CN117113255B - Liquefied natural gas safety monitoring system based on status monitoring and automatic content identification - Google Patents
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Abstract
The invention relates to the technical field of natural gas safety monitoring, in particular to a liquefied natural gas safety monitoring system capable of automatically identifying content based on state monitoring. The emergency scheduling unit is used for receiving data after abnormal state detection, data after analysis and data after trend prediction, making an emergency plan for the data after abnormal state detection, the data after analysis and the data after trend prediction, and then intelligently scheduling the made emergency plan. According to the invention, the emergency plan is formulated according to the analyzed data and the identified data, and the emergency equipment is started by formulating the emergency plan, so that LNG leakage accidents are prevented, and the expansion of the accidents is avoided.
Description
Technical Field
The invention relates to the technical field of natural gas safety monitoring, in particular to a liquefied natural gas safety monitoring system capable of automatically identifying content based on state monitoring.
Background
The current city development has higher and higher dependence on gas, gas supply relates to the operation of various industries of the city and the production and life of people, and a natural gas safety monitoring system appears in the market, but the accuracy and the reliability of the natural gas safety monitoring system are influenced by technical limitations, the accuracy, the error, the sensitivity and other technical indexes of a sensor may influence the monitoring result, and the technical limitations may cause the error of the monitoring result, so that the error identification or omission of the LNG state and content abnormality is caused.
Meanwhile, due to the complexity and diversity of data, the natural gas safety monitoring system can possibly have the situation of misunderstanding or misreporting of the data, and for a complex system and an operation environment, the monitoring system can not accurately identify the actual abnormal situation or generate false alarms and reports, which can lead staff to misunderstand the actual situation, influence the judgment and decision of the LNG safety state, and can not timely help the monitoring staff to escape from a dangerous area when the danger is generated or LNG leaks, and can not take corresponding measures for the predicted trend of LNG equipment in advance, so that the liquefied natural gas safety monitoring system based on the state monitoring automatic identification content is provided.
Disclosure of Invention
The invention aims to provide an automatic content identification liquefied natural gas safety monitoring system based on state monitoring so as to solve the problems in the background art.
In order to achieve the above purpose, the invention provides a liquefied natural gas safety monitoring system capable of automatically identifying content based on state monitoring, which comprises an acquisition processing unit, a state monitoring unit, an analysis and prediction unit, an alarm disposal unit and an emergency dispatching unit;
the acquisition processing unit is used for receiving acquired data and preprocessing the acquired data;
the state monitoring unit is used for monitoring the LNG equipment in real time, and identifying and detecting abnormal states of the monitored data;
the analysis and prediction unit is used for receiving and analyzing the data after preprocessing operation and the identified data, performing visual modeling on the analyzed data, and predicting trend on the identified data and the analyzed data;
the alarm handling unit is used for receiving the data after abnormal state detection and the analyzed data and triggering an alarm;
the emergency scheduling unit is used for receiving the identified data, the analyzed data and the data with predicted trend, making an emergency plan, and then intelligently scheduling the made emergency plan;
the emergency scheduling unit is used for receiving the data after abnormal state detection, the data after analysis and the data after trend prediction, making an emergency plan for the data after abnormal state detection, the data after analysis and the data after trend prediction, intelligently scheduling the made emergency plan, and simultaneously identifying and detecting the abnormal state again by using the state monitoring unit.
As a further improvement of the technical scheme, the acquisition processing unit comprises a data acquisition module and a data preprocessing module;
the data acquisition module is used for receiving the data acquired by the sensor and transmitting the acquired data into the data preprocessing module;
the data preprocessing module is used for receiving the acquired data and preprocessing the acquired data.
As a further improvement of the technical scheme, the state monitoring unit is used for monitoring the LNG equipment in real time, identifying the data monitored in real time through an artificial intelligence technology, detecting the abnormal state of the identified data, and transmitting the identified data into the analysis and prediction unit.
As a further improvement of the technical scheme, the analysis and prediction unit comprises a data analysis module and a visual modeling module, and the emergency scheduling unit comprises an emergency plan module;
the data analysis module is used for receiving the data after the preprocessing operation in the data preprocessing module, receiving the data identified in the state monitoring unit, analyzing according to the data after the preprocessing operation and the identified data through a data mining technology, analyzing whether the LNG has leakage or not, and simultaneously transmitting the analyzed data into the visual modeling module;
the visual modeling module is used for receiving the analyzed data, storing the analyzed data by adopting a relational database, establishing template data for the stored data, and visualizing the established template data;
the emergency plan module is used for receiving the analyzed data, receiving the data identified in the state monitoring unit, making an emergency plan according to the analyzed data and the identified data, and transmitting the made emergency plan into the visual modeling module.
As a further improvement of the technical scheme, the analysis and prediction unit further comprises a trend prediction module;
the trend prediction module is used for receiving the data identified in the state monitoring unit, receiving the data analyzed in the data analysis module, predicting the trend according to the identified data and the analyzed data, adopting a double-index smoothing algorithm, and simultaneously transmitting the data after predicting the trend into the emergency plan module;
the emergency plan module receives the data after the trend prediction, receives the data identified in the state monitoring unit, makes an emergency plan according to the data after the trend prediction and the data identified, and transmits the made emergency plan into the visual modeling module.
As a further improvement of the technical scheme, the formula of the double-exponential smoothing algorithm is as follows:
Leved(x)＝α*Y(x)+(1-α)*(Leved(x-1)+Tend(x-1))；
in the formula, level (x) represents a horizontal predicted result value of time x, Y (x) is a predicted value of time x, α is an identified data value, and (1- α) is a predicted trend Tend and a contribution value of data analyzed at the previous time.
As a further improvement of the technical scheme, the emergency dispatching unit further comprises an intelligent dispatching module;
the intelligent scheduling module is used for receiving the emergency plan formulated in the emergency plan module and performing intelligent scheduling according to the formulated emergency plan.
As a further improvement of the technical scheme, the emergency plan module is used for receiving the data after identification, receiving the data after analysis in the data analysis module, receiving the data after trend prediction in the trend prediction module, making an emergency plan according to the identified data, the analyzed data and the data after trend prediction, transmitting the made emergency plan into the intelligent scheduling module, performing intelligent scheduling according to the made emergency plan by the intelligent scheduling module, and identifying and detecting abnormal states again by the state monitoring unit.
Compared with the prior art, the invention has the beneficial effects that:
1. in the liquefied natural gas safety monitoring system based on the state monitoring and automatic content identification, the emergency plan module is used for receiving the analyzed data, receiving the data identified in the state monitoring unit, and when the state monitoring unit detects that LNG leaks or the data analysis module analyzes that LNG leaks and other abnormal conditions exist, making an emergency plan according to the analyzed data and the identified data, and starting emergency equipment by making the emergency plan, so that LNG leakage accidents are prevented, and accident expansion is avoided.
2. In the liquefied natural gas safety monitoring system based on the state monitoring and automatic content identification, the intelligent scheduling module is used for receiving the emergency plan formulated in the emergency plan module, performing intelligent scheduling according to the formulated emergency plan, and automatically adjusting the temperature coefficient through intelligent scheduling to avoid the influence of LNG leakage on the environment and personnel;
and meanwhile, the state monitoring unit is used for identifying and detecting the abnormal state again for the data after intelligent scheduling, and the data after intelligent scheduling can be verified, so that potential errors or omission can be corrected.
Drawings
FIG. 1 is an overall block diagram of the present invention;
FIG. 2 is a block diagram of an acquisition processing unit according to the present invention;
FIG. 3 is a block diagram of an analytical prediction unit according to the present invention;
fig. 4 is a block diagram of a conforming emergency unit of the present invention.
The meaning of each reference sign in the figure is:
1. an acquisition processing unit; 11. a data acquisition module; 12. a data preprocessing module;
2. a state monitoring unit;
3. an analysis prediction unit; 31. a data analysis module; 32. a visual modeling module; 33. a trend prediction module;
4. an alarm handling unit;
5. an emergency dispatch unit; 51. an emergency plan module; 52. and an intelligent scheduling module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The invention provides a liquefied natural gas safety monitoring system capable of automatically identifying content based on state monitoring, referring to fig. 1-4, which comprises an acquisition processing unit 1, a state monitoring unit 2, an analysis prediction unit 3, an alarm disposal unit 4 and an emergency dispatching unit 5;
considering that the risk that a sensor, data transmission equipment or a monitoring algorithm in the current state monitoring system may have faults, when a key component fails, the monitoring system may not be capable of timely identifying and reporting abnormal conditions, so that potential safety risks are increased, then we provide an automatic content identification liquefied natural gas safety monitoring system based on state monitoring, an acquisition processing unit 1 of the system is used for receiving acquired data and performing preprocessing operation on the acquired data, a state monitoring unit 2 is used for monitoring LNG equipment (LNG (liquefied natural gas) equipment is an equipment system for liquefied natural gas production, storage, transportation and supply) in real time, identifying and detecting abnormal states of the monitored data, an analysis prediction unit 3 is used for receiving and analyzing the data after preprocessing operation, visually modeling the analyzed data, predicting trend of the identified data and the analyzed data, an alarm processing unit 4 is used for receiving the data after detecting abnormal states, the analyzed data and triggering an alarm, and a scheduling unit 5 is used for receiving the identified data, the analyzed trend and performing intelligent scheduling after making of an emergency schedule;
the emergency scheduling unit 5 is configured to receive the data after abnormal state detection, the data after analysis and the data after trend prediction, make an emergency plan for the data after abnormal state detection, the data after analysis and the data after trend prediction, and then make intelligent scheduling for the made emergency plan, and meanwhile, use the state monitoring unit 2 to identify and detect the abnormal state again for the data after intelligent scheduling, so that by identifying and detecting the abnormal state again, confirmation and accuracy of the abnormal state can be improved, whether the abnormal situation exists is further determined, and misinformation or misjudgment possibility is eliminated.
The following is a refinement of the above units, please refer to fig. 2-4;
the acquisition processing unit 1 comprises a data acquisition module 11 and a data preprocessing module 12;
the data acquisition module 11 is configured to receive data acquired by a sensor, where the principle of data acquisition is to convert various physical quantities (such as temperature, pressure, liquid level, components, etc.) around LNG storage tanks, pipelines, tank trucks, etc. into electrical signals or other signals that can be processed by a computer to form a real-time data stream, and meanwhile, transmit the acquired data into the data preprocessing module 12, where the data preprocessing module 12 is configured to receive the acquired data and perform preprocessing operations on the acquired data, where the preprocessing operations include data cleaning (data cleaning refers to processing and converting the acquired raw data to remove erroneous, incomplete or redundant data), and data transformation (data transformation refers to converting the raw data to change the expression form, distribution or relation of the data to meet the requirements of analysis, modeling or application), and reduce errors and erroneous data in the data through the preprocessing operations, so as to provide accurate data for the subsequent data analysis module 31;
method steps for forming a real-time data stream:
(1) sensor interface design: the sensor interface is designed to convert the signal output by the sensor into a digital signal suitable for computer processing, and the interface can adopt devices such as an A/D converter and the like to convert the analog signal into a digital signal so as to realize the digital processing of data;
(2) and (3) data acquisition hardware design: the data acquisition hardware circuit is designed and comprises a signal conditioning circuit, a sampling hold circuit, an A/D converter and the like, and the signals output by the sensor are preprocessed and converted, so that the stability and the accuracy of the data are ensured;
(3) and (3) data acquisition software design: data acquisition software is designed, and the data acquisition software is communicated with the sensor in a serial port communication mode, a network communication mode and the like to acquire data output by the sensor;
(4) and (3) data acquisition system architecture design: the data acquisition system architecture is designed, a distributed architecture is adopted, data of a plurality of sensors can be acquired and processed, the efficiency and accuracy of data acquisition are improved, and simultaneously, the concurrent processing and real-time updating of the data can be realized by adopting technologies such as multithreading, asynchronous IO and the like.
The state monitoring unit 2 is used for monitoring the LNG equipment in real time, identifying the data after the real-time monitoring through an artificial intelligence technology, realizing the real-time monitoring of various physical quantities (such as temperature, pressure, liquid level, components and the like) around the LNG equipment, automatically identifying the temperature, pressure, liquid level, components of the LNG, detecting abnormal states of the identified data, realizing the real-time monitoring of various physical quantities around the LNG equipment, transmitting the data after the abnormal states are detected into the alarm processing unit 4 when the abnormal conditions of the LNG leakage are detected, and the alarm processing unit 4 is used for receiving the data after the abnormal states are detected in the state monitoring unit 2 and triggering an alarm to timely discover the leakage, the leakage quantity and the leakage position of the LNG equipment, reminding related personnel to take corresponding measures and transmitting the identified data into the analysis prediction unit 3.
The analysis and prediction unit 3 comprises a data analysis module 31 and a visual modeling module 32, and the emergency dispatch unit 5 comprises an emergency plan module 51;
the data analysis module 31 is configured to receive the data after the preprocessing operation in the data preprocessing module 12, receive the data after the recognition in the status monitoring unit 2, and analyze, by using a data mining technology (the data mining technology refers to a series of methods and tools for mining valuable information, patterns and association rules from a large amount of data), according to the data after the preprocessing operation and the recognized data, to analyze whether the LNG has a leakage condition, and when an abnormal condition of the LNG leakage is analyzed, to transmit the analyzed data to the alarm handling unit 4, and the alarm handling unit 4 is configured to receive the analyzed data and trigger an alarm, remind a relevant person to take corresponding measures, and at the same time, to transmit the analyzed data to the visual modeling module 32.
The visual modeling module 32 is configured to receive the analyzed data and store the analyzed data using a relational database (relational database is a database management system based on a relational model), such as MySQL (MySQL is a popular open-source relational database management system), oracle (Oracle is a globally leading relational database management system), or using a non-relational database (non-relational database is a database management system that does not use a traditional relational database model, unlike relational databases, which uses different data models and storage methods to meet the requirements of large-scale data storage and high concurrent access), for example, mongoDB (MongoDB is an open-source document-type non-relational database, which uses a document-oriented data model to store data in a flexible and nestable document structure), and simultaneously builds template data of the stored data, wherein the template data comprises temperature, pressure, liquid level and components of LNG, and visualizes the built template data, and the built template data is displayed in a form of a graph, a figure and the like, so that subsequent data analysis and accident investigation are facilitated, and a data visualization tool including a Tableau (Tableau is a popular data visualization and business intelligence tool) can be used, which provides an intuitive visual interface and rich analysis functions, and helps users to quickly observe and extract valuable insight from the data.
The emergency plan module 51 is configured to receive the analyzed data, receive the identified data in the status monitoring unit 2, and make an emergency plan according to the analyzed data and the identified data, so that safety awareness can be enhanced through the emergency plan, and instruct monitoring personnel and related personnel to respond to actions when abnormal situations occur, for example, when LNG is analyzed to leak, the system automatically makes the emergency plan, helps the monitoring personnel know an emergency procedure and an operation procedure, how to avoid being affected by LNG, how to quickly and safely escape from a dangerous area, and simultaneously starts emergency equipment, such as start spraying, timely control a leak accident, avoid the expansion of casualties of the accident, and meanwhile, transmit the made emergency plan into the visual modeling module 32, so that the monitoring personnel and related personnel can know the emergency procedure and the operation procedure through a visual tool.
The analysis prediction unit 3 further includes a trend prediction module 33;
the trend prediction module 33 is configured to receive the data identified in the state monitoring unit 2, receive the data analyzed in the data analysis module 31, and predict a trend according to the identified data and the analyzed data, where the predicted trend can help a monitor to predict an abnormal situation early, so that measures can be taken more timely, an automatic alarm or an emergency triggering process can be realized, a problem of quick response can be solved, the possibility of event expansion can be reduced, and a double-index smoothing algorithm is adopted, and meanwhile, the data after the predicted trend is transmitted into the emergency plan module 51;
the formula of the double-exponential smoothing algorithm:
Leved(x)＝α*Y(x)+(1-α)*(Leved(x-1)+Tend(x-1))；
in the formula, the level (x) represents a horizontal predicted result value of time x, Y (x) is a predicted value of time x, alpha is an identified data value, the contribution of the current predicted value to the horizontal predicted result value is controlled, and (1-alpha) is a predicted trend Tend and a contribution value of data analyzed at the last moment.
The emergency plan module 51 receives the data after trend prediction, receives the data after recognition in the state monitoring unit 2, and makes an emergency plan according to the data after trend prediction and the data after recognition, measures can be taken in advance according to the made emergency plan, potential risks are prevented, injuries to personnel and equipment are reduced, and meanwhile the made emergency plan is transmitted into the visual modeling module 32, so that monitoring personnel and related personnel can conveniently know the emergency flow and operation procedure through a Tableau visual tool.
The emergency dispatch unit 5 further comprises an intelligent dispatch module 52;
the intelligent scheduling module 52 is configured to receive the emergency plan formulated in the emergency plan module 51, for example, when the emergency plan adopts a manner of reducing the temperature to control the leakage of LNG, the system performs intelligent scheduling according to the formulated emergency plan, adjusts the temperature of LNG to be within the safety range of the LNG temperature, avoids leakage or explosion when the temperature exceeds the bearing capacity of the container thereof, and should follow the relevant safety standard and operation rules in the LNG processing process to ensure the safety and reliability of LNG.
The emergency plan module 51 is configured to receive the data identified in step 2, receive the data analyzed in step 31, receive the data predicted in step 33, and make an emergency plan according to the identified data, the analyzed data and the data predicted in step 33, where the emergency plan can standardize a monitoring process, explicitly monitor tasks, responsibilities and processes, and combine the techniques of state monitoring and automatic content identification, so that a monitor can obtain key information more accurately and efficiently, reduce the influence of human factors, improve monitoring efficiency, and transfer the made emergency plan into the intelligent scheduling module 52, and the intelligent scheduling module 52 performs intelligent scheduling according to the made emergency plan, so that the intelligent scheduling can not only adjust temperature or pressure, but also optimize utilization and allocation of resources, and can automatically schedule the running state of equipment, reasonably allocate manpower, equipment and time resources, and use an optimized scheduling algorithm to reduce cost and improve efficiency, and then the state monitoring unit 2 recognizes and detects abnormal states again by using the intelligent scheduled data, thereby not only reducing the system judgment or misidentification but also improving the error and the intervention of the system, and the unnecessary intervention of the personnel;
optimizing a scheduling algorithm formula:
opch＝even*exon*(1-exrate)；
the method is mainly used for intelligent scheduling, automatic and intelligent operation of intelligent scheduling and real-time adjustment of monitoring data, can improve the safety and reliability of the system, can avoid the problems of overload and failure of the system through effective scheduling, and also improves the stability of the system.
The use flow is as follows:
the emergency plan module 51 receives the identified data, the analyzed data and the data after trend prediction, makes an emergency plan, transmits the made emergency plan into the visual modeling module 32, facilitates the inspection of monitoring personnel and related personnel, and simultaneously transmits the made emergency plan into the intelligent scheduling module 52, the intelligent scheduling module 52 performs intelligent scheduling according to the made emergency plan, and the state monitoring unit 2 re-identifies and detects abnormal states of the intelligently scheduled data.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (1)
1. Liquefied natural gas safety monitoring system based on status monitoring automatic identification content, its characterized in that: the system comprises an acquisition processing unit (1), a state monitoring unit (2), an analysis and prediction unit (3), an alarm treatment unit (4) and an emergency dispatching unit (5);
the acquisition processing unit (1) is used for receiving acquired data and preprocessing the acquired data;
the state monitoring unit (2) is used for monitoring the LNG equipment in real time, and identifying and detecting abnormal states of the monitored data;
the analysis and prediction unit (3) is used for receiving and analyzing the data after preprocessing operation and the identified data, performing visual modeling on the analyzed data, and simultaneously predicting trend on the identified data and the analyzed data;
the alarm handling unit (4) is used for receiving the data after abnormal state detection, the analyzed data and triggering an alarm;
the emergency scheduling unit (5) is used for receiving the identified data, the analyzed data and the data after trend prediction, making an emergency plan, and then intelligently scheduling the made emergency plan;
the emergency scheduling unit (5) is used for receiving the data after abnormal state detection, the data after analysis and the data after trend prediction, making an emergency plan for the data after abnormal state detection, the data after analysis and the data after trend prediction, intelligently scheduling the made emergency plan, and simultaneously, identifying and detecting the abnormal state again for the data after intelligent scheduling by the state monitoring unit (2);
the acquisition processing unit (1) comprises a data acquisition module (11) and a data preprocessing module (12);
the data acquisition module (11) is used for receiving data acquired by the sensor, transmitting the acquired data into the data preprocessing module (12), converting various physical quantities around the LNG storage tank, the pipeline and the tank wagon into electric signals or other signals which can be processed by a computer to form a real-time data stream, and simultaneously transmitting the acquired data into the data preprocessing module (12); the data preprocessing module (12) is used for receiving the acquired data and preprocessing the acquired data; the state monitoring unit (2) is used for monitoring the LNG equipment in real time, identifying the data monitored in real time through an artificial intelligence technology, detecting an abnormal state of the identified data, and transmitting the identified data into the analysis and prediction unit (3);
the analysis and prediction unit (3) comprises a data analysis module (31) and a visual modeling module (32), and the emergency scheduling unit (5) comprises an emergency plan module (51);
the data analysis module (31) is used for receiving the data after the preprocessing operation in the data preprocessing module (12), receiving the data identified in the state monitoring unit (2), analyzing whether LNG has leakage or not according to the data after the preprocessing operation and the identified data by a data mining technology, and simultaneously transmitting the analyzed data into the visual modeling module (32);
the visual modeling module (32) is used for receiving the analyzed data, storing the analyzed data by adopting a relational database, establishing template data for the stored data, and visualizing the established template data;
the emergency plan module (51) is used for receiving the analyzed data, receiving the data identified in the state monitoring unit (2), making an emergency plan according to the analyzed data and the identified data, and transmitting the made emergency plan into the visual modeling module (32);
the analysis and prediction unit (3) further comprises a trend prediction module (33);
the trend prediction module (33) is used for receiving the data identified in the state monitoring unit (2), receiving the data analyzed in the data analysis module (31), predicting the trend according to the identified data and the analyzed data, adopting a double-index smoothing algorithm, and simultaneously transmitting the data after predicting the trend into the emergency plan module (51);
the emergency plan module (51) receives the data after the trend prediction, receives the data identified in the state monitoring unit (2), formulates an emergency plan according to the data after the trend prediction and the data identified, and transmits the formulated emergency plan into the visual modeling module (32);
the double-exponential smoothing algorithm formula:
Leved(x)＝α*Y(x)+(1-α)*(Leved(x-1)+Tend(x-1))；
in the formula, level (x) represents a horizontal prediction result value of time x, Y (x) is a prediction value of time x, alpha is an identified data value, the contribution of the current prediction value to the horizontal prediction result value is controlled, and (1-alpha) is a prediction trend Tend and a contribution value of data analyzed at the previous moment;
the emergency dispatching unit (5) further comprises an intelligent dispatching module (52);
the intelligent scheduling module (52) is used for receiving the emergency plan formulated in the emergency plan module (51) and performing intelligent scheduling according to the formulated emergency plan;
the emergency plan module (51) is used for receiving the data identified in the state monitoring unit (2), receiving the data analyzed in the data analysis module (31), receiving the data subjected to trend prediction in the trend prediction module (33), making an emergency plan according to the identified data, the analyzed data and the data subjected to trend prediction, transmitting the made emergency plan into the intelligent scheduling module (52), intelligently scheduling the intelligent scheduling module (52) according to the made emergency plan, and identifying and detecting an abnormal state again through the state monitoring unit (2) on the intelligently scheduled data;
the intelligent scheduling module (52) performs intelligent scheduling according to the formulated emergency plan, adopts an optimized scheduling algorithm, automatically schedules the running state of equipment, reasonably distributes manpower, equipment and time resources, and then performs recognition and abnormal state detection on the data after intelligent scheduling through the state monitoring unit (2);
optimizing a scheduling algorithm formula:
opch＝even*exon*(1-exrate)；
the opch refers to data after intelligent scheduling, even refers to data values of an emergency plan after establishment, exon refers to coefficients to be adjusted, and exote refers to probability of last prediction trend.
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