Digital chemical production information data management method, platform and system

Abstract

The invention relates to the field of temperature risk early warning, in particular to a digital chemical production information data management method, platform and system. The method comprises the following steps: firstly, acquiring actual temperature data of each production flow at each historical moment before the current moment, predicting temperatures of a target production flow at the historical moment and the current moment, and calculating the temperature of the target production flow based on the difference between the actual temperature data and the predicted temperature data of the target production flow at the historical moment; adjusting the predicted temperature data at the current moment to obtain an adjusted predicted temperature, and adjusting the standard temperature range of the target production process according to the difference between the adjusted predicted temperature of the target production process and the adjusted predicted temperature of the subsequent production process at the current moment and the position of the target production process to obtain a risk assessment value at the current moment. And carrying out risk early warning on the temperature of the target production process at the current moment. According to the invention, the accuracy of predicting the chemical production temperature can be improved, and the abnormal temperature of the production process can be early warned in time.

Description

Technical Field

[0001] The present invention relates to the field of temperature risk early warning, and particularly to a digital chemical production information data management method, platform and system. Background Art

[0002] The chemical production process needs to be carried out under strict environmental control through complex chemical reactions to ensure the production of chemical products with high purity. Among them, temperature is an important environmental factor in chemical production, which affects the activity and reaction efficiency of catalysts during the chemical reaction process, and even determines the purity of reaction products, etc. Therefore, in such chemical production processes, the monitoring and management of temperature data are very important.

[0003] In order to ensure timely early warning of temperature anomalies in each process during chemical production, in the related art, historical temperature data is usually used to predict future temperature data, and by comparing the predicted data with the temperature range, it is judged whether a temperature anomaly early warning needs to be made. However, since the existing method only simply uses the historical temperature data itself for prediction, the endothermic and exothermic phenomena during the chemical reaction process, which also cause temperature changes, are not considered during the prediction process, resulting in low prediction accuracy. At the same time, chemical production usually includes multiple interconnected production processes, and the importance of different production processes in chemical production and the sensitivity requirements for temperature early warning are different, resulting in the inability of the existing method to timely early warn of temperature anomalies in different production processes. Summary of the Invention

[0004] In order to solve the technical problem that the existing method cannot timely early warn of temperature anomalies in different production processes, the purpose of the present invention is to provide a digital chemical production information data management method, platform and system, and the specific technical solutions adopted are as follows: The present invention proposes a digital chemical production information data management method, and the method includes: Obtain the actual temperature data of each production process at each historical moment within a preset time period before the current moment; Take any one production process as the target production process, and predict the predicted temperature data of the target production process at the historical moment and the current moment according to the actual temperature data of each historical moment of the target production process; according to the difference between the actual temperature data and the predicted temperature data of the target production process at the historical moment, adjust the predicted temperature data of the target production process at the current moment to obtain the adjusted predicted temperature of the target production process at the current moment; Take other production processes adjacent to and after the target production process as the successor production processes of the target production process. Obtain the warning sensitivity of the target production process at the current moment according to the difference in the adjusted predicted temperature between the target production process and each successor production process at the current moment, and the position of the target production process among all production processes. Adjust the standard temperature range of the target production process according to the warning sensitivity, and combine the adjusted predicted temperature of the target production process at the current moment to obtain the risk assessment value of the target production process at the current moment. Based on the risk assessment value, conduct a risk warning on the temperature of the target production process at the current moment.

[0005] Furthermore, the predicted temperature data of the target production process at historical moments and the current moment includes: Within a preset time period, take other historical moments except for the first preset number of historical moments as the historical moments to be predicted. Take the current moment or any one of the historical moments to be predicted as the target moment to be predicted. Conduct curve fitting on the actual temperature data of the target production process at all moments before the target moment to be predicted to obtain the fitting function of the target production process at the target moment to be predicted. Input the target moment to be predicted into the fitting function and output the predicted temperature data of the target production process at the target moment to be predicted.

[0006] Furthermore, obtaining the adjusted predicted temperature of the target production process at the current moment includes: Take the difference between the actual temperature data and the predicted temperature data of the target production process at each historical moment to be predicted as the temperature prediction deviation value of the target production process at each historical moment to be predicted. Take the serial number value corresponding to each historical moment to be predicted as the numerator, and the cumulative value of the serial number values corresponding to all historical moments to be predicted as the denominator, and take the ratio as the reference weight of each historical moment to be predicted. Use the reference weights of each historical moment to be predicted to perform weighted summation on the temperature prediction deviation values of the target production process at each historical moment to be predicted, and obtain the temperature adjustment amount of the target production process at the current moment. Take the sum of the predicted temperature data and the temperature adjustment amount of the target production process at the current moment as the adjusted predicted temperature of the target production process at the current moment.

[0007] Furthermore, obtaining the warning sensitivity of the target production process at the current moment includes: Perform a negative correlation mapping on the absolute value of the difference between the adjusted predicted temperature at the current moment between the target production process and each subsequent production process, to obtain the temperature similarity at the current moment between the target production process and each subsequent production process; Take the average value of the temperature similarities at the current moment between the target production process and all subsequent production processes as the temperature importance of the target production process at the current moment; Use the number of all other production processes after the target production process as the numerator, and use the sum of the number of all other production processes before the target production process and a preset adjustment parameter as the denominator, and take the ratio as the position parameter of the target production process; Perform comprehensive processing on the temperature importance and the position parameter and then perform normalization processing to obtain the warning sensitivity of the target production process at the current moment.

[0008] Further, the obtaining of the risk assessment value of the target production process at the current moment includes: According to the warning sensitivity of the target production process at the current moment, narrow the standard temperature range of the target production process to obtain the adjusted temperature range of the target production process at the current moment; When the adjusted predicted temperature of the target production process at the current moment belongs to the adjusted temperature range, then set the risk assessment value of the target production process at the current moment to the numerical value 0; When the adjusted predicted temperature of the target production process at the current moment is greater than the upper limit value of the adjusted temperature range, then obtain the risk assessment value of the target production process at the current moment according to the difference between the adjusted predicted temperature of the target production process at the current moment and the upper limit value of the adjusted temperature range; When the adjusted predicted temperature of the target production process at the current moment is less than the lower limit value of the adjusted temperature range, then obtain the risk assessment value of the target production process at the current moment according to the difference between the adjusted predicted temperature of the target production process at the current moment and the lower limit value of the adjusted temperature range.

[0009] Further, the obtaining of the adjusted temperature range of the target production process at the current moment includes: Take half of the product value of the warning sensitivity of the target production process at the current moment and the length of the standard temperature range of the target production process as the endpoint adjustment amount of the standard temperature range of the target production process at the current moment; Obtain the adjusted temperature range of the target production process at the current moment, where the lower limit value of the adjusted temperature range is equal to the sum value of the lower limit value of the standard temperature range of the target production process and the endpoint adjustment amount, and the upper limit value of the adjusted temperature range is equal to the difference value between the upper limit value of the standard temperature range of the target production process and the endpoint adjustment amount.

[0010] Further, the risk warning for the temperature of the target production process at the current moment includes: If the risk assessment value of the target production process at the current moment is equal to the value 0, no risk warning is issued; If the risk assessment value of the target production process at the current moment is greater than the value 0, different levels of risk warnings are issued for the target production process according to other production processes adjacent to the target production process and the risk assessment value of the target production process at the current moment.

[0011] Further, the issuance of different levels of risk warnings for the target production process includes: Normalize the average value of the sum of all other production processes adjacent to the target production process and the risk assessment value of the target production process at the current moment to obtain the warning level coefficient of the target production process at the current moment; If the warning level coefficient is less than a preset first threshold, a first-level risk warning is issued. If the warning level coefficient is not less than the preset first threshold and less than the preset second threshold, a second-level risk warning is issued. If the warning level coefficient is not less than the preset second threshold, a third-level risk warning is issued, where the severity of the first-level risk warning, the second-level risk warning, and the third-level risk warning gradually increases.

[0012] The present invention also proposes a digital chemical production information data management platform, and the management platform includes: A data acquisition module for obtaining the actual temperature data of each production process at each historical moment within a preset time period before the current moment; A temperature prediction module for taking any production process as the target production process, predicting the predicted temperature data of the target production process at the historical moment and the current moment according to the actual temperature data of each historical moment of the target production process; adjusting the predicted temperature data of the target production process at the current moment according to the difference between the actual temperature data of the target production process at the historical moment and the predicted temperature data to obtain the adjusted predicted temperature of the target production process at the current moment; A risk assessment module for taking other production processes adjacent to the target production process and after the target production process as the successor production processes of the target production process, obtaining the warning sensitivity of the target production process at the current moment according to the difference between the adjusted predicted temperature of the target production process and each successor production process at the current moment and the position of the target production process among all production processes; adjusting the standard temperature range of the target production process according to the warning sensitivity, and combining the adjusted predicted temperature of the target production process at the current moment to obtain the risk assessment value of the target production process at the current moment; A temperature warning module for performing risk warnings on the temperatures of each production process at the current moment based on the risk assessment value.

[0013] The present invention also provides a digital chemical production information data management system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the digital chemical production information data management methods are implemented.

[0014] The present invention has the following beneficial effects: Considering that the existing methods cannot give timely warnings about temperature anomalies in different production processes, the present invention first obtains the actual temperature data of each production process at each historical moment within a preset time period before the current moment, and then predicts the predicted temperature data of the target production process at the historical moment and the current moment. Considering that the endothermic and exothermic phenomena in the actual chemical reaction process will cause temperature changes, there is a large deviation between the prediction result obtained only based on the temperature data at the historical moment itself and the actual temperature. Therefore, by analyzing the deviation between the actual temperature and the predicted temperature through the difference between the actual temperature data and the predicted temperature data of the target production process at the historical moment, the predicted temperature data of the target production process at the current moment is adjusted, so that the obtained adjusted predicted temperature is closer to the actual temperature, improving the accuracy of the prediction result to ensure that timely warnings can be made subsequently. At the same time, chemical production usually includes multiple interconnected production processes, and the importance of different production processes in chemical production and the sensitivity requirements for temperature warnings are different. Therefore, the sensitivity of temperature anomaly warnings for the target production process at the current moment is reflected by the obtained warning sensitivity, and then a timely warning is made about the temperature situation of the target production process at the current moment through the obtained risk assessment value. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a digital chemical production information data management method provided by an embodiment of the present invention; Figure 2 It is a framework diagram of a digital chemical production information data management platform provided by an embodiment of the present invention; Figure 3Schematic diagram for comparing the temperature prediction effect in a catalytic reaction process provided by an embodiment of the present invention; Figure 4 Schematic diagram for analyzing the correlation between sensitivity and temperature deviation provided by an embodiment of the present invention; Figure 5 Schematic diagram for the relationship between the dynamic threshold interval and the risk level provided by an embodiment of the present invention. Detailed implementation manners

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a digital chemical production information data management method, platform and system proposed according to the present invention. In the following description, different "an 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.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solutions of a digital chemical production information data management method, platform and system provided by the present invention with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a flowchart of a digital chemical production information data management method provided by an embodiment of the present invention. The method includes: Step S1: Obtain the actual temperature data of each production process at each historical moment within a preset time period before the current moment.

[0021] Since multiple production processes are usually involved in the chemical production process and different production processes have different temperature requirements, in the embodiments of the present invention, temperature sensors are first deployed in the environments where each production process in the chemical production is located, and the actual temperature data of each production process at each historical moment within a preset time period before the current moment is collected by using the temperature sensors. Among them, the actual temperature data at the current moment is unknown, and the temperature at the current moment needs to be predicted and judged whether to give an early warning later. The time interval for data collection is set to 5 - 10 seconds, and the preset time period is usually 1 - 2 hours. In an embodiment of the present invention, the time interval for data collection is set to 10 seconds, and the preset time period is set to 1 hour. The specific values of the data collection time interval and the preset time period can also be set by the implementer according to the specific implementation scenario, and are not limited herein.

[0022] It should be noted that each production process in chemical production is interconnected, and in actual chemical production, each production process operates simultaneously. For any production process except the first and the last production processes, there are multiple adjacent other production processes after it and multiple adjacent other production processes before it. Here, adjacent means directly connected. For example, if the adjacent production processes before production process C are A and B, and the adjacent production processes after production process C are D and E, then after production processes A and B are completed, they directly enter production process C, and after production process C is completed, it directly enters production processes D and E.

[0023] Step S2: Take any production process as the target production process, and based on the actual temperature data of the target production process at each historical moment, predict the predicted temperature data of the target production process at the historical moment and the current moment; according to the difference between the actual temperature data and the predicted temperature data of the target production process at the historical moment, adjust the predicted temperature data of the target production process at the current moment to obtain the adjusted predicted temperature of the target production process at the current moment.

[0024] Since different production processes have different requirements for temperature control, first analyze any production process and take any production process as the target production process. In order to give a timely warning of the temperature anomaly of the target production process, it is necessary to predict the temperature of the target production process at the current moment. The change of the actual temperature data of the target production process over time can show its change trend in the next period of time. At the same time, due to the endothermic and exothermic phenomena in the actual chemical reaction process, which will cause temperature changes, the prediction result obtained only based on the temperature data at the historical moment itself has a large deviation from the actual temperature. Therefore, in the embodiments of the present invention, it is also necessary to predict the temperature of the target production process at the historical moment, so as to obtain the predicted temperature data of the target production process at the historical moment and the current moment. Subsequently, the predicted temperature data at the current moment can be adjusted by combining the difference between the predicted temperature data and the actual temperature data of the target production process at the historical moment, so as to improve the accuracy of the temperature prediction of the target production process at the current moment, and facilitate the subsequent timely warning of the temperature anomaly phenomenon of the target production process at the current moment.

[0025] Preferably, in an embodiment of the present invention, the method for obtaining the predicted temperature data of the target production process at the historical moment and the current moment specifically includes: To ensure the reliability of the temperature prediction for the target production process at historical moments, it is necessary to reserve a sufficient amount of actual temperature data. Therefore, within a preset time period, other historical moments except for the first preset number of historical moments are used as the historical moments to be predicted. Subsequently, the temperatures of the target production process at the historical moments to be predicted and the current moment can be predicted. Among them, the preset number is generally 20 - 50. In an embodiment of the present invention, the preset number is set to 30. The specific value of the preset number can also be set by the implementer according to the specific implementation scenario and is not limited herein.

[0026] Taking the current moment or any one of the historical moments to be predicted as the target moment to be predicted, curve fitting is performed on the actual temperature data of all moments before the target moment to be predicted for the target production process, and a fitting function of the target production process at the target moment to be predicted is obtained. This fitting function is the function corresponding to the fitting curve and is a function of time. Therefore, the target moment to be predicted can be input into the fitting function to output the predicted temperature data of the target production process at the target moment to be predicted. Among them, in the embodiment of the present invention, the least squares method or other methods can be selected for curve fitting, which is not limited herein.

[0027] In other embodiments of the present invention, a time series prediction algorithm can also be used for temperature prediction. For example, an Autoregressive Integrated Moving Average (ARIMA) or an exponential smoothing algorithm can be selected, etc., which is not limited herein.

[0028] Through the above - mentioned same method, the predicted temperature data of the target production process at each historical moment to be predicted and the current moment can be obtained. From the above analysis, it can be seen that there is a deviation between the prediction result based only on the actual temperature data itself and the actual temperature data. The reason for this deviation is that the endothermic and exothermic phenomena in the actual chemical production process will cause temperature changes. Therefore, in the embodiment of the present invention, based on the difference between the actual temperature data and the predicted temperature data of the target production process at historical moments, the deviation between the predicted temperature data and the actual temperature data under the influence of endothermic and exothermic in the actual chemical production process is analyzed, and based on this, the predicted temperature data of the target production process at the current moment is adjusted to obtain the adjusted predicted temperature of the target production process at the current moment, improving the accuracy of the temperature prediction of the target production process at the current moment and facilitating subsequent timely warning.

[0029] Preferably, in an embodiment of the present invention, the method for obtaining the adjusted predicted temperature of the target production process at the current moment specifically includes: The difference between the actual temperature data and the predicted temperature data of the target production process at each historical moment to be predicted is used as the temperature prediction deviation value of the target production process at each historical moment to be predicted.

[0030] Considering that in the process of adjusting the predicted temperature data at the current moment, the reference value of the temperature prediction deviation value of the historical moment to be predicted closer to the current moment is greater. Therefore, the serial number value corresponding to each historical moment to be predicted can be used as the numerator, and the cumulative value of the serial number values corresponding to all historical moments to be predicted can be used as the denominator. The ratio is used as the reference weight of each historical moment to be predicted, and the reference weights of each historical moment to be predicted are used to perform weighted summation on the temperature prediction deviation values of the target production process at each historical moment to be predicted, so as to obtain the temperature adjustment amount of the target production process at the current moment.

[0031] Furthermore, the sum of the predicted temperature data and the temperature adjustment amount of the target production process at the current moment is used as the adjusted predicted temperature of the target production process at the current moment.

[0032] As an example, in an embodiment of the present invention, the expression of the adjusted predicted temperature of the target production process at the current moment can be specifically, for example: Wherein, represents the adjusted predicted temperature of the target production process at the current moment; represents the predicted temperature data of the target production process at the current moment; represents the th reference weight of the historical moment to be predicted; represents the actual temperature data of the target production process at the th historical moment to be predicted; represents the predicted temperature data of the target production process at the th historical moment to be predicted; represents the temperature prediction deviation value of the target production process at the th historical moment to be predicted; represents the number of historical moments to be predicted; represents the temperature adjustment amount of the target production process at the current moment; represents the th serial number value corresponding to the historical moment to be predicted; represents the th serial number value corresponding to the historical moment to be predicted; represents the cumulative value of the serial number values corresponding to all historical moments to be predicted.

[0033] By using the same method as described above, the adjusted predicted temperature of each production process at the current moment can be obtained.

[0034] Step S3: Take the other production processes that are adjacent to the target production process and are after the target production process as the successor production processes of the target production process. According to the difference in the adjusted predicted temperature at the current moment between the target production process and each successor production process, and the position of the target production process among all production processes, obtain the warning sensitivity of the target production process at the current moment; according to the warning sensitivity, adjust the standard temperature range of the target production process, and combine with the adjusted predicted temperature of the target production process at the current moment to obtain the risk assessment value of the target production process at the current moment.

[0035] For the target production process, there may be multiple production processes adjacent to it after it. When there is a problem with the temperature control of the target production process, it will directly affect the chemical production quality of the adjacent production processes after it. Therefore, in the embodiments of the present invention, first, take the other production processes that are adjacent to the target production process and are after the target production process as the successor production processes of the target production process. The closer the adjusted predicted temperature at the current moment between the target production process and the successor production processes is, the more serious the impact on the successor production processes when the temperature of the target production process is abnormal, and thus a more strict and sensitive temperature abnormality warning needs to be carried out for the target production process. At the same time, when the position of the target production process among all production processes in the entire chemical production is more forward, it indicates that when the temperature of the target production process is abnormal, the impact on the entire chemical production is more serious, and thus a more strict and sensitive temperature abnormality warning needs to be carried out for the target production process. Therefore, according to the difference in the adjusted predicted temperature at the current moment between the target production process and each successor production process, and the position of the target production process among all production processes, obtain the warning sensitivity of the target production process at the current moment, and use the warning sensitivity to reflect the sensitivity of the temperature abnormality warning that needs to be carried out for the target production process at the current moment. Subsequently, the standard temperature range of the target production process can be adjusted through the warning sensitivity to ensure that a timely warning can be made when the temperature of the target production process is abnormal at the current moment.

[0036] Preferably, in an embodiment of the present invention, the method for obtaining the warning sensitivity of the target production process at the current moment specifically includes: Perform a negative correlation mapping on the absolute value of the difference between the adjusted predicted temperature at the current moment between the target production process and each subsequent production process to obtain the temperature similarity between the target production process and each subsequent production process at the current moment. The greater the temperature similarity, the closer the adjusted predicted temperature between the target production process and each subsequent production process at the current moment. Furthermore, the average value of the temperature similarities between the target production process and all subsequent production processes at the current moment can be used as the temperature importance of the target production process at the current moment. The greater the temperature importance, the greater the impact on the production quality of the subsequent production processes when the temperature of the target production process is abnormal at the current moment. Furthermore, it indicates that a more sensitive temperature anomaly warning for the target production process is more needed.

[0037] It should be noted that for the last production process, there are no adjacent other production processes after it, that is, there are no subsequent production processes for the last production process. Therefore, the temperature importance of the last production process can be set to the value 0 to ensure the smooth progress of subsequent calculations.

[0038] Use the number of all other production processes after the target production process as the numerator, and use the sum of the number of all other production processes before the target production process and the preset adjustment parameter as the denominator, and use the ratio as the position parameter of the target production process. The greater the position parameter, the more forward the position of the target production process, and further indicates that a more sensitive temperature anomaly warning for the target production process is more needed.

[0039] Furthermore, after comprehensively considering the temperature importance and the position parameter and performing a normalization process, limit the calculation result within the range to obtain the warning sensitivity of the target production process at the current moment.

[0040] In the embodiments of the present invention, the comprehensive consideration of the two can be achieved by calculating the sum value or the product value of the temperature importance and the position parameter, and this is not limited herein.

[0041] As an example, in an embodiment of the present invention, the expression of the warning sensitivity of the target production process at the current moment can be specifically, for example: Among them, represents the warning sensitivity of the target production process at the current moment; represents the number of all other production processes after the target production process; represents the number of all other production processes before the target production process; represents the preset adjustment parameter, which is used to prevent the denominator from being 0, The value range of , in an embodiment of the present invention, Set to 0.01, The specific value of can also be set by the implementer according to the specific implementation scenario and is not limited herein; represents the position parameter of the target production process; represents the adjusted predicted temperature of the target production process at the current moment; represents the th successor production process of the target production process at the current moment's adjusted predicted temperature; represents the temperature similarity between the target production process and the th successor production process at the current moment; represents a preset adjustment coefficient used to prevent the denominator from being 0, The value range of is , in an embodiment of the present invention, is set to 0.001, The specific value of can also be set by the implementer according to the specific implementation scenario and is not limited herein; represents the temperature importance of the target production process at the current moment; represents the number of successor production processes of the target production process; represents the hyperbolic tangent function used for normalization processing. In other embodiments of the present invention, other functions such as activation functions can also be used for normalization processing and are not limited herein. It should be noted that in other embodiments of the present invention, negative correlation mapping can also be achieved through other basic mathematical operations, which will not be elaborated herein.

[0042] It should be noted that in other embodiments of the present invention, negative correlation mapping can also be achieved through other basic mathematical operations, which will not be elaborated herein.

[0043] The greater the early warning sensitivity of the target production process at the current moment, the more it indicates that the target production process needs to be more sensitive to abnormal temperature early warning at the current moment. Therefore, based on the early warning sensitivity of the target production process at the current moment, the standard temperature range of the target production process can be adjusted, and combined with the adjusted predicted temperature of the target production process at the current moment, the risk assessment value of the target production process at the current moment can be obtained. Subsequently, based on the risk assessment value, it can be determined whether it is necessary to perform temperature abnormal early warning on the target production process at the current moment to improve the timeliness of temperature early warning. Among them, the standard temperature range of the production process is a known range to ensure that the temperature of each production process is maintained within the corresponding standard temperature range. Each production process has a standard temperature range, and there are certain differences in the standard temperature ranges of different production processes.

[0044] Preferably, in an embodiment of the present invention, the method for obtaining the risk assessment value of the target production process at the current moment specifically includes: First, in order to improve the sensitivity of the abnormal temperature warning for the target production process, it is necessary to narrow down the standard temperature range of the target production process according to the warning sensitivity at the current moment of the target production process, so as to obtain the adjusted temperature range of the target production process at the current moment.

[0045] Preferably, in an embodiment of the present invention, the method for obtaining the adjusted temperature range of the target production process at the current moment specifically includes: Taking half of the product value of the warning sensitivity of the target production process at the current moment and the length of the standard temperature range of the target production process as the endpoint adjustment amount of the standard temperature range of the target production process at the current moment, where the length of the standard temperature range of the target production process is equal to the difference between the upper limit value and the lower limit value of the standard temperature range.

[0046] Obtaining the adjusted temperature range of the target production process at the current moment, the lower limit value of the adjusted temperature range is equal to the sum value of the lower limit value of the standard temperature range of the target production process and the endpoint adjustment amount, and the upper limit value of the adjusted temperature range is equal to the difference value between the upper limit value of the standard temperature range of the target production process and the endpoint adjustment amount.

[0047] As an example, in an embodiment of the present invention, the adjusted temperature range of the target production process at the current moment can be specifically expressed as: Wherein, represents the lower limit value of the adjusted temperature range of the target production process at the current moment; represents the upper limit value of the adjusted temperature range of the target production process at the current moment; represents the lower limit value of the standard temperature range of the target production process; represents the upper limit value of the standard temperature range of the target production process; represents the length of the standard temperature range of the target production process; represents the warning sensitivity of the target production process at the current moment; represents the endpoint adjustment amount of the standard temperature range of the target production process at the current moment.

[0048] When the adjusted predicted temperature of the target production process at the current moment belongs to the adjusted temperature range, it indicates that the temperature of the target production process is not abnormal at that moment, and the risk assessment value of the target production process at the current moment is set to the numerical value 0.

[0049] When the adjusted predicted temperature of the target production process at the current moment is greater than the upper limit value of the adjusted temperature range, it indicates that the temperature of the target production process at that moment is abnormally high. Then, based on the difference between the adjusted predicted temperature of the target production process at the current moment and the upper limit value of the adjusted temperature range, the risk assessment value of the target production process at the current moment is obtained.

[0050] In an embodiment of the present invention, the difference or the square of the difference between the adjusted predicted temperature of the target production process at the current moment and the upper limit value of the adjusted temperature range can be used as the risk assessment value of the target production process at the current moment, and this is not limited herein.

[0051] When the adjusted predicted temperature of the target production process at the current moment is less than the lower limit value of the adjusted temperature range, it indicates that the temperature of the target production process at that moment is abnormally low. Then, based on the difference between the adjusted predicted temperature of the target production process at the current moment and the lower limit value of the adjusted temperature range, the risk assessment value of the target production process at the current moment is obtained.

[0052] In an embodiment of the present invention, the difference or the square of the difference between the lower limit value of the adjusted temperature range and the adjusted predicted temperature of the target production process at the current moment can be used as the risk assessment value of the target production process at the current moment, and this is not limited herein.

[0053] As an example, in an embodiment of the present invention, the expression of the risk assessment value of the target production process at the current moment can be specifically, for example: Wherein, represents the risk assessment value of the target production process at the current moment; represents the adjusted predicted temperature of the target production process at the current moment; represents the lower limit value of the adjusted temperature range of the target production process at the current moment; represents the upper limit value of the adjusted temperature range of the target production process at the current moment.

[0054] Thus, the risk assessment value of the target production process at the current moment is obtained.

[0055] Step S4: Based on the risk assessment value, perform a risk warning on the temperature of the target production process at the current moment.

[0056] After obtaining the risk assessment value of the target production process at the current moment, a risk warning can be performed on the temperature of the target production process at the current moment based on the risk assessment value, so as to make a timely warning of the abnormal temperature phenomenon in the target production process.

[0057] Preferably, in an embodiment of the present invention, the method for performing a risk warning on the temperature of the target production process at the current moment specifically includes: If the risk assessment value of the target production process at the current moment is equal to the value 0, it indicates that the temperature of the target production process at the current moment is normal, and no risk warning is issued.

[0058] If the risk assessment value of the target production process at the current moment is greater than the value 0, it indicates that the temperature of the target production process at the current moment is abnormal. Then, different levels of risk warnings can be issued for the target production process according to other production processes adjacent to the target production process and the risk assessment value of the target production process at the current moment.

[0059] Preferably, in an embodiment of the present invention, the method for issuing different levels of risk warnings for the target production process specifically includes: The greater the risk assessment value of the target production process at the current moment, and the greater the risk assessment values of other production processes adjacent to the target production process at the current moment, it indicates that the temperatures of the target production process and the adjacent production processes at the current moment deviate more from the normal temperature range. Then, a more serious warning message needs to be issued for the target production process. Therefore, the average value of the sum of the risk assessment values of all other production processes adjacent to the target production process and the target production process at the current moment can be normalized, and the calculation result is limited to within the range to obtain the warning level coefficient of the target production process at the current moment.

[0060] In an embodiment of the present invention, the normalization process can specifically be, for example, the maximum-minimum normalization process. In other embodiments of the present invention, other normalization methods can be selected according to the specific value range, which will not be elaborated here.

[0061] As an example, in an embodiment of the present invention, the expression of the warning level coefficient of the target production process at the current moment can specifically be, for example: Among them, represents the warning level coefficient of the target production process at the current moment; represents the risk assessment value of the target production process at the current moment; represents the risk assessment value of the th other production process adjacent to the target production process at the current moment; represents the number of other production processes adjacent to the target production process; represents the normalization function for normalization processing.

[0062] If the early warning level coefficient is less than the preset first threshold, a first-level risk early warning is issued. If the early warning level coefficient is not less than the preset first threshold and less than the preset second threshold, a second-level risk early warning is issued. If the early warning level coefficient is not less than the preset second threshold, a third-level risk early warning is issued. Among them, the severity of the first-level risk early warning, the second-level risk early warning, and the third-level risk early warning gradually increases. In an embodiment of the present invention, indicator lights of different colors can be used to represent early warning information of different levels.

[0063] Among them, the value range of the preset first threshold is , and the value range of the preset first threshold is , in an embodiment of the present invention, the preset first threshold is set to 0.3, and the preset second threshold is set to 0.7. The specific values of the preset first threshold and the preset second threshold can also be set by the implementer according to the specific implementation scenario, and are not limited herein.

[0064] Through the above same method, the temperature of each production process in chemical production can be warned.

[0065] An embodiment of the present invention provides a digital chemical production information data management platform. Please refer to Figure 2 , which shows a framework diagram of a digital chemical production information data management platform provided by an embodiment of the present invention. The management platform includes: A data acquisition module, configured to acquire the actual temperature data of each production process at each historical moment within a preset time period before the current moment; A temperature prediction module, configured to use any one production process as the target production process, and predict the predicted temperature data of the target production process at the historical moment and the current moment according to the actual temperature data of each historical moment of the target production process; according to the difference between the actual temperature data and the predicted temperature data of the target production process at the historical moment, adjust the predicted temperature data of the target production process at the current moment to obtain the adjusted predicted temperature of the target production process at the current moment; A risk assessment module, configured to use the other production processes adjacent to and after the target production process as the successor production processes of the target production process, and obtain the early warning sensitivity of the target production process at the current moment according to the difference between the adjusted predicted temperature at the current moment between the target production process and each successor production process, and the position of the target production process among all production processes; according to the early warning sensitivity, adjust the standard temperature range of the target production process, and combine the adjusted predicted temperature of the target production process at the current moment to obtain the risk assessment value of the target production process at the current moment; A temperature early warning module, configured to perform risk early warning on the temperature of each production process at the current moment based on the risk assessment value.

[0066] See Figures 3 - 5 , Figure 3 which is a schematic diagram for comparing the temperature prediction effect of the catalytic reaction process provided by an embodiment of the present invention; Figure 4 which is a schematic diagram for analyzing the correlation between sensitivity and temperature deviation provided by an embodiment of the present invention; Figure 5 which is a schematic diagram for the relationship between the dynamic threshold interval and the risk level provided by an embodiment of the present invention; In combination with Figures 3 - 5 the relevant content for corroboration, the present application can effectively achieve the early warning effect on the temperature of each production process in the chemical production in the temperature prediction module and the risk assessment module.

[0067] An embodiment of the present invention provides a digital chemical production information data management system, which includes a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and when the computer program runs in the processor, it can implement the method described in steps S1 to S4.

[0068] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A digital chemical production information data management method, characterized in that: The method comprises: Obtain the actual temperature data of each production process at each historical moment in the preset time period before the current moment; Taking any production process as the target production process, predicting the predicted temperature data of the target production process at the historical moment and the current moment according to the actual temperature data of the target production process at each historical moment; adjusting the predicted temperature data of the target production process at the current moment according to the difference between the actual temperature data of the target production process at the historical moment and the predicted temperature data, and obtaining the adjusted predicted temperature of the target production process at the current moment; Other production processes that are adjacent to the target production process and are located after the target production process are regarded as subsequent production processes of the target production process, and the early warning sensitivity of the target production process at the current moment is obtained according to the difference in the adjusted predicted temperature between the target production process and each subsequent production process at the current moment, and the position of the target production process in all production processes; according to the early warning sensitivity, the standard temperature range of the target production process is adjusted, and combined with the adjusted predicted temperature of the target production process at the current moment, the risk assessment value of the target production process at the current moment is obtained; Based on the risk assessment value, a risk warning is issued for the temperature of the target production process at the current moment.

2. A digital chemical production information data management method according to claim 1, characterized in that: The predicted temperature data of the target production process at the historical moment and the current moment include: In a preset time period, other historical moments except the previous preset number of historical moments are used as the historical moments to be predicted; Take the current moment or any historical moment to be predicted as the target moment to be predicted, perform curve fitting on the actual temperature data of the target production process at all moments before the target moment to be predicted, obtain the fitting function of the target production process at the target moment to be predicted, input the target moment to be predicted into the fitting function, and output the predicted temperature data of the target production process at the target moment to be predicted.

3. A digital chemical production information data management method according to claim 2, characterized in that: The step of obtaining the adjusted predicted temperature of the target production process at the current moment includes: The difference between the actual temperature data and the predicted temperature data of the target production process at each historical moment to be predicted is used as the temperature prediction deviation value of the target production process at each historical moment to be predicted; The serial number value corresponding to each historical moment to be predicted is used as the numerator, the cumulative value of the serial number values ​​corresponding to all historical moments to be predicted is used as the denominator, and the ratio is used as the reference weight of each historical moment to be predicted; Using the reference weights of each historical moment to be predicted, weighted summation is performed on the temperature prediction deviation values ​​of the target production process at each historical moment to be predicted to obtain the temperature adjustment amount of the target production process at the current moment; The sum of the predicted temperature data and the temperature adjustment amount of the target production process at the current moment is used as the adjusted predicted temperature of the target production process at the current moment.

4. A digital chemical production information data management method according to claim 1, characterized in that: The obtaining of the early warning sensitivity of the target production process at the current moment includes: Performing negative correlation mapping on the absolute value of the difference between the target production process and each subsequent production process at the current moment of the adjusted predicted temperature to obtain the temperature similarity between the target production process and each subsequent production process at the current moment; Taking the average value of the temperature similarities between the target production process and all subsequent production processes at the current moment as the temperature importance of the target production process at the current moment; The number of all other production processes after the target production process is used as the numerator, the number of all other production processes before the target production process and the sum of the preset adjustment parameters are used as the denominator, and the ratio is used as the position parameter of the target production process; The temperature importance and the position parameter are integrated and normalized to obtain the early warning sensitivity of the target production process at the current moment.

5. A digital chemical production information data management method according to claim 1, characterized in that: The step of obtaining the risk assessment value of the target production process at the current moment includes: According to the warning sensitivity of the target production process at the current moment, the standard temperature range of the target production process is narrowed to obtain the adjustment temperature range of the target production process at the current moment; When the adjusted predicted temperature of the target production process at the current moment belongs to the adjusted temperature range, the risk assessment value of the target production process at the current moment is set to a value of 0; When the adjusted predicted temperature of the target production process at the current moment is greater than the upper limit value of the adjusted temperature range, the risk assessment value of the target production process at the current moment is obtained according to the difference between the adjusted predicted temperature of the target production process at the current moment and the upper limit value of the adjusted temperature range; When the adjusted predicted temperature of the target production process at the current moment is less than the lower limit value of the adjusted temperature range, the risk assessment value of the target production process at the current moment is obtained based on the difference between the adjusted predicted temperature of the target production process at the current moment and the lower limit value of the adjusted temperature range.

6. A digital chemical production information data management method according to claim 5, characterized in that: The step of obtaining the adjustment temperature range of the target production process at the current moment includes: One half of the product of the warning sensitivity of the target production process at the current moment and the length of the standard temperature range of the target production process is used as the endpoint adjustment amount of the standard temperature range of the target production process at the current moment; Obtain the adjustment temperature range of the target production process at the current moment, the lower limit value of the adjustment temperature range is equal to the sum of the lower limit value of the standard temperature range of the target production process and the endpoint adjustment amount, and the upper limit value of the adjustment temperature range is equal to the difference between the upper limit value of the standard temperature range of the target production process and the endpoint adjustment amount.

7. A digital chemical production information data management method according to claim 1, characterized in that: The risk warning of the temperature of the target production process at the current moment includes: If the risk assessment value of the target production process at the current moment is equal to 0, no risk warning will be issued; If the risk assessment value of the target production process at the current moment is greater than 0, risk warnings of different levels are issued to the target production process based on other production processes adjacent to the target production process and the risk assessment value of the target production process at the current moment.

8. A digital chemical production information data management method according to claim 7, characterized in that: The risk warnings of different levels issued for the target production process include: Normalize the average of the risk assessment values ​​and the target production process at the current moment for all other production processes adjacent to the target production process to obtain the warning level coefficient of the target production process at the current moment; If the warning level coefficient is less than the preset first threshold, a first-level risk warning is issued; if the warning level coefficient is not less than the preset first threshold and less than the preset second threshold, a second-level risk warning is issued; if the warning level coefficient is not less than the preset second threshold, a third-level risk warning is issued, wherein the severity of the first-level risk warning, the second-level risk warning and the third-level risk warning gradually increases.

9. A digital chemical production information data management platform, characterized in that: The management platform includes: A data acquisition module is used to obtain the actual temperature data of each production process at each historical moment in a preset time period before the current moment; The temperature prediction module is used to take any production process as the target production process, and predict the predicted temperature data of the target production process at the historical moment and the current moment according to the actual temperature data of the target production process at each historical moment; according to the difference between the actual temperature data of the target production process at the historical moment and the predicted temperature data, adjust the predicted temperature data of the target production process at the current moment to obtain the adjusted predicted temperature of the target production process at the current moment; A risk assessment module is used to take other production processes that are adjacent to the target production process and are located after the target production process as subsequent production processes of the target production process, and obtain the early warning sensitivity of the target production process at the current moment according to the difference in the adjusted predicted temperature between the target production process and each subsequent production process at the current moment, and the position of the target production process in all production processes; according to the early warning sensitivity, adjust the standard temperature range of the target production process, and obtain the risk assessment value of the target production process at the current moment in combination with the adjusted predicted temperature of the target production process at the current moment; The temperature warning module is used to issue a risk warning for the temperature of each production process at the current moment based on the risk assessment value.

10. A digital chemical production information data management system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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