A method for monitoring production environment data of methyl methacrylate

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method for monitoring data of a methyl methacrylate production environment; obtaining abnormal change points according to the data difference characteristics of a historical temperature time series; obtaining reaction deposition characteristic values according to the temperature difference characteristics of adjacent abnormal change points; judging whether the abnormal change points are deposition characteristic points according to the reaction deposition characteristic values; obtaining a temperature fluctuation sequence according to all the deposition characteristic points; obtaining a deposition change degree value according to the similarity characteristics between the temperature fluctuation sequence and a preset ascending sequence. The present invention obtains an adaptive lag coefficient weight of the abnormal change points according to the deposition change degree value and the reaction deposition characteristic values; predicts the temperature according to an ARIMA prediction model including the adaptive lag coefficient weight; monitors the production process according to the predicted temperature, avoids the influence of the solid catalyst deposition phenomenon on temperature monitoring, and improves the accuracy of temperature monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically relates to a method for monitoring the production environment data of methyl methacrylate. Background Art

[0002] Methyl methacrylate is an important organic chemical raw material with wide applications. The process route of co-producing methyl acrylate, methyl propionate and methyl methacrylate by condensation-hydrogenation-condensation using methyl acetate and formaldehyde as raw materials is the main method for industrial synthesis of methyl methacrylate. During the industrial production process, the temperature of the reaction process has a great influence on the quality of the synthesis result. Therefore, it is necessary to monitor the temperature in the reaction kettle during the production process to ensure the quality of methyl methacrylate.

[0003] Since the production time of methyl methacrylate is relatively long, the temperature value in the future period can be predicted based on the temperature changes in the historical reaction process. The existing ARIMA time series model can predict the temperature data in the future period through the historical temperature data. The temperature abnormality trend during the production process is judged according to the difference between the predicted temperature and the actual temperature. However, the solid catalyst in the production process of methyl methacrylate is prone to normal deposition and aggregation, resulting in intense heat release in local areas and causing temperature fluctuations. Due to the uncertainty of the deposition and aggregation of the solid catalyst, it is easy to cause deviation in the temperature prediction of the ARIMA prediction model, resulting in a large difference between the predicted temperature and the actual temperature, and ultimately leading to low accuracy in the abnormal temperature monitoring during the production process of methyl methacrylate. Summary of the Invention

[0004] In order to solve the technical problem that the normal deposition and aggregation of the solid catalyst in the production process of methyl methacrylate cause temperature fluctuations and affect the accuracy of abnormal temperature monitoring, the purpose of the present invention is to provide a method for monitoring the production environment data of methyl methacrylate, and the specific technical solution adopted is as follows:

[0005] Obtain the historical temperature time series of the reaction kettle at the current moment during the production process of methyl methacrylate;

[0006] Obtain abnormal change points according to the data difference characteristics between data points and adjacent data points and the difference characteristics between data points and the overall data change trend in the historical temperature time series; obtain the reaction deposition characteristic value according to the temperature difference characteristics between adjacent abnormal change points;

[0007] Determine whether the abnormal change point is a deposition characteristic point according to the reaction deposition characteristic value; if there is a deposition characteristic point, construct a temperature change curve based on all deposition characteristic points, and obtain a temperature fluctuation sequence according to the temperature difference characteristics of the peaks and valleys in the temperature change curve; obtain a deposition change degree value according to the similarity characteristics of the data changes between the temperature fluctuation sequence and a preset ascending sequence;

[0008] Adaptively adjust the original lag coefficient weight of the abnormal change point in the ARIMA prediction model according to the deposition change degree value and the reaction deposition characteristic value to obtain an adaptive lag coefficient weight; predict the temperature at a future moment according to the ARIMA prediction model including the adaptive lag coefficient weight to obtain a predicted temperature; monitor the production process according to the predicted temperature.

[0009] Further, the step of obtaining the abnormal change point according to the data difference characteristics between the data points in the historical temperature time series and adjacent data points and the difference characteristics between the data points and the overall data change trend includes:

[0010] Construct a temperature-time coordinate system for the historical temperature time series, calculate the sum value of the Euclidean distances between any data point in the historical temperature time series and the adjacent data points before and after in the temperature-time coordinate system to obtain the neighborhood difference characteristic value of the any data point; decompose the historical temperature time series in the temperature-time coordinate system to obtain a temperature change trend curve, calculate the Euclidean distance between the any data point and the data point at the same moment in the temperature change trend curve and normalize it to obtain the trend difference characteristic value of the any data point; calculate the reciprocal of the neighborhood difference characteristic value and normalize it to obtain the neighborhood similarity; calculate the average value of the trend difference characteristic value and the neighborhood similarity to obtain the abnormality degree of the any data point;

[0011] When the abnormality degree of the any data point exceeds a preset abnormality threshold, the any data point is an abnormal change point.

[0012] Further, the step of obtaining the reaction deposition characteristic value according to the temperature difference characteristics between adjacent abnormal change points includes:

[0013] Calculate the difference between the temperature of any abnormal change point and the temperature of the next adjacent abnormal change point to obtain a temperature difference; calculate the reciprocal of the absolute value of the sum value of all temperature differences to obtain a temperature stability characteristic value; calculate the ratio of the number of abnormal change points to a preset constant to obtain a quantity characteristic value; calculate the product of the quantity characteristic value and the temperature stability characteristic value to obtain the reaction deposition characteristic value.

[0014] Further, the step of determining whether the abnormal change point is a deposition characteristic point according to the reaction deposition characteristic value includes:

[0015] When the reaction deposition characteristic value exceeds a preset deposition threshold, the abnormal change point is a deposition characteristic point.

[0016] Further, the step of obtaining a temperature fluctuation sequence according to the temperature difference characteristics of the peaks and valleys in the temperature change curve includes:

[0017] Calculate the longitudinal vertical distance between any peak point and the nearest valley point in the future time in the temperature change curve to obtain an amplitude characteristic value; sort all the amplitude characteristic values in chronological order to obtain the temperature fluctuation sequence.

[0018] Further, the step of obtaining a deposition change degree value according to the similarity characteristics of the data change between the temperature fluctuation sequence and a preset ascending sequence includes:

[0019] Calculate the Spearman correlation coefficient between the temperature fluctuation sequence and the preset ascending sequence and perform a positive correlation mapping to obtain the deposition change degree value.

[0020] Further, the step of adaptively adjusting the original lag coefficient weight of the abnormal change point in the ARIMA prediction model according to the deposition change degree value and the reaction deposition characteristic value to obtain an adaptive lag coefficient weight includes:

[0021] When the reaction deposition characteristic value does not exceed the preset deposition threshold, the adaptive lag coefficient weight of the abnormal change point is a constant 0; when the deposition change degree value does not exceed the preset change threshold, calculate the product of the original lag coefficient weight of the abnormal data point and the deposition change degree value to obtain the adaptive lag coefficient weight; when the deposition change degree value exceeds the preset change threshold, calculate the sum value of the deposition change degree value and a preset second constant to obtain an adjustment amount; calculate the product of the adjustment amount and the original lag coefficient weight to obtain the adaptive lag coefficient weight.

[0022] Further, the step of monitoring the production process according to the predicted temperature includes:

[0023] Calculate the absolute value of the difference between the predicted temperature and the actual measured temperature at the same moment to obtain a temperature error value; when the temperature error value exceeds the preset error threshold, an abnormality occurs in the methyl methacrylate production process.

[0024] The present invention has the following beneficial effects:

[0025] In the present invention, obtaining abnormal change points can determine whether there are data points with suspected abnormal temperature trends in the historical temperature time series, and then adaptively adjust the weights of the abnormal change points in the prediction process; since abnormal temperature trends during the production process may be caused by solid catalyst deposition and unstable reactor heating, obtaining reaction deposition characteristic values can characterize the reasons for the occurrence of abnormal change points, further improving the accuracy of setting the weights of abnormal change points and temperature anomaly monitoring. Due to the uncertainty of the deposition phenomenon of solid catalysts during the reaction process, different deposition phenomenon change trends will lead to differences in temperature change trends, so obtaining the temperature fluctuation sequence can characterize the change trend of the deposition phenomenon; obtaining the deposition change degree value can quantify the change trend of the deposition phenomenon, further improving the accuracy of setting the weights of abnormal change points. Obtaining the adaptive lag coefficient weight can change the importance of abnormal change points in the prediction process, so that temperature prediction results caused by different factors can be different. Finally, monitoring based on the predicted temperature can avoid the influence of solid catalyst deposition phenomena on temperature monitoring and improve the accuracy of temperature monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0027] Figure 1 It is a flowchart of a method for monitoring methyl methacrylate production environment data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method for monitoring methyl methacrylate production environment data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0030] The following specifically describes the specific solution of a method for monitoring methyl methacrylate production environment data provided by the present invention with reference to the accompanying drawings.

[0031] Please refer to Figure 1 , which shows a flowchart of a method for monitoring methyl methacrylate production environment data provided by an embodiment of the present invention. The method includes the following steps:

[0032] Step S1, obtain the historical temperature time series of the reaction kettle at the current moment during the production process of methyl methacrylate.

[0033] Using methyl acetate and formaldehyde as raw materials, the process route of co-producing methyl acrylate, methyl propionate and methyl methacrylate through condensation-hydrogenation-condensation is the main method for industrial production of methyl methacrylate. During the condensation process, solid catalysts such as silica are required for catalytic action. The temperature in the reaction kettle during the condensation reaction has an important influence on the reaction result. Therefore, obtain the historical temperature time series of the reaction kettle at the current moment during the production process of methyl methacrylate, and collect the temperature through the thermocouple temperature sensor in the reaction kettle; in the embodiment of the present invention, collect the temperature data in the previous 5 minutes before the current moment during the condensation process as the historical temperature time series at the current moment, and the collection frequency is 1 time per second; as the current moment is updated, the corresponding historical temperature time series will be updated synchronously; the implementer can determine the collection duration and collection frequency by himself.

[0034] Step S2, obtain the abnormal change points according to the data difference characteristics between the data points and the adjacent data points and the difference characteristics between the data points and the overall data change trend in the historical temperature time series; obtain the reaction deposition characteristic value according to the temperature difference characteristics between the adjacent abnormal change points.

[0035] During the condensation reaction process, the control of temperature is crucial. The temperature change at adjacent moments during the reaction process is regular. Therefore, the predicted temperature predicted according to the historical normal temperature data is similar to the actual temperature at the same moment; when there is an abnormal temperature trend, there will be a difference between the result predicted according to the normal temperature data and the actual measurement result, and thus it is possible to judge whether there is an abnormality in the condensation process according to the degree of difference. Since solid catalysts may deposit and aggregate during the condensation reaction process, resulting in intense heat release in a local area of the reaction kettle and an abnormal temperature trend; however, the deposition of solid catalysts is a normal situation during the reaction process, and the temperature will tend to be normal as the reaction proceeds; therefore, during the temperature prediction process, it is necessary to reduce the uncertain influence of the deposition and aggregation of solid catalysts and improve the accuracy of temperature monitoring during the condensation reaction process.

[0036] During the normal condensation reaction process, when the solid catalyst is evenly distributed in the reaction kettle, the temperature monitoring data in the early stage of the reaction shows a stable trend with a small degree of fluctuation; during the reaction progress stage, due to the action of the solid catalyst, the temperature will show a slow upward trend. Therefore, during the normal reaction process, the temperature monitored at any time conforms to the overall temperature trend change. If the temperature monitored at any time has a large difference from the overall temperature trend, an abnormal temperature trend may occur at that moment. Further, individual noise points may appear during the acquisition process. The noise points will also have a large difference from the overall temperature trend change, but the noise points will also have a large difference from the temperature data at the adjacent moments before and after, while the real abnormal temperature trend data points are correlated. Therefore, the abnormal change points can be obtained according to the data difference characteristics between the data points and the adjacent data points in the historical temperature time series and the difference characteristics between the data points and the overall data change trend.

[0037] Preferably, in the embodiments of the present invention, the steps of obtaining the abnormal change points include: constructing a temperature-time coordinate system for the historical temperature time series, calculating the sum of the Euclidean distances between any data point in the historical temperature time series and the adjacent data points before and after in the temperature-time coordinate system, and obtaining the neighborhood difference characteristic value of the any data point; when the neighborhood difference characteristic value is larger, it means that the any data point is more likely to be a noise point and has less correlation with the temperature data at the adjacent moments before and after. Decompose the historical temperature time series in the temperature-time coordinate system to obtain the temperature change trend curve. It should be noted that decomposing the series into time series is a prior art, and the specific steps will not be elaborated. The temperature change trend curve reflects the overall change trend of the temperature time series. Calculate the Euclidean distance between any data point and the data point at the same moment in the temperature change trend curve and normalize it to obtain the trend difference characteristic value of the any data point; when the trend difference characteristic value is larger, it means that the temperature of the any data point has a larger difference from the overall temperature change trend and is more likely to represent an abnormal temperature trend and a noise point. Calculate the reciprocal of the neighborhood difference characteristic value and normalize it to obtain the neighborhood similarity; when the neighborhood similarity is larger, it means that the any data point is less likely to be a noise point. Calculate the average value of the trend difference characteristic value and the neighborhood similarity to obtain the abnormal degree of the any data point; when the abnormal degree of the any data point is larger, it means that the any data point is more likely to represent an abnormal temperature trend. When the abnormal degree of the any data point exceeds the preset abnormal threshold, the any data point is an abnormal change point; in the embodiments of the present invention, the preset abnormal threshold is 0.6. Through this preset abnormal threshold, the abnormal change points suspected of abnormal temperature trends can be better distinguished, and the implementer can determine it according to the implementation scenario; the abnormal change points represent that there is a suspected abnormal temperature trend at that moment during the condensation reaction process. The formula for obtaining the abnormal degree includes:

[0038] Wherein, Q represents the degree of abnormality of the arbitrary data point, D represents the Euclidean distance between the arbitrary data point and the data point at the same moment in the temperature change trend curve, represents normalization, represents the trend difference eigenvalue; represents the Euclidean distance between the arbitrary data point and the previous adjacent data point, represents the Euclidean distance between the arbitrary data point and the next adjacent data point, represents the neighborhood difference eigenvalue; represents the neighborhood similarity.

[0039] Furthermore, the reason for the occurrence of abnormal change points may be that during the stirring of the solid catalyst, deposition and aggregation occur, resulting in intense heat release in a local area; it may also be caused by unstable heating of the reaction kettle. For example, continuous heating leads to a rapid increase in temperature, or the interruption of heating leads to a rapid decrease in temperature, causing the temperature to deviate from the normal temperature change trend. Since the deposition and aggregation of the solid catalyst will gradually dissociate as the reaction progresses and the temperature will tend to the normal level, it is necessary to consider the temperature change caused by the deposition process during the prediction process to make the temperature prediction value closer to the temperature level caused by the deposition situation and avoid false alarms; for the temperature abnormality caused by unstable heating of the reaction kettle, such abnormal data points need to be discarded during the prediction process to make the prediction result closer to the normal temperature level, and thus there is a difference between the prediction value and the actual abnormal temperature value for early warning. Therefore, it is first necessary to determine whether the abnormal change point is caused by the deposition of the solid catalyst. Unstable heating of the reaction kettle will radiate to the entire internal area, and the resulting temperature offset will change continuously in synchronization, such as continuous temperature increase deviation, continuous dimensionality reduction deviation trend term, and the overall abnormal data points will show the characteristics of gradually increasing or decreasing temperature. For the temperature increase caused by the solid catalyst, the temperature in some areas of the reaction kettle increases, and the high temperature will be conducted to other areas due to the solution stirring heat, resulting in a gradual decrease in temperature; as some solid catalysts are deposited again, the temperature will fluctuate again. Therefore, the reaction deposition characteristic value is obtained according to the temperature difference characteristics of adjacent abnormal change points.

[0040] Preferably, in the embodiments of the present invention, the step of obtaining the reaction deposition characteristic value includes: calculating the difference between the temperature of any abnormal change point and the temperature of the next adjacent abnormal change point to obtain a temperature difference; calculating the reciprocal of the absolute value of the sum of all temperature differences to obtain a temperature stability characteristic value; when the temperature stability characteristic value is larger, it means that the absolute value of the sum of all temperature differences is smaller, and the temperature of the abnormal change point shows a more fluctuating trend; when the temperature stability characteristic value is smaller, it means that the temperature of the abnormal change point shows a more single-changing trend, and it is more likely to be caused by unstable heating of the reaction kettle. Calculating the ratio of the number of abnormal change points to a preset constant to obtain a quantity characteristic value; in the embodiments of the present invention, the preset constant is 5, aiming to avoid analysis errors in the temperature stability characteristic value due to a small number of abnormal change points; the implementer can determine it according to the implementation scenario. The larger the quantity characteristic value, the more abnormal change points there are, and the greater the credibility of the temperature stability characteristic value. Calculating the product of the quantity characteristic value and the temperature stability characteristic value to obtain a reaction deposition characteristic value; when the reaction deposition characteristic value is larger, it means that the abnormal change point is more likely to be caused by solid catalyst deposition. The formula for obtaining the reaction deposition characteristic value includes:

[0041]

[0042] In the formula, F represents the reaction deposition characteristic value, N represents the number of abnormal change points, represents the temperature of the nth abnormal change point, represents the temperature of the th abnormal change point, represents the temperature difference, represents the temperature stability characteristic value, a represents the preset constant,

[0043] Step S3, judging whether the abnormal change point is a deposition characteristic point according to the reaction deposition characteristic value; if there is a deposition characteristic point, constructing a temperature change curve according to all deposition characteristic points, and obtaining a temperature fluctuation sequence according to the temperature difference characteristics of the peaks and valleys in the temperature change curve; obtaining a deposition change degree value according to the similarity characteristics of the data changes between the temperature fluctuation sequence and a preset ascending sequence.

[0044] When the reaction deposition eigenvalue is smaller, it means that it is more likely to be an abnormal temperature trend caused by the instability of the reaction kettle. Therefore, it is necessary to determine whether the abnormal change point is a deposition characteristic point according to the reaction deposition eigenvalue. Specifically, when the reaction deposition eigenvalue exceeds the preset deposition threshold, the abnormal change point is a deposition characteristic point. In the embodiment of the present invention, the preset deposition threshold is 0.3, and the implementer can determine it according to the implementation scenario. The deposition characteristic point characterizes the abnormal temperature trend caused by the deposition of solid catalyst in the historical temperature time series. For the abnormal temperature trend caused by the deposition of solid catalyst, it is necessary to further analyze the possible temperature change characteristics, so as to make the prediction result more consistent with the actual temperature and avoid false temperature alarms caused by the deposition situation. Under the continuous stirring action of the reaction kettle at different times, the deposition and aggregation of the catalyst may further expand or gradually dissociate. During the temperature fluctuation process of the deposition characteristic point, if the amplitude of the fluctuation gradually decreases, it means that the temperature rise caused by the deposition of solid catalyst gradually decreases, and the deposited and aggregated solid catalyst is gradually dissociating. Furthermore, the deposited and aggregated part has no influence on temperature monitoring after dissociation, and the weight of the deposition characteristic point needs to be reduced during the prediction process to make the temperature data in the prediction process more normal. When the temperature fluctuation amplitude of the deposition characteristic point gradually increases, it means that the deposition aggregation does not dissociate and more solid catalyst is aggregated. Furthermore, this deposition phenomenon will affect the temperature at future moments. Therefore, the weight of the deposition characteristic point needs to be provided during the prediction process to make the prediction result more able to reflect the deposition characteristic and avoid large deviations between the predicted temperature and the actual temperature, resulting in false alarms. Therefore, if there are deposition characteristic points, a temperature change curve is constructed according to all the deposition characteristic points, and a temperature fluctuation sequence is obtained according to the temperature difference characteristics of the peaks and valleys in the temperature change curve. Preferably, in the embodiment of the present invention, the step of obtaining the temperature fluctuation sequence includes: calculating the longitudinal vertical distance between any peak point in the temperature change curve and the nearest valley point in the future time to obtain the amplitude characteristic value; the amplitude characteristic value reflects the temperature difference between the peak point and the nearest valley point in the future time. When the amplitude characteristic value is larger, it means that the temperature change is more obvious. Sort all the amplitude characteristic values in chronological order to obtain the temperature fluctuation sequence.

[0045] Further, when the amplitude eigenvalue in the temperature fluctuation sequence shows an increasing trend, it means that the deposition phenomenon of the solid catalyst is gradually becoming severe, and the subsequent temperature is easily affected by the deposition and aggregation of the catalyst; conversely, when the amplitude eigenvalue in the temperature fluctuation sequence does not show an increasing trend, it means that the deposition phenomenon is gradually dissociating, and the subsequent temperature will not be affected by the deposition and aggregation of the catalyst. Therefore, the deposition change degree value is obtained according to the similar characteristics of the data change between the temperature fluctuation sequence and the preset ascending sequence; preferably, in the embodiment of the present invention, the steps of obtaining the deposition change degree value include: calculating the Spearman correlation coefficient between the temperature fluctuation sequence and the preset ascending sequence and performing a positive correlation mapping to obtain the deposition change degree value; in the embodiment of the present invention, the preset ascending sequence is , when the data change trends of the temperature fluctuation sequence and the preset ascending sequence are more similar, the Spearman correlation coefficient is closer to 1, and when the data change trends are more opposite, the Spearman correlation coefficient is closer to -1. It should be noted that the calculation of the Spearman correlation coefficient belongs to the prior art, and the specific steps will not be elaborated. After the positive correlation mapping, the value range of the deposition change degree value is ; when the deposition change degree value is larger, it means that the deposition and aggregation phenomenon is more severe, and the subsequent temperature is more easily affected by the deposition phenomenon.

[0046] Step S4, adaptively adjust the original lag coefficient weight of the abnormal change point in the ARIMA prediction model according to the deposition change degree value and the reaction deposition eigenvalue to obtain the adaptive lag coefficient weight; predict the temperature at the future moment according to the ARIMA prediction model including the adaptive lag coefficient weight to obtain the predicted temperature; monitor the production process according to the predicted temperature.

[0047] After obtaining the reaction deposition eigenvalue and the deposition change degree value, the original lag coefficient weight of the abnormal change point in the ARIMA prediction model can be adaptively adjusted according to the deposition change degree value and the reaction deposition eigenvalue to obtain the adaptive lag coefficient weight; preferably, in the embodiment of the present invention, the steps of obtaining the adaptive lag coefficient weight include: when the reaction deposition eigenvalue does not exceed the preset deposition threshold, the adaptive lag coefficient weight of the abnormal change point is a constant 0; it means that the heating of the reaction kettle may be unstable. Therefore, the adaptive lag coefficient weight of the abnormal change point is set to 0, so that the abnormal change point does not participate in the temperature prediction of the ARIMA at the future moment. Furthermore, the prediction result is close to the temperature characteristic when the temperature trend changes normally, while the actual temperature value at the future moment is affected by the unstable heating of the reaction kettle, so there is a large difference between the predicted temperature and the actual temperature, and the temperature abnormality during the condensation process is monitored.

[0048] Further, when the deposition change degree value does not exceed the preset change threshold, it means that the deposition phenomenon is gradually dissociating, and the subsequent temperature is not affected by the deposition. Therefore, it is necessary to reduce the hysteresis coefficient weight of the deposition characteristic point to reduce the influence of the deposition characteristic point on the prediction result. In the embodiment of the present invention, the preset change threshold is 0.5, and the implementer can determine it according to the implementation scenario. Then, calculate the product of the original hysteresis coefficient weight of the abnormal data point and the deposition change degree value to obtain the adaptive hysteresis coefficient weight. The original linear coefficient weights of all data points in the historical temperature time series are all 1. The adaptive hysteresis coefficient weight of such deposition characteristic points is less than the constant 1, reducing the influence on the prediction result and making the predicted temperature value closer to the actual temperature value. When the deposition change degree value exceeds the preset change threshold, it means that the deposition phenomenon increases, and the temperature at the future moment will continue to be affected by the deposition of the solid catalyst. Therefore, it is necessary to increase the importance of the deposition characteristic point in the prediction process. Calculate the sum value of the deposition change degree value and the preset second constant to obtain the adjustment amount. In the embodiment of the present invention, the preset second constant is 1, and the implementer can determine it according to the implementation scenario, aiming to make the adaptive hysteresis coefficient weight exceed the original value. Calculate the product of the adjustment amount and the original hysteresis coefficient weight to obtain the adaptive hysteresis coefficient weight. The adaptive hysteresis coefficient weight of such deposition characteristic points exceeds the original hysteresis coefficient weight. Therefore, the temperature characteristics of the deposition characteristic points are more considered in the prediction process, making the prediction result closer to the temperature level caused by the deposition situation, and thus making the prediction result closer to the actual value. It avoids the situation where the prediction result deviates greatly from the actual value due to the deposition phenomenon and is considered a temperature anomaly.

[0049] Further, the temperature at the future moment can be predicted according to the ARIMA prediction model including the adaptive hysteresis coefficient weight to obtain the predicted temperature. It should be noted that the ARIMA prediction model belongs to the prior art, and the specific prediction steps will not be elaborated. In the embodiment of the present invention, the temperature value 10 seconds in the future at the current moment is predicted. Finally, monitor the production process according to the predicted temperature. Calculate the absolute value of the difference between the predicted temperature and the actual measured temperature at the same moment to obtain the temperature error value. When the temperature error value exceeds the preset error threshold, an abnormality occurs in the methyl methacrylate production process. The implementer can set the preset error threshold according to the implementation scenario. In the embodiment of the present invention, the preset error threshold is 10 degrees Celsius.

[0050] In summary, the embodiment of the present invention provides a method for monitoring the production environment data of methyl methacrylate; obtaining abnormal change points according to the data difference characteristics of the historical temperature time series; obtaining reaction deposition characteristic values according to the temperature difference characteristics of adjacent abnormal change points; judging whether the abnormal change points are deposition characteristic points according to the reaction deposition characteristic values; obtaining a temperature fluctuation sequence according to all the deposition characteristic points; and obtaining a deposition change degree value according to the similarity characteristics between the temperature fluctuation sequence and a preset ascending sequence. The present invention obtains an adaptive lag coefficient weight of the abnormal change points according to the deposition change degree value and the reaction deposition characteristic values; predicts the temperature according to the ARIMA prediction model including the adaptive lag coefficient weight; and monitors the production process according to the predicted temperature, avoiding the influence of the solid catalyst deposition phenomenon on temperature monitoring and improving the accuracy of temperature monitoring.

[0051] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] Each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring methyl methacrylate production environment data, characterized in that: The method comprises the following steps: Get the historical temperature time series of the reactor at the current moment in the production process of methyl methacrylate; Obtain an abnormal change point according to the data difference characteristics between the data point and the adjacent data points in the historical temperature time series, and the difference characteristics between the data point and the overall data change trend; obtain a reaction deposition characteristic value according to the temperature difference characteristics of the adjacent abnormal change points; According to the reaction deposition characteristic value, determine whether the abnormal change point is a deposition characteristic point; if there is a deposition characteristic point, construct a temperature change curve according to all deposition characteristic points, and obtain a temperature fluctuation sequence according to the temperature difference characteristics of the peaks and valleys in the temperature change curve; obtain a deposition change degree value according to the similarity characteristics of the temperature fluctuation sequence and the data change of the preset ascending sequence; The original lag coefficient weight of the abnormal change point in the ARIMA prediction model is adaptively adjusted according to the deposition change degree value and the reaction deposition characteristic value to obtain an adaptive lag coefficient weight; the temperature at a future moment is predicted according to the ARIMA prediction model including the adaptive lag coefficient weight to obtain a predicted temperature; and the production process is monitored according to the predicted temperature.

2. A method for monitoring methyl methacrylate production environment data according to claim 1, characterized in that: The step of obtaining abnormal change points according to the data difference characteristics between the data point and the adjacent data points in the historical temperature time series and the difference characteristics between the data point and the overall data change trend includes: Constructing a temperature-time coordinate system of the historical temperature time series, calculating the sum of the Euclidean distances between any data point in the historical temperature time series and the adjacent data points before and after in the temperature-time coordinate system, and obtaining a neighborhood difference characteristic value of the arbitrary data point; performing time series decomposition on the historical temperature time series in the temperature-time coordinate system, obtaining a temperature change trend curve, calculating and normalizing the Euclidean distance between the arbitrary data point and the data point at the same time in the temperature change trend curve, and obtaining a trend difference characteristic value of the arbitrary data point; calculating and normalizing the inverse of the neighborhood difference characteristic value, and obtaining a neighborhood similarity; calculating the average value of the trend difference characteristic value and the neighborhood similarity, and obtaining the degree of abnormality of the arbitrary data point; When the abnormal degree of the arbitrary data point exceeds a preset abnormal threshold, the arbitrary data point is an abnormal change point.

3. A method for monitoring methyl methacrylate production environment data according to claim 1, characterized in that: The step of obtaining the reaction deposition characteristic value according to the temperature difference characteristics of adjacent abnormal change points comprises: Calculate the temperature difference between any abnormal change point and the next adjacent abnormal change point to obtain the temperature difference; calculate the inverse of the absolute value of the sum of all temperature differences to obtain the temperature stability characteristic value; calculate the ratio of the number of abnormal change points to a preset constant to obtain the quantity characteristic value; calculate the product of the quantity characteristic value and the temperature stability characteristic value to obtain the reaction deposition characteristic value.

4. A method for monitoring methyl methacrylate production environment data according to claim 1, characterized in that: The step of judging whether the abnormal change point is a deposition feature point according to the reaction deposition feature value comprises: When the reaction deposition characteristic value exceeds a preset deposition threshold, the abnormal change point is a deposition characteristic point.

5. A method for monitoring methyl methacrylate production environment data according to claim 1, characterized in that: The step of obtaining a temperature fluctuation sequence according to the temperature difference characteristics of the peaks and valleys in the temperature change curve comprises: The longitudinal vertical distance between any peak point in the temperature change curve and the nearest trough point in the future time is calculated to obtain the amplitude characteristic value; all the amplitude characteristic values ​​are sorted in chronological order to obtain the temperature fluctuation sequence.

6. A method for monitoring methyl methacrylate production environment data according to claim 1, characterized in that: The step of obtaining the deposition variation value according to the similar characteristics of the data variation of the temperature fluctuation sequence and the preset ascending sequence comprises: The Spearman correlation coefficient between the temperature fluctuation sequence and the preset ascending sequence is calculated and positively correlated to obtain the deposition change degree value.

7. A method for monitoring methyl methacrylate production environment data according to claim 4, characterized in that: The step of adaptively adjusting the original hysteresis coefficient weight of the abnormal change point in the ARIMA prediction model according to the deposition change degree value and the reaction deposition characteristic value to obtain the adaptive hysteresis coefficient weight comprises: When the reaction deposition characteristic value does not exceed the preset deposition threshold, the adaptive hysteresis coefficient weight of the abnormal change point is a constant of 0; when the deposition change degree value does not exceed the preset change threshold, the product of the original hysteresis coefficient weight of the abnormal data point and the deposition change degree value is calculated to obtain the adaptive hysteresis coefficient weight; when the deposition change degree value exceeds the preset change threshold, the sum of the deposition change degree value and a preset second constant is calculated to obtain the adjustment amount; the product of the adjustment amount and the original hysteresis coefficient weight is calculated to obtain the adaptive hysteresis coefficient weight.

8. A method for monitoring methyl methacrylate production environment data according to claim 1, characterized in that: The step of monitoring the production process according to the predicted temperature comprises: The absolute value of the difference between the predicted temperature and the actual measured temperature at the same time is calculated to obtain a temperature error value; when the temperature error value exceeds a preset error threshold, an abnormality occurs in the methyl methacrylate production process.

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