Hydrocracking product quality prediction method, device and memory

文档序号：1039324 发布日期：2020-10-30 浏览：40次中文

阅读说明：本技术 加氢裂化的产品质量预测方法、装置和存储器 (Hydrocracking product quality prediction method, device and memory ) 是由黄新露吕建新陈玉石赵玉琢佟伟 *** 于 2019-04-28 设计创作，主要内容包括：本发明公开了加氢裂化的产品质量预测方法、装置和存储器,其中所述方法包括步骤：预设加氢裂化系统的监控数据的数据标准；获取加氢裂化系统的监测数据；根据数据标准对历史监控数据进行数据整理；根据监控数据统一的采集周期和采集时间点,对监控数据中的性质信息数据和工况数据进行关联,构建建模数据；根据预设目标变量和预设自变量通过分类模型对建模数据进行模型训练,生成产品质量预测模型。本发明能够以采集时间点为关联点将各种监控数据进行同步关联。从而使各监控数据之间就具有了更好的对应性,因此,就可以实时获取的监控数据为参数通过预测模型来对监控对象进行实时的监控,就可以及时且准确的获得加氢裂化的产品质量预测的结果了。(The invention discloses a method, a device and a memory for predicting the quality of a hydrocracking product, wherein the method comprises the following steps: presetting a data standard of monitoring data of a hydrocracking system; acquiring monitoring data of a hydrocracking system; performing data sorting on the historical monitoring data according to a data standard; associating property information data and working condition data in the monitoring data according to the uniform acquisition period and acquisition time point of the monitoring data to construct modeling data; and performing model training on the modeling data through a classification model according to a preset target variable and a preset independent variable to generate a product quality prediction model. The invention can synchronously correlate various monitoring data by taking the acquisition time point as a correlation point. Therefore, the monitoring data which can be obtained in real time are taken as parameters to carry out real-time monitoring on the monitored object through the prediction model, and the result of predicting the quality of the hydrocracking product can be timely and accurately obtained.)

1. A method for predicting the quality of a hydrocracked product, comprising the steps of:

s11, presetting a data standard of monitoring data of the hydrocracking system, wherein the data standard comprises a uniform acquisition cycle and an acquisition time point of the monitoring data; the monitoring data comprises property information data and working condition data; the property information data comprises product attribute information of products preset by the hydrocracking system and raw material property information of various raw materials;

s12, acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

s13, performing data sorting on the historical monitoring data according to the data standard, including:

extending the property information data, including: adjusting the interval granularity of the collection time points of the property information data to be consistent with the interval granularity of the collection time points of the working condition information, and updating the data of a blank collection time point between two pieces of property information data including data obtained from historical data to be consistent with the property information data with a time sequence ahead;

S14, associating property information data and working condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data;

s15, performing model training on the modeling data through a classification model according to a preset target variable and a preset independent variable to generate the product quality prediction model; the preset independent variable comprises the property information number and working condition data; the preset target variable comprises the product quality of a preset product of the hydrocracking system.

2. The product quality prediction method of claim 1, further comprising:

s16, obtaining key parameter items of the product quality according to the weight values of the influence weight of each parameter item in the working condition information and the raw material property information on the product quality;

and S17, taking the key parameter items obtained in real time as input, and obtaining the prediction result of the preset product of the hydrocracking system through the product quality prediction model.

3. The product quality prediction method of claim 1, wherein the performing classification model training on the modeling data comprises:

s21, dividing the modeling data into training data and testing data according to a preset proportion;

S22, modeling by using the training data, and evaluating by using the test data;

s23, when the evaluation result does not reach the preset value, adjusting the parameter items and/or the iteration times during modeling, and returning to the step S21; and when the evaluation result reaches a preset value, finishing modeling.

4. The product quality prediction method according to claim 1, wherein the associating property information data and operating condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data comprises:

generating a wide table according to the time corresponding relation between the working condition information and the property information data; and the wide table is used for storing the values of all parameter items in the working condition information and the property information data at the same acquisition time point in a correlation manner.

5. The product quality prediction method of claim 1, wherein the classification model comprises one of a general linear regression model, a logistic regression model, a decision tree model, a support vector machine model, a discriminant model, and a neural network model, and any combination thereof.

6. The product quality prediction method according to claim 2, wherein the obtaining of the key parameter item of the product quality according to the weight value of the influence weight of each parameter item in the operating condition information and the material property information on the product quality includes:

And determining the parameter items with the weight values higher than the preset value and/or determining the preset number of parameter items before the weight values are sorted as key parameter items.

7. The product quality prediction method according to claim 5, wherein the step of respectively obtaining the acquisition time point corresponding to each of the operating condition information, and performing median calculation on all the operating condition information within a preset time period before and after the acquisition time point to generate the operating condition information after the operating condition information is corrected comprises:

the preset time period is from 30 minutes before the acquisition time point to 30 minutes after the acquisition time point.

8. The product quality prediction method of any one of claims 1 to 7, wherein the product comprises liquefied gas, light naphtha, heavy naphtha, aviation kerosene, diesel oil, or tail oil.

9. A hydrocracked product quality prediction unit comprising:

the data standard comprises a uniform acquisition cycle and an acquisition time point of the monitoring data; the monitoring data comprises property information data and working condition data; the property information data comprises product attribute information of products preset by the hydrocracking system and raw material property information of various raw materials;

The data acquisition unit is used for acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

the data sorting unit is used for sorting the historical monitoring data according to the data standard, and comprises:

the modeling data generation unit is used for associating property information data and working condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data;

The model training unit is used for carrying out model training on the modeling data through a classification model according to a preset target variable and a preset independent variable to generate the product quality prediction model; the preset independent variable comprises the property information number and working condition data; the preset target variable comprises the product quality of a preset product of the hydrocracking system.

10. A memory comprising a software program adapted to be executed by a processor for performing the steps of the product quality prediction method according to any one of claims 1 to 8.

Technical Field

The invention relates to the field of petrochemical industry, in particular to a method, a device and a memory for predicting the quality of a hydrocracking product.

Background

With the development of information technology, the informatization degree of petroleum refining production devices is higher and higher, and mass production data is accumulated along with the informatization degree; for example, business systems such as a real-time database System of a hydrocracking System, a Distributed Control System (DCS), a Manufacturing Execution System MES (MES), a Laboratory Information Management System (LIMS), and the like, which are currently established in large petrochemical enterprises, can accumulate a large amount of data for safe production and Management. Deep mining and utilization of data is far from adequate. The method and the device have the advantages that the accident occurrence rule is searched from massive data, the management level of safety production is improved, and the important significance and effect are achieved.

The inventor finds that, in the prior art, a large amount of important production information is hidden behind data acquired by the various service systems, but the data quality difference of the historical data is large, and the influence manner of various working condition parameters on the product quality of a hydrocracking product is not clear enough, so that a process worker cannot timely know what influence the current working condition parameters have on the product quality according to the historical data.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a hydrocracking product quality prediction method, a hydrocracking product quality prediction device and a hydrocracking product quality prediction memory. The invention can improve the accuracy and timeliness of product quality pre-judgment.

The invention provides a method for predicting the quality of a hydrocracking product, which comprises the following steps:

s12, acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

s13, performing data sorting on the historical monitoring data according to the data standard, including:

Extending the property information data, including: adjusting the interval granularity of the collection time points of the property information data to be consistent with the interval granularity of the collection time points of the working condition information, and updating the data of a blank collection time point between two pieces of property information data including data obtained from historical data to be consistent with the property information data with a time sequence ahead;

s14, associating property information data and working condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data;

Preferably, in the embodiment of the present invention, the method further includes:

And S17, taking the key parameter items obtained in real time as input, and obtaining the prediction result of the preset product of the hydrocracking system through the product quality prediction model.

Preferably, in an embodiment of the present invention, the training of the classification model on the modeling data includes:

s21, dividing the modeling data into training data and testing data according to a preset proportion;

s22, modeling by using the training data, and evaluating by using the test data;

Preferably, in an embodiment of the present invention, the associating the property information data and the operating condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data includes:

Preferably, in an embodiment of the present invention, the classification model includes one of a general linear regression model, a logistic regression model, a decision tree model, a support vector machine model, a discriminant model and a neural network model, and any combination thereof.

Preferably, in an embodiment of the present invention, the obtaining, according to the weight value of the influence weight of each parameter item in the operating condition information and the raw material property information on the product quality, a key parameter item of the product quality includes:

and determining the parameter items with the weight values higher than the preset value and/or determining the preset number of parameter items before the weight values are sorted as key parameter items.

Preferably, in the embodiment of the present invention, the obtaining the acquisition time point corresponding to each piece of operating condition information, and performing median calculation on all pieces of operating condition information in a preset time period before and after the acquisition time point to generate the operating condition information after the operating condition information is corrected includes:

the preset time period is from 30 minutes before the acquisition time point to 30 minutes after the acquisition time point.

Preferably, in an embodiment of the invention, the product comprises liquefied gas, light naphtha, heavy naphtha, aviation kerosene, diesel oil or tail oil.

In another aspect of the embodiments of the present invention, there is also provided a hydrocracking product quality prediction apparatus, including:

the data acquisition unit is used for acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

the data sorting unit is used for sorting the historical monitoring data according to the data standard, and comprises:

The modeling data generation unit is used for associating property information data and working condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data;

the model training unit is used for carrying out model training on the modeling data through a classification model according to a preset target variable and a preset independent variable to generate the product quality prediction model; the preset independent variable comprises the property information number and working condition data; the preset target variable comprises the product quality of a preset product of the hydrocracking system.

In another aspect of an embodiment of the present invention, there is also provided a memory including a software program adapted to execute the steps of the above-described method for predicting product quality of hydrocracking by a processor.

It can be seen from the above that, in the present invention, a uniform data acquisition cycle and a synchronous acquisition time point are set for the monitoring data from different monitoring devices by presetting the data standard of the monitoring data of the hydrocracking system; and then, data sorting is carried out according to different acquisition periods and acquisition time points of the original monitoring data, so that various monitoring data which are not originally associated can be associated according to the acquisition periods and the acquisition time points in the preset data standard.

When data sorting is carried out, the problem of low quality of modeling data caused by the fact that the acquisition time point of the monitoring data lags behind the fluctuation time of the product quality of a product is solved by correcting the monitoring data in a mode of carrying out median value taking at a plurality of adjacent acquisition time points; in addition, aiming at the problem that some monitoring data acquisition equipment is lack of monitoring data or the interval granularity of the acquisition time point of the original data is overlarge, the embodiment of the invention also adopts a specific data filling means to update the data of the blank acquisition time point between the two property information data including the data acquired from the historical data into the mode consistent with the property information data with the front time sequence, so that the monitoring data after data arrangement accords with the preset data standard.

According to the embodiment of the invention, various monitoring data have a uniform acquisition cycle and acquisition time points, so that various monitoring data can be synchronously associated by taking the acquisition time points as association points. The embodiment of the invention can lead the monitoring data to have better correspondence, thus leading the prediction accuracy of the prediction model of the hydrocracking product quality prediction established by the embodiment of the invention to be more accurate. Therefore, the monitoring data acquired in real time are taken as parameters to monitor the monitored object in real time through the prediction model, and the result of predicting the quality of the hydrocracking product can be timely and accurately acquired.

Furthermore, in the invention, the effect of selecting the model of the key parameter item can be ensured by dividing the modeling data into the training data and the test data according to the preset proportion when the modeling data is subjected to the classification model training, so that the prediction accuracy of the product quality prediction model established by the method is more accurate.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic representation of the steps of a hydrocracking product quality prediction process as described in the present invention;

FIG. 2 is a schematic representation of yet another step of the hydrocracking product quality prediction process described in the present invention;

FIG. 3 is a schematic diagram of a hydrocracking product quality predicting apparatus according to the present invention;

fig. 4 is a schematic structural diagram of the computer device according to the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to improve timeliness and accuracy of hydrocracking product quality prediction, as shown in fig. 1, an embodiment of the present invention provides a hydrocracking product quality prediction method, including the steps of:

in the embodiment of the present invention, the acquisition device and the memory of the various production operation data of the hydrocracking System as the monitoring data include various production operation data stored in a real-time database System, a Distributed Control System (DCS), a Manufacturing Execution System MES (Manufacturing Execution System), a Laboratory Information Management System (LIMS), and the like of the hydrocracking System.

In the embodiment of the present invention, the historical data in the real-time database system includes parameter data of various aspects of the hydrocracking production process and related equipment, for example, the operating condition information may include data of temperature, pressure, flow, liquid level or valve opening and the like of each main link of the hydrocracking production process, and data of product flow and the like.

The LIMS system is mainly used for collecting, analyzing, reporting and managing performance indexes such as components and quality of various raw materials and various products, so that all links of laboratory work are comprehensively quantified and quality managed. The database of the LIMS system may store property information data of the feedstock and various major products (such as liquefied gas, light naphtha, heavy naphtha, aviation kerosene, diesel oil, tail oil, etc.) in the hydrocracking production process; specifically, the property information data may include product attribute information of a preset product and raw material property information of a plurality of raw materials; the product attribute information may specifically include density, distillation range, quality, and the like of the product.

The acquisition periods and the acquisition time points of the monitoring data stored in the various acquisition devices and the memories are different, so that the original monitoring data cannot be directly used as modeling data of a prediction model; for the reasons, in the embodiment of the invention, the data standard of the monitoring data of the hydrocracking system is preset firstly, so as to unify the acquisition cycle and the acquisition time point of various monitoring data; in the embodiment of the present invention, the monitoring data may specifically include property information data and operating condition data; the property information data can comprise product attribute information of preset products of the hydrocracking system and raw material property information of various raw materials;

S12, acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

after the data standard of the monitoring data is preset, the monitoring data of the hydrocracking system can be acquired through the various acquisition devices and the memories.

The monitoring data acquired by the embodiment of the invention can comprise historical monitoring data and real-time monitoring data, wherein the historical monitoring data is used for constructing a prediction model through data training.

In practical application, due to the fact that storage space is limited, operations such as data dilution and the like can be performed on data stored in the acquisition equipment and the memory of the various monitoring data, and accordingly data loss can be caused.

S13, performing data sorting on the historical monitoring data according to the data standard, including:

correcting the working condition information according to a preset rule, wherein the preset rule comprises the following steps: respectively acquiring an acquisition time point corresponding to each piece of working condition information, and performing median calculation on all pieces of working condition information in a preset time period before and after the acquisition time point to generate working condition information after the working condition information is corrected; extending the property information data, including: adjusting the interval granularity of the collection time points of the property information data to be consistent with the interval granularity of the collection time points of the working condition information, and updating the data of a blank collection time point between two pieces of property information data including data obtained from historical data to be consistent with the property information data with a time sequence ahead;

In practical application, if data is not collated, when the product quality is predicted in a prediction model mode, the prediction effect is poor, and the inventor finds that the prediction result of the hydrocracking product quality prediction model in the prior art is not accurate enough through research, at least, the reason of the prediction result comprises that the data quality used in model training is poor, specifically, the inventor learns that the acquisition time of the working condition information and the detection time of the influence of the product property influenced by the working condition information are not completely synchronous through researching the characteristics of the hydrocracking process flow; in addition, the influence of the condition information on the property information data is not point-to-point (time), specifically, the change of the property information data at a certain time is not caused by the change of the condition information at a certain time, but the change of the condition information at a certain time period can only cause the explicit adjustment of the correspondence of the property information data.

For the above reasons, in the embodiment of the present invention, the operating condition information is corrected according to the preset rule, so as to improve the correspondence between the operating condition information and the property information data;

Specifically, the preset rules may include: respectively acquiring time points corresponding to each piece of working condition information, and performing median calculation on all pieces of working condition information in a preset time period before and after the time points to generate working condition information after the working condition information is corrected; taking a preset time period before and after a time point as a time period from the first 30 minutes of the time point to the last 30 minutes of the time point as an example, before correction, the numerical value of the working condition information with the time point of X year Y month Z day at 10 am and 30 minutes at 30 m is A, and the correction process is to perform median calculation on all the working condition information from half an hour before the time point (X year Y month Z day at 10 am) to half an hour (X year Y month Z day at 11 am), so that the numerical value of the working condition information after correction reduces contingency and leaping property, which is equivalent to adopting correspondence between the representative working condition information numerical value and property information data in a larger time period, and thus, the calculated median number is taken as the numerical value of the working condition information, and a time period lasts, thereby causing the change of the property information data; thus, the correspondence between the median number and the property information data is corrected.

In the embodiment of the invention, aiming at the problems that compared with the working condition information, the data volume of the property information data is smaller and the data granularity is sparse, the property information data is expanded, specifically, the interval granularity of the collection time point of the property information data is adjusted to be consistent with the collection period and the collection time point set by the preset data standard, and the data of the blank time point between the two property information data including the data obtained from the historical data is updated to be consistent with the property information data with the front time sequence; taking an example that an acquisition cycle set by a preset data standard is one record per minute, taking an example that a certain parameter item in certain property information data is one record per 4 hours, when data filling is performed, firstly, the record of the property information data is also set to be one record per minute, then, data of blank time points in the property information data is filled, and the data of the blank time points are updated to be consistent with the property information data with a front time sequence, namely, the acquisition cycle and the acquisition time point of the expanded property information data are consistent with the preset data standard.

because various monitoring data in the embodiment of the invention have uniform acquisition cycle and acquisition time point, various monitoring data can be synchronously associated by taking the acquisition time point as an association point. The embodiment of the invention can lead the monitoring data to have better correspondence, thus leading the prediction accuracy of the prediction model of the hydrocracking product quality prediction established by the embodiment of the invention to be more accurate.

In practical application, each parameter item of property information data and each parameter item of working condition information at a certain time point can be included in a strip record of a wide table in a mode of establishing the wide table; when the wide table comprises a large number of records, modeling data is constructed, namely, the wide table is generated according to the time corresponding relation between the working condition information and the property information data; and the wide table is used for storing the values of all parameter items in the working condition information and the property information data at the same time point in an associated manner.

S15, performing model training on the modeling data through a classification model according to a preset target variable and a preset independent variable to generate the product quality prediction model; the preset independent variable comprises the property information number and working condition data; the preset target variable comprises the product quality of a preset product of the hydrocracking system.

Therefore, the monitoring data acquired in real time are taken as parameters to monitor the monitored object in real time through the prediction model, and the result of predicting the quality of the hydrocracking product can be timely and accurately acquired.

The method for performing classification model training on modeling data may specifically be as shown in fig. 2, and includes the steps of:

s21, dividing the modeling data into training data and testing data according to a preset proportion;

in practical applications, the preset ratio may be set to 7 to 3, that is, 70% of the data is used as training data, and the other 30% of the data is used as test data. It should be noted that, in the embodiment of the present invention, the numerical value of the preset ratio may be adjusted and set according to the needs of those skilled in the art, and is not limited specifically herein.

S22, modeling by using training data, and evaluating by using test data;

modeling through training data to construct a model for predicting product quality; in practical applications, the classification model used in the embodiment of the present invention may be a general linear regression model, a logistic regression model, a decision tree model, a support vector machine model, a discriminant model, or a neural network model, or two or more of the models may be used for mutual verification and correction.

The test data may be evaluated during the modeling process to verify the accuracy and validity of the predictive model.

S23, when the evaluation result does not reach the preset value, adjusting the parameter items and/or the iteration times during modeling, and returning to the step S21; and when the evaluation result reaches a preset value, finishing modeling.

In summary, in the embodiment of the present invention, a data standard of monitoring data of a hydrocracking system is preset, and a uniform data acquisition period and a synchronous acquisition time point are set for the monitoring data from different monitoring devices; and then, data sorting is carried out according to different acquisition periods and acquisition time points of the original monitoring data, so that various monitoring data which are not originally associated can be associated according to the acquisition periods and the acquisition time points in the preset data standard.

Preferably, in the embodiment of the present invention, the method may further include the steps of:

In the embodiment of the invention, the modeling data is used for prejudging the product quality, so that the data which can effectively prejudge the product quality can be screened from the original modeling data; specifically, the raw modeling data also includes a lot of atypical data and noise data; on one hand, the original modeling data also comprises data which can not well reflect the product quality, for example, the corresponding relation between the change of the numerical value of some parameter items in some working condition information and/or property information data and the product quality change is not clear, therefore, in the embodiment of the invention, the data can be screened; therefore, parameter items with clear corresponding relation between numerical value change and product quality change are screened from all parameter items in the working condition information and/or the property information data.

For example, during the operation of the hydrocracking unit, the parameter items of the operating condition information of the DCS system are more than five hundred, most of which have insignificant influence on the product quality of the product, and therefore, it is necessary to determine the parameter items having large influence on the product quality from the plurality of operating condition information and the feedstock property information.

After the modeling data is obtained, in order to determine which parameter items have a larger influence on the product quality and a specific influence mode from various working condition information and various parameter items in the raw material property information, in the embodiment of the invention, the modeling data is subjected to classification model training, and the key parameter items of the product quality are obtained by obtaining the weight values of the influence weights of the various parameter items in the working condition information and the raw material property information on the product quality.

The prediction accuracy and effectiveness of the prediction model can be judged through test data, and in the process of continuously adjusting each parameter item in the working condition information and the raw material property information to obtain a better prediction result, the weight value of the influence weight of each parameter item on the product quality, namely the influence degree of each parameter item on the product quality, can be obtained, so that the important parameter items influencing the product quality can be determined from a plurality of parameter items, and in practical application, the parameter items with the weight values higher than the preset values and/or the preset number of parameter items before the weight value sorting can be determined as the important parameter items.

By selecting key parameter items, the number of items of input parameters of the prediction model is reduced, the operation efficiency of the prediction model is improved, and the interference of some atypical data and noise data on the prediction model is reduced. Thereby also improving the prediction result of the prediction model.

Therefore, in the embodiment of the invention, the effect of selecting the model with key parameter items can be ensured by dividing the modeling data into the training data and the test data according to the preset proportion when the modeling data is subjected to the classification model training, so that the prediction accuracy of the product quality prediction model established by the method is more accurate.

In an embodiment of the present invention, a hydrocracking product quality prediction apparatus is further provided, and fig. 3 shows a schematic structural diagram of the hydrocracking product quality prediction apparatus provided in the embodiment of the present invention, where the hydrocracking product quality prediction apparatus is an apparatus corresponding to the hydrocracking product quality prediction method described in embodiment 1, that is, the hydrocracking product quality prediction method in embodiment 1 is implemented by a virtual apparatus, and each virtual module constituting the hydrocracking product quality prediction apparatus may be executed by an electronic device, such as a network device, a terminal device, or a server.

Specifically, the apparatus for predicting the quality of a hydrocracked product in the embodiment of the present invention includes:

the data standard of the monitoring data of the hydrocracking system is preset, and the data standard comprises a uniform acquisition cycle and an acquisition time point of the monitoring data; the monitoring data comprises property information data and working condition data; the property information data comprises product attribute information of products preset by the hydrocracking system and raw material property information of various raw materials;

the data acquisition unit 02 is used for acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

the data sorting unit 03 is configured to perform data sorting on the historical monitoring data according to the data standard, and includes:

The modeling data generation unit 04 is used for associating property information data and working condition data in the monitoring data according to the uniform acquisition cycle and acquisition time point of the monitoring data to construct modeling data;

the model training unit 05 is used for performing model training on the modeling data through a classification model according to a preset target variable and a preset independent variable to generate the product quality prediction model; the preset independent variable comprises the property information number and working condition data; the preset target variable comprises the product quality of a preset product of the hydrocracking system.

Since the working principle and the beneficial effects of the hydrocracking product quality predicting apparatus in the embodiment of the present invention have been described and illustrated in the hydrocracking product quality predicting method in embodiment 1, they may be referred to each other and will not be described herein again.

In an embodiment of the present invention, there is further provided a memory, wherein the memory includes a software program adapted to be executed by the processor for performing the steps of the hydrocracking product quality prediction method corresponding to fig. 1.

The embodiment of the present invention can be implemented by means of a software program, that is, by writing a software program (and an instruction set) for implementing each step in the hydrocracking product quality prediction method corresponding to fig. 1, the software program is stored in a memory, and the memory is arranged in a computer device, so that the software program can be called by a processor of the computer device to implement the purpose of the embodiment of the present invention.

The application scenario of the memory in the embodiment of the present invention is described below by using a computer device, as shown in fig. 4, the computer terminal includes a bus 201, a memory 202, a processor 203, and a communication module 204;

the bus 201 is used to connect the memory 202 and the processor 203; the processor 203 is used for executing software programs in the memory 202; the communication module 204 is used to communicate with the real-time database system and the LIMS.

The memory 202, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 203 executes various functional applications and data processing of the electronic device, i.e. the processing method of the above-described method embodiment, by running the non-transitory software programs, instructions and modules stored in the memory 202.

The memory 202 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory 202 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 202 may optionally include memory located remotely from the processor 203, which may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory 203 and, when executed by the one or more processors 203, perform:

s12, acquiring monitoring data of the hydrocracking system, wherein the monitoring data comprises historical monitoring data and real-time monitoring data;

s13, performing data sorting on the historical monitoring data according to the data standard, including:

Preferably, the method further comprises the following steps:

Since the working principle and the beneficial effects of the memory and the hydrocracking product quality prediction method in the embodiment of the present invention have been described and described in detail in the embodiment of the hydrocracking product quality prediction method corresponding to fig. 1, the memory in the embodiment of the present invention can be understood by referring to the embodiment of the hydrocracking product quality prediction method corresponding to fig. 1, and will not be described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned memory comprises: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a ReRAM, an MRAM, a PCM, a NAND Flash, a NOR Flash, a Memory, a magnetic disk, an optical disk, or other various media that can store program codes.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

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Hydrocracking product quality prediction method, device and memory

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