the intensity change of temperature fluctuation in different time periods. Comprehensive fluctuation intensity: the overall temperature fluctuation intensity index is calculated by weighted averaging the energy of each frequency band. The weights of different frequency bands are adjusted according to their importance in the temperature fluctuation, so as to obtain a comprehensive fluctuation intensity index that can reflect the overall temperature fluctuation. Normalization: In order to facilitate comparison, the fluctuation intensity index is further normalized and adjusted to a standard range to ensure that its value is between 0 and 1. After normalization, it can be compared with the minimum and maximum values ​​in the historical data to better evaluate whether the current temperature fluctuation exceeds the normal range.

[0032] Specifically, the specific process of judging whether the temperature of the latex paint production process is abnormal is as follows: based on the temperature fluctuation intensity index, the temperature fluctuation intensity index is compared with the set temperature fluctuation intensity index threshold. When the temperature fluctuation intensity index exceeds the set threshold, it indicates that the temperature of the latex paint production process is abnormal.

[0033] In this embodiment, the possible temperature anomaly in the latex paint production process is effectively identified by comparing the temperature fluctuation intensity index with the set threshold. This method ensures that the temperature control of the production process has a high degree of accuracy and timely response capability, avoiding the problem of unstable temperature fluctuations caused by the lack of precise monitoring and automatic adjustment in traditional methods, thereby effectively ensuring the quality and consistency of latex paint products.

[0034] Specifically, the specific process of identifying abnormal temperature in the latex paint production stage is as follows: obtain the temperature fluctuation intensity index of each stage in the latex paint production process, determine whether the temperature of the latex paint production process is abnormal based on the threshold of the temperature fluctuation intensity index of each stage in the latex paint production process, and identify abnormal temperature in the latex paint production stage.

[0035] In this implementation, by real-time monitoring of the temperature fluctuation intensity index at each stage of the latex paint production process and comparing it with the threshold, temperature anomalies can be accurately identified. Compared with traditional manual monitoring, the system's automated judgment not only improves the response speed, but also reduces the impact of human factors on temperature management, ensuring the stability of the production process and the consistency of the product.

[0036] Specifically, the logic of dynamically adjusting the power of latex paint temperature control equipment through PID control algorithm is as follows: real-time detection of the deviation between the temperature fluctuation intensity index of each stage of the latex paint production process and the set temperature fluctuation intensity index threshold value as the input signal of PID control; according to the size of the deviation, calculate the proportional term, that is, the product of the current deviation and the proportional gain, to reflect the immediate impact of the current deviation on the system control; calculate the integral term, that is, the product of the accumulated value of historical errors and the integral gain, to eliminate long-term errors and gradually correct the temperature; calculate the differential term, that is, the product of the error change rate and the differential gain, to predict future temperature change trends; add the calculation results of the proportional, integral and differential terms to generate a comprehensive control signal, which indicates the power output of the temperature control equipment that needs to be adjusted.

[0037] In this implementation scheme, the system first monitors the temperature fluctuation intensity index of each stage of the latex paint production process in real time, and compares these indexes with the set temperature fluctuation intensity thresholds. Deviation refers to the difference between the current actual temperature fluctuation intensity and the set threshold, which serves as the input signal for PID control. This deviation reflects the gap between the current temperature state and the target temperature state, which directly affects the response of the temperature control system. Calculate the proportional term (P): The proportional term reflects the impact of the current deviation on the immediate control of the temperature control system. The proportional gain is an adjustment parameter used to determine the sensitivity of the deviation size to the system response. The specific calculation formula is: P = K p ×e ( t ) ; Where P is the output of the proportional term, indicating the immediate control response K p is the proportional gain, which determines the contribution of the deviation to the control signal, e ( t ) is the current deviation, that is, the difference between the current temperature fluctuation intensity index and the set threshold. The proportional term provides an immediate response to the current temperature deviation, allowing the system to quickly adjust to minimize the deviation. The integral term is calculated to eliminate long-term errors, especially when the proportional control cannot eliminate the persistent error. The integral term accumulates past deviations and gradually corrects the temperature. The integral term is calculated as: Among them, I is the output of the integral term, which represents the accumulation of long-term error, and K i is the integral gain, which controls the effect of the integral term on error accumulation, e ( t ) is the current deviation, The integral term represents the integral of all deviations from the start time to the current moment, reflecting the cumulative effect of the error. The integral term helps to eliminate long-term deviations in temperature control, such as drift or progressive deviations in the system. The purpose of calculating the differential term is to predict future temperature change trends and respond in advance based on the speed of the current deviation change. The differential term calculation formula is: Among them, D is the output of the differential term, which represents the prediction of the error change rate, and K d is the differential gain, which determines the sensitivity of the response to the error change rate The derivative of the error, which represents the rate of change of the deviation over time, reflects the speed of change of the current deviation. The differential term helps the system to foresee and reduce over-adjustment of temperature fluctuations by responding to the rate of change of the error, and plays a role in smoothing the system response. The calculation results of the proportional term, integral term, and differential term are added together to generate a comprehensive control signal, which is used to indicate the power output that needs to be adjusted for the temperature control device. The calculation formula for the comprehensive control signal is: Among them, u ( t ) It is the control signal, indicating the power output of the temperature control device that needs to be adjusted, K p is the proportional gain, K i is the integral gain, K d is the differential gain, e ( t ) is the current deviation, is the rate of change of the deviation.

[0038] See also Figure 2 A temperature control management method for latex paint production includes the following steps: S1. Monitoring the temperature changes of latex paint in the three stages of polymerization reaction, dispersion mixing and cooling through a temperature sensor, and analyzing the temperature fluctuation characteristics through a time series to generate a temperature fluctuation curve; S2. Acquiring historical temperature data of the latex paint stage, performing spectrum similarity analysis on the temperature fluctuation curve, identifying temperature periodic fluctuations and amplitude characteristics, obtaining the temperature fluctuation intensity index, and judging whether the temperature in the latex paint production process is abnormal; S3. Identifying abnormal temperature in the latex paint production stage, and dynamically adjusting the power of the latex paint temperature control equipment through a PID control algorithm.

[0039] In the present embodiment, step S1: in this step, the system performs real-time temperature monitoring on the key stages (polymerization reaction, dispersion mixing, cooling stage) in the latex paint production process through a temperature sensor. These three stages are respectively related to chemical reaction, raw material mixing and temperature control in the latex paint production process, and the temperature variation in each stage has an important impact on the quality of the final product. Step S2: in this step, the system will conduct a more in-depth analysis to identify the periodic fluctuation of temperature and its intensity in the latex paint production process to assess whether the temperature meets the requirements. Step S3: in this step, the system determines whether there is abnormal temperature, and dynamically adjusts the power of the temperature control device through the PID control algorithm to ensure that the temperature meets the production requirements. Through the precise control of the PID algorithm, the system can ensure the precise regulation of the temperature in the latex paint production process, avoiding the influence of excessive or too small temperature fluctuations on the quality of the latex paint. 。

[0040] In summary, the present application has at least the following effects: through real-time temperature monitoring and analysis, it is possible to accurately capture temperature fluctuations in the production process, identify abnormal temperatures in a timely manner, ensure the temperature control accuracy and stability in the latex paint production process, and avoid product quality problems caused by temperature fluctuations. The PID control algorithm is used to dynamically adjust the power of the temperature control equipment, and the temperature control equipment is accurately adjusted according to the deviation between the temperature fluctuation intensity index and the set threshold value, which greatly improves the response speed and automatic adjustment ability of the system, reduces manual intervention, and improves production efficiency. By identifying the temperature fluctuation characteristics of different production stages through spectrum analysis, the system can adopt different temperature control strategies for different stages to meet the diversified temperature control requirements in the latex paint production process and improve the consistency and adaptability of product quality. By accurately identifying and adjusting abnormal temperatures, the problems of uneven temperature control and unstable finished products caused by delayed response or inaccurate control of traditional temperature control systems are avoided, thereby effectively improving the overall quality and market competitiveness of latex paint.

[0041] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0045] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0046] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A temperature control management system for latex paint production, characterized in that: Includes the following modules: latex paint temperature monitoring module, temperature abnormality module, temperature adjustment module; The latex paint temperature monitoring module is used to monitor the temperature changes of the latex paint in the three stages of polymerization reaction, dispersion mixing and cooling through a temperature sensor, and analyze the temperature fluctuation characteristics through a time series to generate a temperature fluctuation curve; The temperature anomaly module is used to obtain historical temperature data of the latex paint stage, perform spectrum similarity analysis on the temperature fluctuation curve, identify temperature periodic fluctuations and amplitude characteristics, obtain the temperature fluctuation intensity index, and determine whether the temperature of the latex paint production process is abnormal; The temperature adjustment module is used to identify abnormal temperature in the latex paint production stage and dynamically adjust the power of the latex paint temperature control equipment through a PID control algorithm.

2. A temperature control management system for latex paint production according to claim 1, characterized in that: The specific process of analyzing the temperature fluctuation characteristics through time series and generating the temperature fluctuation curve is as follows: The real-time temperature data of the three stages of polymerization reaction, dispersion mixing and cooling are obtained from the latex paint temperature monitoring module, and continuous sampling is performed at set time intervals; De-noise and filter the collected temperature data to remove outliers and noise, and use the autoregressive moving average model to perform time series modeling on the temperature data to identify temperature trends and periodic fluctuations; The autoregressive moving average model is analyzed to extract the characteristics of temperature fluctuations, including the amplitude, period, and frequency of fluctuations, and to construct a temperature fluctuation curve.

3. A temperature control management system for latex paint production according to claim 2, characterized in that: The specific process of performing spectrum similarity analysis on the temperature fluctuation curve and identifying the temperature periodic fluctuation and amplitude characteristics is as follows: The temperature fluctuation curve is converted into frequency domain representation by fast Fourier transform (FFT), and the main frequency components in the spectrum are analyzed to identify the frequency and amplitude of periodic fluctuations; The spectrum characteristics of different stages are compared to extract the periodic change pattern of temperature fluctuation. Based on the peak amplitude analysis of the spectrum, the deviation between the intensity of temperature fluctuation and process requirements is judged, and the periodic fluctuation and amplitude characteristics are classified and marked.

4. A temperature control management system for latex paint production according to claim 3, characterized in that: The specific process of obtaining the temperature fluctuation intensity index is as follows: According to the spectrum analysis results, the amplitude information in the spectrum is extracted and expressed as A(f), where A(f) is the amplitude value at frequency f, indicating the intensity of the frequency component; Calculate the energy distribution of each frequency band in the spectrum, defined as E(f) = A(f) 2 , where E(f) is the energy value at frequency f, which is the square of the amplitude A(f), indicating the contribution of this frequency component to the overall signal energy; Divide the spectrum signal into multiple frequency bands f1, f2, ..., f n , and calculate the total energy E in each frequency band total , the formula is as follows: In the formula, E total Indicates the frequency band [ f1,f n] The total energy within the frequency band reflects the importance of the frequency band in the total energy. n are the starting frequency and ending frequency of the frequency band, respectively, to determine the range of the frequency band. The energy of all frequency components in the frequency band is summed to obtain the total energy of the frequency band; The temperature fluctuation intensity I is obtained by combining the fluctuation intensity of each frequency band. temp , defined as the weighted average of the energy of each frequency band, the formula is as follows: In the formula, E i : The energy of the ith frequency band, reflecting the contribution of the frequency components of this band to the temperature fluctuation, w i : Weight factor, which is used to indicate the relative contribution of the ith frequency band to the overall temperature fluctuation intensity, and is usually adjusted based on the importance of the frequency band. n: Total number of frequency bands, which indicates the number of frequency bands divided in the spectrum analysis; According to the volatility intensity index I temp , the temperature fluctuation intensity index is further normalized to obtain the normalized temperature fluctuation intensity index ξ temp , the formula is as follows: In the formula, ξ temp : Normalized temperature fluctuation intensity index, indicating the intensity of temperature fluctuation, I min : The lowest temperature fluctuation intensity in the historical data, used in the normalization process to ensure that the lower limit of the index is 0, I max : The highest temperature fluctuation intensity in the historical data, used in the normalization process to ensure that the upper limit of the index is 1.

5. A temperature control management system for latex paint production according to claim 4, characterized in that: The specific process of judging whether the temperature in the latex paint production process is abnormal is as follows: Based on the temperature fluctuation intensity index, the temperature fluctuation intensity index is compared with a set temperature fluctuation intensity index threshold. When the temperature fluctuation intensity index exceeds the set threshold, it indicates that the temperature of the latex paint production process is abnormal.

6. A temperature control management system for latex paint production according to claim 5, characterized in that: The specific process of identifying abnormal temperatures during the latex paint production stage is as follows: The temperature fluctuation intensity index of each stage in the latex paint production process is obtained. According to the threshold value of the temperature fluctuation intensity index of each stage in the latex paint production process, it is judged whether the temperature of the latex paint production process is abnormal, and the abnormal temperature in the latex paint production stage is identified.

7. A temperature control management system for latex paint production according to claim 6, characterized in that: The logic of dynamically adjusting the power of latex paint temperature control equipment through PID control algorithm is as follows: Real-time detection of the deviation between the temperature fluctuation intensity index at each stage of the latex paint production process and the set temperature fluctuation intensity index threshold value as the input signal of PID control; According to the size of the deviation, the proportional term is calculated, that is, the product of the current deviation and the proportional gain, reflecting the immediate impact of the current deviation on system control; Calculate the integral term, which is the product of the accumulated value of the historical error and the integral gain, to eliminate long-term errors and gradually correct the temperature; Calculate the differential term, which is the product of the error change rate and the differential gain, to predict future temperature change trends; The proportional, integral and differential calculation results are added together to generate a comprehensive control signal, which indicates the power output of the temperature control device that needs to be adjusted.

8. A temperature control management method for latex paint production, using a temperature control management system for latex paint production according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Monitor the temperature changes of latex paint in the three stages of polymerization, dispersion mixing and cooling through temperature sensors, and analyze the temperature fluctuation characteristics through time series to generate a temperature fluctuation curve; S2. Obtain historical temperature data of the latex paint stage, perform spectrum similarity analysis on the temperature fluctuation curve, identify the periodic temperature fluctuation and amplitude characteristics, obtain the temperature fluctuation intensity index, and determine whether the temperature of the latex paint production process is abnormal; S3. Identify abnormal temperatures during the latex paint production stage and dynamically adjust the power of the latex paint temperature control equipment through the PID control algorithm.

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