Refinery Dynamic Equipment Risk Early Warning Method and Early Warning System

Abstract

The present invention relates to the field of petrochemical machinery, and discloses a risk early warning method and an early warning system for refinery moving equipment. The early warning method includes: obtaining historical operation data and current operation data of a plurality of refinery moving equipment; mining several equipment state patterns from the historical operation data to establish an equipment state library; establishing a corresponding equipment risk model; according to the current operation data of each refinery moving equipment, determining its current equipment state pattern, and using its current operation data and the equipment risk model corresponding to its current equipment state pattern to conduct risk early warning on it. By establishing an equipment risk early warning model adapted to different equipment state patterns based on the historical data of multiple similar equipment, the pertinence of the risk early warning model is effectively improved, thereby improving the accuracy and timeliness of equipment risk early warning, and summarizing experience based on the data comparison between multiple similar equipment to prevent similar risks from occurring in similar equipment, effectively improving the reliability of equipment operation.

Description

Technical Field

[0001] The present invention relates to the field of petrochemical machinery, and particularly to a risk early warning method and system for refinery rotating equipment. Background Art

[0002] In recent years, the major overhaul cycle of the installations of Sinopec refinery enterprises has been continuously extended. The economic benefits brought by long-term operation are very huge, but higher requirements are also put forward for the reliability of equipment. At present, faults of rotating equipment in Sinopec refinery installations still occur from time to time. The main reasons for the occurrence of faults in refinery rotating equipment are as follows: the operating conditions of some equipment deviate from the safe area for a long time (for example, fouling and blade fracture of the expander in the fluid catalytic cracking unit), the daily inspection and maintenance are not in place, the operating experience and emergency handling experience of operators are lacking, etc. In addition, due to reasons such as continuous staff reduction in refinery enterprises, relatively harsh operating conditions of refinery equipment, and increasingly strict laws and regulations, it has become an inevitable requirement to improve the fault early warning ability of refinery rotating equipment to improve its reliability.

[0003] At present, in the aspect of fault early warning of refinery rotating equipment, a condition monitoring system based on the analysis of equipment vibration signals is widely used. It mainly monitors the performance parameters of equipment, such as characteristic data of vibration, displacement, temperature, etc. under the operating conditions online, and through processing and analysis, obtains information reflecting the equipment state and fault symptoms, so as to realize the state evaluation, fault diagnosis and life prediction of the equipment. This fault early warning method uses the monitoring data of a single equipment to conduct fault early warning on the equipment, and has the disadvantages of single data source and data structure, and relatively low early warning degree and accuracy for faults. Summary of the Invention

[0004] In order to overcome or at least partially overcome the above technical problems existing in the prior art, an embodiment of the present invention provides a risk early warning method and system for refinery rotating equipment.

[0005] On the one hand, the present invention provides a risk early warning method for refinery rotating equipment. The risk early warning method for refinery rotating equipment includes: obtaining historical operation data and current operation data of a plurality of refinery rotating equipment; mining several equipment state modes of the refinery rotating equipment from the historical operation data to establish an equipment state library; determining the current equipment state mode of the refinery rotating equipment from the equipment state library according to the current operation data of each refinery rotating equipment; establishing an equipment risk model for several equipment state modes of the refinery rotating equipment; and using the current operation data of each refinery rotating equipment and the equipment risk model corresponding to the current equipment state mode of the refinery rotating equipment to conduct risk early warning on the refinery rotating equipment.

[0006] Preferably, the establishment of the equipment risk model for several equipment status modes of the refinery dynamic equipment includes: classifying the historical operation data according to the several equipment status modes of the refinery dynamic equipment mined; and constructing an equipment risk model corresponding to each equipment status mode according to the historical operation data included in each equipment status mode.

[0007] Preferably, the equipment status modes include: a fault status mode, a healthy status mode, and an optimal status mode; and the equipment risk models corresponding to the equipment status modes are respectively a fault status risk model, a healthy status risk model, and an optimal status risk model.

[0008] Preferably, the risk early warning for the refinery dynamic equipment includes: inputting the current operation data of the refinery dynamic equipment into the equipment risk model corresponding to the current operation mode of the refinery dynamic equipment to obtain the risk early warning data of the refinery dynamic equipment; and displaying the risk early warning data with a first feature, where the first feature is used to indicate the multi-level risk early warning of the refinery dynamic equipment.

[0009] Preferably, after obtaining the risk early warning data of the refinery dynamic equipment, the risk early warning for the refinery dynamic equipment further includes: determining the risk level of the refinery dynamic equipment according to the risk early warning data; and drawing risk early warning diagrams of multiple refinery dynamic equipment according to the importance degree of each refinery dynamic equipment and the risk level.

[0010] According to the second aspect of the embodiments of the present invention, there is provided a refinery dynamic equipment risk early warning system, characterized in that the refinery dynamic equipment risk early warning system includes: an acquisition unit for acquiring historical operation data and current operation data of multiple refinery dynamic equipment; a mining unit for mining several equipment status modes of the refinery dynamic equipment from the historical operation data to establish an equipment status library; a status confirmation unit for determining the current equipment status mode of the refinery dynamic equipment from the equipment status library according to the current operation data of each refinery dynamic equipment; a modeling unit for establishing an equipment risk model for several equipment status modes of the refinery dynamic equipment; and an early warning unit for performing risk early warning on the refinery dynamic equipment by using the current operation data of each refinery dynamic equipment and the equipment risk model corresponding to the current equipment status mode of the refinery dynamic equipment.

[0011] Preferably, the modeling unit includes: a data classification subunit, configured to classify the historical operation data according to several equipment status modes of the refinery dynamic equipment mined; and a modeling subunit, configured to construct an equipment risk model corresponding to each equipment status mode according to the historical operation data included in each equipment status mode.

[0012] Preferably, the warning unit includes: a data prediction unit, configured to input the current operation data of the refinery dynamic equipment into the equipment risk model corresponding to the current operation mode of the refinery dynamic equipment to obtain risk warning data of the refinery dynamic equipment; and a first warning subunit, configured to display the risk warning data with a first feature, where the first feature is used to indicate multi-level risk warning of the refinery dynamic equipment.

[0013] Preferably, the warning unit further includes: a risk level determination subunit, configured to determine the risk level of the refinery dynamic equipment according to the risk warning data after obtaining the risk warning data of the refinery dynamic equipment; and a second warning subunit, configured to draw risk warning diagrams of multiple refinery dynamic equipment according to the importance degree of each refinery dynamic equipment and the risk level.

[0014] According to a third aspect of the embodiments of the present invention, there is provided a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable the machine-readable storage medium to execute the above-mentioned refinery dynamic equipment risk warning method.

[0015] Through the above technical solutions, the present invention mines several equipment status modes of the refinery dynamic equipment from the historical operation data of multiple refinery dynamic equipment to establish an equipment status library, establishes an equipment risk model for the equipment status mode, determines the current equipment status mode according to the current operation data of each refinery dynamic equipment, uses the corresponding risk model for risk warning, and establishes an equipment risk warning model adapted to different equipment status modes according to the historical data of multiple similar equipment, effectively improving the pertinence of the risk warning model, thereby improving the accuracy and timeliness of equipment risk warning, and being able to implement technical analysis and comparison between multiple similar equipment, which is beneficial to summarizing experience and lessons according to the operation conditions of similar equipment, preventing similar risks from occurring in similar equipment, and effectively improving the operation reliability of the equipment.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and form a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the accompanying drawings:

[0018] Figure 1 is a flowchart of the risk early warning method for refinery dynamic equipment provided in the first embodiment of the present invention;

[0019] Figure 2 is a risk early warning matrix diagram for refinery dynamic equipment provided in the second embodiment of the present invention;

[0020] Figure 3 is a block diagram of the risk early warning system for refinery dynamic equipment provided in the third embodiment of the present invention;

[0021] Figure 4 is a block diagram of the modeling unit of the risk early warning system for refinery dynamic equipment provided in the fourth embodiment of the present invention;

[0022] Figure 5 is a block diagram of the early warning unit of the risk early warning system for refinery dynamic equipment provided in the fifth embodiment of the present invention;

[0023] Figure 6 is a block diagram of the early warning unit of the risk early warning system for refinery dynamic equipment provided in the sixth embodiment of the present invention;

[0024] Figure 7 is a network architecture diagram of the risk early warning system for refinery dynamic equipment provided in the seventh embodiment of the present invention; and

[0025] Figure 8 is an architecture diagram of an application example of the risk early warning system for refinery dynamic equipment provided in the eighth embodiment of the present invention.

[0026] Explanation of the reference numerals

[0027] 1. Acquisition unit 2. Mining unit

[0028] 3. Status confirmation unit 4. Modeling unit

[0029] 5. Early warning unit 41. Data classification sub-unit

[0030] 42. Modeling sub-unit 51. Data prediction unit

[0031] 52. First early warning sub-unit 53. Risk level confirmation sub-unit

[0032] 54. Second early warning sub-unit Detailed description

[0033] The following is a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0034] Embodiment 1

[0035] Figure 1 is a flow chart of a risk early warning method for refinery moving equipment provided in Embodiment 1 of the present invention. As Figure 1 shown, it may include:

[0036] S100. Obtain the operation data of multiple refinery moving equipment.

[0037] Specifically, the operation state during the operation of refinery moving equipment is affected by various factors (such as mechanical signals, equipment process parameters, equipment service life, maintenance cycle, laboratory analysis parameters, etc.). In addition, the operation conditions and characteristics of multiple similar refinery moving equipment have certain commonalities. If the operation data of similar equipment can be analyzed to summarize the operation rules, it is beneficial to conduct risk early warning and prevention for other equipment in similar refinery moving equipment. Therefore, in order to more accurately analyze the operation of refinery moving equipment for timely and accurate risk early warning, the operation data of multiple refinery moving equipment is obtained in the embodiments of the present invention, including historical operation data and current operation data.

[0038] For example, by establishing a remote cloud server within the enterprise and developing a heterogeneous interface that can adapt to the signal transmission of various models of refinery moving equipment, the status monitoring data of refinery moving equipment (such as parameters such as vibration speed value, vibration acceleration value, displacement value, etc.), production real-time data (such as parameters such as flow rate, temperature, pressure, etc.), LIMS (Laboratory Information Management System) data (such as parameters such as kinematic viscosity of lubricating oil, carbon residue, acid value, etc.) can be obtained online, and inspection data, failure / fault records, etc. of refinery moving equipment can be entered into the cloud server.

[0039] S200. Mine the equipment status patterns to establish an equipment status library.

[0040] Specifically, several equipment status patterns of refinery moving equipment are mined from the historical operation data to establish an equipment status library.

[0041] In the preferred embodiment of the present invention, the equipment status patterns include: failure status pattern, healthy status pattern, and optimal status pattern.

[0042] For example, by using big data technologies (such as clustering algorithms) to perform data mining on the historical database, an equipment fault status library based on the fault status mode of refinery moving equipment, an equipment health status library based on the health status mode of refinery moving equipment, and an equipment optimal status library based on the optimal status mode of refinery moving equipment are established.

[0043] S300. Determine the current equipment status mode of the refinery moving equipment from the equipment status library.

[0044] Specifically, according to the current operating data of each refinery moving equipment, through the classification comparison between the real-time operating status and the equipment status library, the current equipment status mode of the refinery moving equipment is intelligently determined from the equipment status library.

[0045] For example, compare the current operating data of a certain refinery moving equipment with the historical data in the equipment fault status library based on the fault status mode of this type of refinery moving equipment. If the data fitting effect is good during the comparison, it indicates that the current equipment status mode of this refinery moving equipment is the fault status mode. On the contrary, if the fitting effect is not good, then compare its current operating data with the historical data in the health status library. Similarly, if the fitting effect is still not good, then compare its current operating data with the historical data in the optimal status library. Of course, the embodiments of the present invention do not limit the order of comparing the current operating data of the refinery moving equipment with the data of the fault status mode, health status mode, and optimal status mode. Similarly, the embodiments of the present invention do not limit the comparison method either. It can simply compare some parameters of the preset current operating data and historical operating data, or use other reasonable comparison methods to determine the current status mode of the refinery moving equipment.

[0046] In the preferred embodiment of the present invention, one or more of the following algorithms are used for the equipment status mode matching operation of the refinery moving equipment: trend extrapolation prediction method, regression prediction method, Kalman filter prediction model (Kalman filter is based on the best criterion of minimum mean square error estimation), combined prediction model, neural network prediction model.

[0047] S400. Establish an equipment risk model for several equipment status modes of the refinery moving equipment.

[0048] In the preferred embodiment of the present invention, the historical operating data is classified according to several equipment status modes of the mined refinery moving equipment, and an equipment risk model corresponding to this equipment status mode is constructed according to the historical operating data included in each equipment status mode.

[0049] For example, the device status modes mined in step S200 include: fault status mode, healthy status mode, and optimal status mode. Then, data corresponding to the fault status mode, healthy status mode, and optimal status mode are respectively selected from the historical operation data of the refinery dynamic equipment. According to the characteristics of the data included in each device status mode, a suitable algorithm (for example: least squares curve fitting method) is used to construct a device risk model corresponding to the device status mode.

[0050] S500. Conduct risk early warning for the refinery dynamic equipment.

[0051] Specifically, the current operation data of each refinery dynamic equipment and the device risk model corresponding to the current device status mode of the refinery dynamic equipment are used to conduct risk early warning for the refinery dynamic equipment.

[0052] In a preferred embodiment of the present invention, the current operation data of the refinery dynamic equipment is input into the device risk model corresponding to the current operation mode of the refinery dynamic equipment to obtain risk early warning data of the refinery dynamic equipment; and the risk early warning data is displayed with a first feature, where the first feature is used to indicate the multi-level risk early warning of the refinery dynamic equipment.

[0053] For example, the risk early warning data of the refinery dynamic equipment can be displayed using one or more of various first features such as tables, texts, and graphics. When the risk early warning data of the refinery dynamic equipment reaches or exceeds the set risk threshold, an alarm can also be given by means such as a buzzer or voice reminder.

[0054] Embodiment 2

[0055] In Embodiment 2 of the present invention, after obtaining the risk early warning data of the refinery dynamic equipment, the risk early warning for the refinery dynamic equipment further includes: determining the risk level of the refinery dynamic equipment according to the risk early warning data, and drawing a risk early warning diagram of multiple refinery dynamic equipment according to the importance degree and risk level of each refinery dynamic equipment.

[0056] For example, a risk early warning diagram for multiple refinery dynamic equipment can be drawn in the form of a matrix diagram. The specific form of the matrix diagram will be Figure 2 described in detail hereinafter and will not be elaborated here.

[0057] Figure 2 is the risk early warning matrix diagram of the refinery dynamic equipment provided by Embodiment 2 of the present invention, as shown in Figure 2As shown, the importance of the equipment can be divided according to the position of the refinery moving equipment in the overall process of equipment operation in the refinery enterprise, the risk level can be divided according to the degree of deviation between the operating parameters of the refinery moving equipment and the alarm threshold, and the risk early warning of the refinery moving equipment can be carried out by combining the importance of the refinery moving equipment and the risk level. The early warning levels can be divided into no alarm, low alarm, and high alarm from low to high. The above is only an exemplary description of the risk early warning method given in the embodiments of the present invention. The division of equipment importance and risk level can be divided into more levels according to needs, and the early warning level can also be further refined. The present invention does not make any limitations on this.

[0058] For example, when the user clicks on the corresponding alarm level in the risk early warning matrix diagram of the refinery moving equipment risk early warning system, the information of the refinery moving equipment corresponding to this alarm level will pop up, which may include the operating data of the refinery moving equipment, etc.

[0059] In addition, in practical applications, different alarm levels can be displayed with pictures of different colors to play a warning role, or other appropriate ways can be used for display or alarm reminders in the form of voice.

[0060] In the preferred embodiment of the present invention, the risk early warning method for refinery moving equipment further includes automatically generating a risk early warning report for the refinery moving equipment at preset time intervals. The preset time can be set to the beginning, middle, and end of the month, and the time range of the preset time can include 15 - 45 days. The risk early warning report can include the operating parameters of the refinery moving equipment, the risk control strategy suggestions given by the refinery moving equipment risk early warning system, equipment life prediction, etc.

[0061] In the preferred embodiment of the present invention, the risk early warning method for refinery moving equipment further includes: adjusting the operating parameters of the refinery moving equipment according to the operating data of the refinery static equipment in the refinery moving equipment risk early warning diagram, and making preventive equipment maintenance.

[0062] Embodiment III

[0063] Figure 3 is a block diagram of the refinery moving equipment risk early warning system provided in Embodiment III of the present invention, as Figure 3As shown, it may include: an acquisition unit 1 for acquiring historical operation data and current operation data of multiple refining dynamic equipment; a mining unit 2 for mining several equipment status patterns of the refining dynamic equipment from the historical operation data to establish an equipment status library; a status confirmation unit 3 for determining the current equipment status pattern of the refining dynamic equipment from the equipment status library according to the current operation data of each refining dynamic equipment; a modeling unit 4 for establishing an equipment risk model for several equipment status patterns of the refining dynamic equipment; and an early warning unit 5 for performing risk early warning on the refining dynamic equipment by using the current operation data of each refining dynamic equipment and the equipment risk model corresponding to the current equipment status pattern of the refining dynamic equipment.

[0064] Embodiment 4

[0065] Figure 4 is a block diagram of the modeling unit of the refining dynamic equipment risk early warning system provided in Embodiment 4 of the present invention. As Figure 4 shown, the modeling unit 4 may include: a data classification subunit 41 for classifying the historical operation data according to several equipment status patterns of the mined refining dynamic equipment; and a modeling subunit 42 for constructing an equipment risk model corresponding to the equipment status pattern according to the historical operation data included in each equipment status pattern.

[0066] Embodiment 5

[0067] Figure 5 is a block diagram of the early warning unit of the refining dynamic equipment risk early warning system provided in Embodiment 5 of the present invention. As Figure 5 shown, the early warning unit 5 may include: a data prediction unit 51 for inputting the current operation data of the refining dynamic equipment into the equipment risk model corresponding to the current operation mode of the refining dynamic equipment to obtain risk early warning data of the refining dynamic equipment; and a first early warning subunit 52 for displaying the risk early warning data with a first feature, where the first feature is used to indicate the multi-level risk early warning of the refining dynamic equipment.

[0068] Embodiment 6

[0069] Figure 6 is a block diagram of the early warning unit of the refining dynamic equipment risk early warning system provided in Embodiment 6 of the present invention. As Figure 6 shown, the early warning unit may further include: a risk level determination subunit 53 for determining the risk level of the refining dynamic equipment according to the risk early warning data after obtaining the risk early warning data of the refining dynamic equipment; and a second early warning subunit 54 for drawing a risk early warning map of multiple refining dynamic equipment according to the importance degree and risk level of each refining dynamic equipment.

[0070] For the specific implementation details and beneficial effects of the risk warning system for refinery moving equipment, refer to the risk warning method for refinery moving equipment, which will not be elaborated here.

[0071] Embodiment Seven

[0072] Figure 7 It is a network architecture diagram of an application example of the risk warning system for refinery moving equipment provided by Embodiment Seven of the present invention. As Figure 7 shown, the risk warning system for refinery moving equipment is set in the remote status monitoring center. Generally, the remote status monitoring center is set up based on enterprises to obtain the equipment operation data of the refinery moving equipment of multiple subsidiaries of the enterprise, including production real-time data (such as parameters like flow rate, temperature, pressure, etc.) obtained from the production database system, analysis data (such as parameters like lubricating oil viscosity, carbon residue, acid value, etc.) obtained from the LIMS (Laboratory Information Management System) system, pump status data obtained from the pump status monitoring system, and large unit status data obtained from the large unit status monitoring system. And analyze and process them, and compare the refinery moving equipment operation data among each branch company. Then, mutual learning can be carried out according to the equipment operation and management methods of each branch company, which is beneficial to improving the management level and further extending the equipment life.

[0073] Embodiment Eight

[0074] Figure 8 It is an architecture diagram of an application example of the risk warning system for refinery moving equipment provided by Embodiment Eight of the present invention. As Figure 8 shown, the risk warning system for refinery moving equipment is set on the server of the enterprise internal status monitoring data center, and is used to obtain the equipment operation data of the pump status detection system, large unit status monitoring system, enterprise production real-time database, LIMS database, and equipment patrol database.

[0075] According to the historical operation data of multiple refinery moving equipment obtained from the above databases, three equipment operation modes, namely the fault status mode, health status mode, and optimal status mode, are mined, and the historical operation data of the refinery moving equipment are classified according to the above equipment operation status modes. According to the historical data corresponding to each equipment operation mode, the corresponding fault status model, health status model, and optimal status model are established. According to the real-time operation data of each equipment, its equipment operation mode is determined, and its real-time operation data is input into the corresponding equipment operation status model to obtain equipment risk warning data, and risk warning is carried out in the form of a rotating equipment risk warning diagram.

[0076] Referring to the equipment operation data included in the risk warning diagram of rotating equipment, adjustment operations of operation parameters and preventive maintenance operations of the equipment can be carried out. In addition, the risk warning system for refinery rotating equipment can predict the equipment life and issue equipment risk warnings (such as alarms, and sending the corresponding equipment risks to the equipment management personnel by means of WeChat and / or short messages, etc.) according to the equipment operation data included in the risk warning diagram of rotating equipment.

[0077] It should be noted that the rotating equipment mentioned in the embodiments of the present invention is the refinery rotating equipment.

[0078] Through the above technical solutions, the present invention mines several equipment state modes of refinery rotating equipment from the historical operation data of multiple refinery rotating equipment to establish an equipment state library, establishes an equipment risk model for the equipment state modes, determines the current equipment state mode according to the current operation data of each refinery rotating equipment, uses the corresponding risk model to issue risk warnings, and establishes an equipment risk warning model adapted to different equipment state modes according to the historical data of multiple similar equipment, effectively improving the pertinence of the risk warning model, thereby improving the accuracy and timeliness of equipment risk warnings, and being able to realize technical analysis and comparison among multiple similar equipment, which is conducive to summarizing experience and lessons according to the operation conditions of similar equipment, preventing similar risks from occurring in similar equipment, and effectively reducing the equipment operation risk.

[0079] The optional implementation manners of the embodiments of the present invention have been described in detail above with reference to the drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation manners. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

[0080] In addition, it should be noted that, in the case of no contradiction, the various specific technical features described in the above specific implementation manners can be combined in any appropriate manner. To avoid unnecessary repetition, the embodiments of the present invention will not separately describe various possible combination manners.

[0081] Those skilled in the art can understand that all or part of the steps of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a program, and the program is stored in a storage medium, including several instructions for causing a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the methods in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks that can store program codes.

[0082] In addition, any combination can be made among various different embodiments of the embodiments of the present invention, as long as it does not violate the idea of the embodiments of the present invention, and it should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A risk early warning method for refinery moving equipment, characterized in that, the risk early warning method for refinery moving equipment includes: Online obtain the historical operation data and current operation data of multiple refinery moving equipment belonging to multiple enterprises through the heterogeneous interface of the remote cloud server within the enterprise, wherein the historical operation data includes one or more of the following: status monitoring data, production real-time data, LIMS data, inspection data, failure records, pump status data, large unit status data; Mine several equipment status patterns of the refinery moving equipment from the historical operation data to establish an equipment status library, wherein the several equipment status patterns include: fault status pattern, healthy status pattern, optimal status pattern; According to the current operation data of each refinery moving equipment, respectively determine the comparison fitting degrees with the equipment status library in the fault status pattern, the healthy status pattern, and the optimal status pattern by means of matching and comparison, and determine the equipment status pattern with the largest comparison fitting degree in the equipment status library as the current equipment status pattern of the refinery moving equipment; Establish an equipment risk model for several equipment status patterns of the refinery moving equipment; and Use the current operation data of each refinery moving equipment and the equipment risk model corresponding to the current equipment status pattern of the refinery moving equipment to conduct risk early warning on the refinery moving equipment.

2. The risk early warning method for refinery moving equipment according to claim 1, characterized in that, The establishment of an equipment risk model for several equipment status patterns of the refinery moving equipment includes: Classify the historical operation data according to the several equipment status patterns of the refinery moving equipment mined; and Construct an equipment risk model corresponding to the equipment status pattern according to the historical operation data included in each equipment status pattern.

3. The risk early warning method for refinery moving equipment according to claim 1, characterized in that, The equipment risk models corresponding to the equipment status patterns are respectively a fault status risk model, a healthy status risk model, and an optimal status risk model.

4. The risk early warning method for refinery moving equipment according to claim 1, characterized in that, The conduct of risk early warning on the refinery moving equipment includes: Input the current operation data of the refinery moving equipment into the equipment risk model corresponding to the current operation mode of the refinery moving equipment to obtain the risk early warning data of the refinery moving equipment; and Display the risk early warning data with a first feature, and the first feature is used to indicate the multi-level risk early warning of the refinery moving equipment.

5. The risk early warning method for refinery moving equipment according to claim 4, characterized in that, After obtaining the risk early warning data of the refinery moving equipment, the conduct of risk early warning on the refinery moving equipment further includes: Determine the risk level of the refinery moving equipment according to the risk early warning data; Draw a risk early warning map of multiple refinery moving equipment according to the importance degree and the risk level of each refinery moving equipment.

6. A risk early warning system for refinery moving equipment, It is characterized in that the risk early warning system for refinery moving equipment includes: an acquisition unit, configured to obtain online the historical operation data and current operation data of a plurality of refinery moving equipment belonging to multiple enterprises through the heterogeneous interface of the remote cloud server within the enterprise, wherein the historical operation data includes one or more of the following: status monitoring data, production real-time data, LIMS data, inspection data, failure records, pump status data, large unit status data; a mining unit, configured to mine a plurality of equipment status patterns of the refinery moving equipment from the historical operation data to establish an equipment status library, wherein the plurality of equipment status patterns include: a fault status pattern, a healthy status pattern, and an optimal status pattern; a status confirmation unit, configured to respectively determine the comparison fitting degrees with the equipment status library in the fault status pattern, the healthy status pattern, and the optimal status pattern for each refinery moving equipment according to the current operation data of each refinery moving equipment by means of matching and comparison, and determine the equipment status pattern with the largest comparison fitting degree in the equipment status library as the current equipment status pattern of the refinery moving equipment; a modeling unit, configured to establish an equipment risk model for a plurality of equipment status patterns of the refinery moving equipment; and an early warning unit, configured to perform risk early warning on the refinery moving equipment by using the current operation data of each refinery moving equipment and the equipment risk model corresponding to the current equipment status pattern of the refinery moving equipment.

7. The risk early warning system for refinery moving equipment according to claim 6, It is characterized in that the modeling unit includes: a data classification subunit, configured to classify the historical operation data according to the plurality of equipment status patterns of the refinery moving equipment mined; and a modeling subunit, configured to construct an equipment risk model corresponding to the equipment status pattern according to the historical operation data included in each equipment status pattern.

8. The risk early warning system for refinery moving equipment according to claim 6, It is characterized in that the early warning unit includes: a data prediction unit, configured to input the current operation data of the refinery moving equipment into the equipment risk model corresponding to the current operation mode of the refinery moving equipment to obtain risk early warning data of the refinery moving equipment; and a first early warning subunit, configured to display the risk early warning data with a first feature, and the first feature is used to indicate the multi-level risk early warning of the refinery moving equipment.

9. The risk early warning system for refinery moving equipment according to claim 8, It is characterized in that the early warning unit further includes: a risk level determination subunit, configured to determine the risk level of the refinery moving equipment according to the risk early warning data after obtaining the risk early warning data of the refinery moving equipment; a second early warning subunit, configured to draw a risk early warning map of a plurality of refinery moving equipment according to the importance degree and the risk level of each refinery moving equipment.

10. A machine-readable storage medium, It is characterized in that Instructions are stored on the machine-readable storage medium, and the instructions are used to enable the machine-readable storage medium to execute the risk warning method for refinery dynamic equipment according to any one of claims 1-5.

Images