Method for determining risk of refining device, processor and storage medium

Abstract

The invention relates to the field of chemical risks, and discloses a method for determining the risk of a refining device, a processor and a storage medium. The method comprises the following steps: acquiring process parameter data of each process point location of the refining device; determining a corresponding first threshold value of upper limit alarm, a second threshold value of lower limit alarm and a first base point coefficient according to the type of the process parameter data; determining a first difference value between the first threshold value and the second threshold value; determining a first calculation base point according to the product of the first difference value and the first base point coefficient; determining a second difference value between the process parameter data and the first calculation base point and a third difference value between the first threshold value and the first calculation base point; determining a process risk index of a process point location corresponding to the process parameter data according to a ratio of the second difference value to the third difference value; and determining the process risk of the refining device according to each process risk index of each process point location. The scientific and accurate risk calculation method for the refining device is formulated, and risk early warning can be carried out on the refining device more accurately.

Description

Technical Field

[0001] The present invention relates to the field of chemical industry risks, and in particular to a method, a processor and a storage medium for determining the risks of a refining device. Background Art

[0002] The hazardous chemicals industry is developing rapidly, but the situation of hazardous chemicals production safety is still complex and severe, and the risk control of chemical plants is still at a level to be developed. In recent years, accidents involving hazardous chemicals have occurred frequently, involving many links and geographical dispersion. If the risk index of chemical plants cannot be accurately determined and risk warnings for chemical plants cannot be issued in a timely manner, major fire and explosion accidents may occur, resulting in personal injury and major property losses. At present, the company's own safety issues, management issues, government supervision issues, laws, regulations and standards issues, and systemic risk issues have emerged one after another. Take the refining plant as an example. The refining plant has flammable and explosive substances and high-temperature and high-pressure process operations. The refining plant is very prone to fire, explosion, personal injury and other accidents. At present, the risk prediction of refining plants mainly relies on manual experience management, and the accuracy of risk calculation of refining plants is low. Summary of the invention

[0003] In order to overcome the deficiencies in the prior art, an embodiment of the present invention provides a method, a processor, and a storage medium for determining the risk of a refinery.

[0004] In order to achieve the above object, the first aspect of the present invention provides a method for determining the risk of a refinery, comprising:

[0005] Obtain process parameter data of each process point of the refining unit;

[0006] Determine the first threshold of the upper limit alarm, the second threshold of the lower limit alarm and the first base point coefficient according to the type of process parameter data;

[0007] determining a first difference between a first threshold and a second threshold;

[0008] Determine a first calculation base point according to the product of the first difference and the first base point coefficient;

[0009] Determine a second difference between the process parameter data and the first calculation base point, and a third difference between the first threshold and the first calculation base point;

[0010] Determine a process risk index of a process point corresponding to the process parameter data according to a ratio of the second difference to the third difference;

[0011] The process risk of the refining unit is determined based on the process risk index of each process point.

[0012] In an embodiment of the present invention, the refining device includes a normal pressure reducing device; and the process parameter data includes at least one of the following:

[0013] The set current of the electric desalting tank transformer in the atmospheric and vacuum device, the flow rate of the furnace tube of the heating furnace in the atmospheric and vacuum device, the pump outlet flow rate at the bottom of the atmospheric pressure tower in the atmospheric and vacuum device, the pressure of the primary distillation tower in the atmospheric and vacuum device, the furnace temperature of the heating furnace in the atmospheric and vacuum device, the furnace tube temperature in the atmospheric and vacuum device, the tower top temperature of the vacuum tower in the atmospheric and vacuum device, and the furnace outlet temperature of the atmospheric pressure furnace in the atmospheric and vacuum device.

[0014] In an embodiment of the present invention, the method further includes:

[0015] Obtain equipment parameter data of each equipment point of the refining unit;

[0016] Determine the corresponding third threshold of the upper limit alarm, the fourth threshold of the lower limit alarm and the second base point coefficient according to the type of the equipment parameter data;

[0017] determining a fourth difference between the third threshold and the fourth threshold;

[0018] Determine a second calculation base point according to the product of the fourth difference and the second base point coefficient;

[0019] Determine a fifth difference between the device parameter data and the second calculation base point, and a sixth difference between the third threshold and the second calculation base point;

[0020] Determine the equipment risk index of the equipment point corresponding to the equipment parameter data according to the ratio of the fifth difference to the sixth difference;

[0021] The equipment risk of the refining unit is determined based on the equipment risk index of each equipment point.

[0022] In an embodiment of the present invention, the method further includes:

[0023] The greater of the process risk and the equipment risk is used to determine the risk index of the refining unit.

[0024] In an embodiment of the present invention, the refining device includes a normal pressure reducing device; and the equipment parameter data includes at least one of the following:

[0025] The pressure of the sealing oil tank of the primary bottom pump in the constant pressure reduction device, the liquid level of the sealing oil tank of the primary bottom pump in the constant pressure reduction device, and the vibration data of the pump and compressor in the constant pressure reduction device.

[0026] In an embodiment of the present invention, determining the equipment risk of a refinery according to each equipment risk index of each equipment point includes:

[0027] Classify the equipment risk index according to the type of equipment parameter data to obtain multiple categories of equipment risk index;

[0028] The maximum value of the equipment risk index of each category is used as the target equipment risk index of that category;

[0029] Obtain the weight value corresponding to each category of equipment risk index;

[0030] The equipment risk of the refining unit is determined based on the sum of the products of the target equipment risk index of each category and the corresponding weight value.

[0031] In the embodiment of the present invention, the frequency range for acquiring process parameter data or equipment parameter data is 0.8 Hz to 1.2 Hz.

[0032] In an embodiment of the present invention, determining the process risk of a refinery according to each process risk index of each process point includes:

[0033] The process risk index is classified according to the type of process parameter data to obtain multiple categories of process risk index;

[0034] The maximum value of the process risk index of each category is taken as the target process risk index of that category;

[0035] Obtain the weight value corresponding to each category of process risk index;

[0036] The process risk of the refining unit is determined based on the sum of the products of the target risk index of each category and the corresponding weight value.

[0037] A second aspect of the present invention provides a processor configured to execute the above-mentioned method for determining the risk of a refinery.

[0038] A third aspect of the present invention provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the above-mentioned method for determining the risk of a refinery.

[0039] In an embodiment of the present invention, a method for determining the risk of a refining device includes: obtaining process parameter data of each process point of the refining device; determining the corresponding first threshold of the upper limit alarm, the second threshold of the lower limit alarm and the first base point coefficient according to the type of process parameter data; determining the first difference between the first threshold and the second threshold; determining the first calculation base point according to the product of the first difference and the first base point coefficient; determining the second difference between the process parameter data and the first calculation base point, and the third difference between the first threshold and the first calculation base point; determining the process risk index of the process point corresponding to the process parameter data according to the ratio of the second difference to the third difference; determining the process risk of the refining device according to each process risk index of each process point. In this way, a scientific and accurate method for calculating the risk of a refining device is formulated, which can more accurately warn the risk of the refining device, and formulate corresponding control measures in a timely manner to effectively prevent accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0041] Figure 1 A flow chart of a method for determining the risk of a refinery according to an embodiment of the present invention is schematically shown;

[0042] Figure 2 A diagram schematically showing a calculation component diagram of the risk of a refinery according to an embodiment of the present invention;

[0043] Figure 3 The following schematically shows a flow chart for calculating the risk index of a refinery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0045] It should be noted that if the implementation methods of the present application involve directional indications (such as up, down, left, right, front, back...), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0046] In addition, if there are descriptions involving "first", "second", etc. in the implementation methods of this application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various implementation methods can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0047] Figure 1 A flow chart of a method for determining the risk of a refinery according to an embodiment of the present invention is schematically shown. Figure 1 As shown, in one embodiment of the present invention, a method for determining the risk of a refinery is provided, comprising the following steps:

[0048] Step 101, obtaining process parameter data of each process point of the refining device;

[0049] Step 102, determining a first threshold of an upper limit alarm, a second threshold of a lower limit alarm, and a first base point coefficient according to the type of process parameter data;

[0050] Step 103, determining a first difference between the first threshold and the second threshold;

[0051] Step 104, determining a first calculation base point according to the product of the first difference and the first base point coefficient;

[0052] Step 105, determining a second difference between the process parameter data and the first calculation base point, and a third difference between the first threshold and the first calculation base point;

[0053] Step 106, determining a process risk index of a process point corresponding to the process parameter data according to a ratio of the second difference to the third difference;

[0054] Step 107, determining the process risk of the refinery according to each process risk index of each process point.

[0055] Taking the refining unit as the atmospheric and vacuum unit and the process parameter data as the tower top pressure for example, for example, the first threshold of the upper limit alarm of the tower top pressure of the atmospheric and vacuum unit is 140kpa, the second threshold of the lower limit alarm is 1kpa, the first base point coefficient is 0.618, and the current real-time monitoring value of the tower top pressure process parameter data is 102kpa. For the tower top pressure, the calculation process of the process risk index of the atmospheric and vacuum unit process point is as follows:

[0056] Calculate the first calculation base point p: p = (140-1) * 0.618, p = 85.902.

[0057] Calculate the process risk index r of this process point: r = (102-p) / (140-p) = 0.298.

[0058] The refining unit includes multiple process points and corresponding process parameter data. After calculating the process risk index of each process point, the process risk of the refining unit is determined according to the process risk index of each process point. In this way, a scientific and accurate method for calculating the risk of the refining unit is developed, which can more accurately warn of the risk of the refining unit, and formulate corresponding control measures in time to effectively prevent accidents.

[0059] In one embodiment, if the current real-time monitoring value of the process parameter data exceeds the first threshold of the upper limit alarm or the second threshold of the lower limit alarm, the process risk index of the corresponding process point can be calculated as 1.

[0060] In one embodiment, the refining device includes a normal pressure reduction device; the process parameter data includes at least one of the following: the set current of the electric desalting tank transformer in the normal pressure reduction device, the flow rate of the heating furnace tube in the normal pressure reduction device, the pump outlet flow rate at the bottom of the atmospheric pressure tower in the normal pressure reduction device, the pressure of the primary distillation tower in the normal pressure reduction device, the furnace temperature of the heating furnace in the normal pressure reduction device, the furnace tube temperature in the normal pressure reduction device, the tower top temperature of the vacuum tower in the normal pressure reduction device, and the furnace outlet temperature of the atmospheric pressure furnace in the normal pressure reduction device. The data collection of the refining device is more comprehensive and sufficient.

[0061] The calculation of the process risk index of the process point has been explained in detail above by taking the process parameter data of the tower top pressure as an example. The calculation process of the process risk index of other process points of the refining unit is similar and will not be repeated here.

[0062] In one embodiment, the method also includes: acquiring equipment parameter data of each equipment point of the refining device; determining the corresponding third threshold of the upper limit alarm, the fourth threshold of the lower limit alarm and the second base point coefficient according to the type of the equipment parameter data; determining the fourth difference between the third threshold and the fourth threshold; determining the second calculation base point according to the product of the fourth difference and the second base point coefficient; determining the fifth difference between the equipment parameter data and the second calculation base point, and the sixth difference between the third threshold and the second calculation base point; determining the equipment risk index of the equipment point corresponding to the equipment parameter data according to the ratio of the fifth difference to the sixth difference; and determining the equipment risk of the refining device according to each equipment risk index of each equipment point.

[0063] In one embodiment, the frequency range of acquiring process parameter data or equipment parameter data is 0.8 Hz to 1.2 Hz. The data acquisition frequency can be set according to needs and actual conditions, and for example, it can be acquired once per second.

[0064] In one embodiment, the refinery includes a vacuum device; the equipment parameter data includes at least one of the following: the pressure of the primary bottom pump sealing oil tank in the vacuum device, the liquid level of the primary bottom pump sealing oil tank in the vacuum device, and the vibration data of the pump and compressor in the vacuum device. The data collection of the refinery is more comprehensive and sufficient.

[0065] The refining unit also includes multiple equipment points and corresponding equipment parameter data. The calculation process of the equipment risk index of the equipment point is similar to the calculation process of the process risk index of the process point, which will not be repeated here. After calculating the equipment risk index of each equipment point, the equipment risk of the refining unit is determined according to the equipment risk index of each equipment point.

[0066] In one embodiment, determining the process risk of the refining unit based on each process risk index of each process point includes: classifying the process risk index according to the type of process parameter data to obtain multiple categories of process risk index; taking the maximum value of the process risk index of each category as the target process risk index of the category; obtaining the weight value corresponding to the process risk index of each category; and determining the process risk of the refining unit based on the sum of the products of the target risk index of each category and the corresponding weight value.

[0067] In one embodiment, determining the equipment risk of the refining device based on each equipment risk index of each equipment point includes: classifying the equipment risk index according to the type of equipment parameter data to obtain multiple categories of equipment risk index; taking the maximum value of the equipment risk index of each category as the target equipment risk index of the category; obtaining the weight value corresponding to the equipment risk index of each category; and determining the equipment risk of the refining device based on the sum of the products of the target equipment risk index of each category and the corresponding weight value.

[0068] Equipment points and process points can be collectively referred to as monitoring points, and process risk indexes and equipment risk indexes can be collectively referred to as monitoring risk indexes. After the monitoring risk indexes of all monitoring points are obtained, the process risk indexes are classified according to the types of process parameter data. For example, they can be classified according to temperature, pressure, liquid level, flow rate, and current. Equipment risk indexes are classified according to the types of equipment parameter data. For example, they can be classified according to pressure, liquid level, and vibration data.

[0069] For example, it is assumed that the refinery has five categories of process risk indexes, and the target process risk indexes of each category are 0.256, 0.184, 0.643, 0.375, and 0.136. In this way, the process risk index matrix z of the process points of the refinery is formed:

[0070] z=[0.256,0.184,0.643,0.375,0.136]

[0071] For example, it is assumed that the refinery has three types of equipment risk indexes, and the target equipment risk indexes of each type are 0.216, 0.144, and 0.347. In this way, the equipment risk index matrix y of the equipment points of the refinery is formed:

[0072] y=[0.216,0.144,0.347]

[0073] The weight values ​​corresponding to each category of process risk index and each category of equipment risk index can be obtained by direct empirical method. For example, assuming that the weight values ​​corresponding to the five categories of process risk index are 0.3, 0.15, 0.1, 0.2, and 0.25, respectively, the weight matrix c corresponding to the process risk index is formed:

[0074] c = [0.3, 0.15, 0.1, 0.2, 0.25]

[0075] For example, assuming that the weight values ​​corresponding to the three categories of equipment risk index are 0.3, 0.3, and 0.4, respectively, the weight matrix d corresponding to the equipment risk index is formed as follows:

[0076] d = [0.3, 0.3, 0.4]

[0077] Then transpose the weight matrix c and weight matrix d respectively to obtain: c T d T .

[0078] Process risk of refining unit R1=z*c T ,Right now

[0079] R1=[0.256,0.184,0.643,0.375,0.136]*[0.3,0.15,0.1,0.2,0.25] T

[0080] The calculated value is R1 = 0.278. Therefore, in this example, the process risk of the refinery is 0.278.

[0081] Equipment risk of refining unit R2=y*d T ,Right now

[0082] R2=[0.216,0.144,0.347]*[0.3,0.3,0.4] T

[0083] The calculated R2 = 0.247. Therefore, in this example, the equipment risk of the refinery is 0.278.

[0084] As described above, the process risk and equipment risk of the refining device are scientifically calculated and accurately quantified. In one embodiment, the method further includes: determining the risk index of the refining device by the larger one of the process risk and the equipment risk. Exemplarily, the risk index of the refining device R = max (R1, R2) = 0.278. Since the frequency range of obtaining process parameter data or equipment parameter data is 0.8Hz to 1.2Hz, the data collection frequency can be set according to demand and actual conditions. Exemplarily, it can be collected once per second, so that the risk index of the refining device can be understood as a real-time risk index, that is, the risk of the refining device can be determined in real time. In this way, a scientific and accurate method for calculating the risk of the refining device is formulated, which can more accurately warn the risk of the refining device, identify risks in a timely manner and formulate corresponding control measures, and effectively prevent accidents.

[0085] In the embodiment of the present invention, the data collection work mainly collects the process parameter data and equipment parameter data of the refining device through the multi-element data collection gateway device. The process parameter data and equipment parameter data can be understood as online real-time monitoring data. The process parameter data and equipment parameter data are integrated to achieve data fusion and integrated storage.

[0086] As for the data acquisition device, it may include an RS-232 to modbus device, an intranet host, a one-way optical shutter, an extranet host and a communication program.

[0087] Step 1: Design the RS-232 to modbus device, and convert the industrial control network signal to modbus signal through RS-232;

[0088] Analog quantity to Modbus device is mainly responsible for connecting the enterprise's safety barrier or instrument to convert analog quantity into Modbus protocol. Enterprises do not need to purchase Modbus modules, thus saving enterprise transformation costs;

[0089] Step 2: Parse the modbus signal into an intranet host signal; convert the acquired modbus signal into an intranet host signal through a data conversion module;

[0090] Step 3: The intranet host signal is transmitted to the external network host through a one-way optical gate;

[0091] The internal network exchange host aggregates all the internal networks collected, and configures network security through the firewall function to prevent network paths from intersecting, and transmits data to the internal network host;

[0092] The intranet host collects the collected data and implements functions such as automatic threshold acquisition and manual configuration, real-time alarm triggering, edge computing, coding configuration, and calculation of data confidentiality models;

[0093] One-way optical gate transmits the data transmitted in the intranet to the external host. The one-way optical gate has only a transmission optical port on the intranet side and no receiving optical port, while the external side has only a receiving optical port and no transmission port, thus eliminating the possibility of data being transmitted back from the external host. Through the built-in optical gate, one-way transmission of internal and external network data is achieved to ensure the data security of the industrial control system.

[0094] Step 4: The external host connects to the Internet for communication.

[0095] The external network host distributes the acquired data through a private protocol and implements many-to-many optional upload; the external network exchange host aggregates all uploaded networks and configures network security through the firewall function to prevent network paths from being connected, and transmits data to the internal network host; communication and edge computing software, the software system encrypts and packages the data, performs edge computing, alarm judgment, encoding and other functions.

[0096] The deployed data acquisition device is connected to the PLC (Programmable Logic Controller) workstation through the RS-232 port; it can also be connected to the DCS (Distributed Control System) of the device through the OPC (Object Linking and Embedding (OLE) for Process Control) protocol. After the data is connected, the process parameter data and equipment parameter data of the chemical plant (refining plant) are collected, and the industrial control network data is safer.

[0097] Figure 2 A diagram schematically shows a calculation component diagram of the risk of a refinery according to an embodiment of the present invention, which can be seen in Figure 2 , mainly including data acquisition layer, model calculation layer and interface service layer.

[0098] The data acquisition layer mainly includes: process parameter data acquisition module 19, equipment parameter data acquisition module 20, OPC data acquisition module 21, modbus data acquisition module 22, and data point setting module 23. Process parameter data acquisition module 19: completes the real-time data acquisition function of the process points of the refining device. Equipment parameter data acquisition module 20: completes the real-time data acquisition function of the equipment points of the refining device. OPC data acquisition module 21: OPC data acquisition mainly connects the OPC service of the device DCS system through the data acquisition device, and collects the real-time data and A&E event data of the device through OPC-DA and OPC-A&E protocols. Modbus data acquisition module 22: connects to the host hardware interface of the PLC or the device data acquisition interface through the modbus data interface or RS-232 port to obtain relevant real-time data. Data point setting module 23: realizes the binding of data points and device points, and provides basic data for real-time data acquisition of each monitoring point.

[0099] The model calculation layer mainly includes: process risk index calculation module 13, equipment risk index calculation module 14, device risk index calculation module 15, process parameter data preprocessing module 16, device parameter data preprocessing module 17, and data normalization module 18. Process risk index calculation module 13: completes the calculation of process risk index. Equipment risk index calculation module 14: completes the calculation of equipment risk index. Device risk index calculation module 15: completes the calculation of risk index of refining device; Figure 3 A flow chart schematically shows the calculation of the risk index of a refinery according to an embodiment of the present invention, which can be found in Figure 3 . Process parameter data preprocessing module 16: mainly realizes the preprocessing of real-time monitoring data of process parameter data, mainly including the error value processing and missing value processing of process parameter data. Equipment parameter data preprocessing module 17: mainly realizes the preprocessing of real-time monitoring data of equipment parameter data, mainly including the error value processing and missing value processing of equipment parameter data. Data normalization module 18: completes the normalization processing function of process parameter data and equipment parameter data.

[0100] The interface service layer mainly includes: a process risk index interface service module 10, an equipment risk index interface service module 11, and a device risk index interface service module 12. In one embodiment, the interface service includes three forms: API (Application Programming Interface), json (JavaScript Object Notation, JS object notation) interface and XML interface for other systems to call. Process risk index interface service module 10: provides process risk index interface service, and the external system can obtain the calculation result of the process risk index through this service. Equipment risk index interface service module 11: provides equipment risk index interface service, and the external system can obtain the calculation result of the equipment risk index through this service. Device risk index interface service module 12: provides device (refining device) risk index interface service, and the external system can obtain the calculation result of the device risk index through this service.

[0101] In the risk calculation process of the refining and chemical unit, the hardware involved can be used as an edge computing terminal. The edge computing terminal can be built into the industrial mechanism model to improve the model calculation efficiency and realize data calculation services through the terminal. The risk calculation components of the refining and chemical unit mainly include the data acquisition layer, the model calculation layer and the interface service layer, which can realize the integrated integration of data acquisition and data model calculation within the system; it can solve the problems of difficult collection of real-time data of the process and equipment operation (i.e., process parameter data and equipment parameter data) of the refining and chemical unit, difficult data fusion and low efficiency of data model calculation; and it can support the scenario application of "Industrial Internet + Hazardous Chemical Safety Production". By collecting process parameter data and equipment parameter data online in real time, and calculating the process risk and equipment risk of the refining and chemical unit, the risk index of the refining and chemical unit can be determined; it can solve the problem that the process risk cannot be quantified and calculated in real time, the equipment risk cannot be quantified and calculated in real time, and the risk index of the refining and chemical unit cannot be quantified and calculated in real time.

[0102] An embodiment of the present invention provides a processor configured to execute any one of the methods for determining the risk of a refinery in the above embodiments.

[0103] Specifically, the processor may be configured to:

[0104] Obtain process parameter data of each process point of the refining unit;

[0105] Determine the first threshold of the upper limit alarm, the second threshold of the lower limit alarm and the first base point coefficient according to the type of process parameter data;

[0106] determining a first difference between a first threshold and a second threshold;

[0107] Determine a first calculation base point according to the product of the first difference and the first base point coefficient;

[0108] Determine a second difference between the process parameter data and the first calculation base point, and a third difference between the first threshold and the first calculation base point;

[0109] Determine a process risk index of a process point corresponding to the process parameter data according to a ratio of the second difference to the third difference;

[0110] The process risk of the refining unit is determined based on the process risk index of each process point.

[0111] In an embodiment of the present invention, the refining device includes a normal pressure reducing device; and the processor is configured to:

[0112] The process parameter data includes at least one of the following:

[0113] The set current of the electric desalting tank transformer in the atmospheric and vacuum device, the flow rate of the furnace tube of the heating furnace in the atmospheric and vacuum device, the pump outlet flow rate at the bottom of the atmospheric pressure tower in the atmospheric and vacuum device, the pressure of the primary distillation tower in the atmospheric and vacuum device, the furnace temperature of the heating furnace in the atmospheric and vacuum device, the furnace tube temperature in the atmospheric and vacuum device, the tower top temperature of the vacuum tower in the atmospheric and vacuum device, and the furnace outlet temperature of the atmospheric pressure furnace in the atmospheric and vacuum device.

[0114] In this embodiment of the present invention, the processor is further configured to:

[0115] Obtain equipment parameter data of each equipment point of the refining unit;

[0116] Determine the corresponding third threshold of the upper limit alarm, the fourth threshold of the lower limit alarm and the second base point coefficient according to the type of the equipment parameter data;

[0117] determining a fourth difference between the third threshold and the fourth threshold;

[0118] Determine a second calculation base point according to the product of the fourth difference and the second base point coefficient;

[0119] Determine a fifth difference between the device parameter data and the second calculation base point, and a sixth difference between the third threshold and the second calculation base point;

[0120] Determine the equipment risk index of the equipment point corresponding to the equipment parameter data according to the ratio of the fifth difference to the sixth difference;

[0121] The equipment risk of the refining unit is determined based on the equipment risk index of each equipment point.

[0122] In this embodiment of the present invention, the processor is further configured to:

[0123] The greater of the process risk and the equipment risk is used to determine the risk index of the refining unit.

[0124] In an embodiment of the present invention, the refining device includes a normal pressure reducing device; and the processor is configured to:

[0125] The device parameter data includes at least one of the following:

[0126] The pressure of the sealing oil tank of the primary bottom pump in the constant pressure reduction device, the liquid level of the sealing oil tank of the primary bottom pump in the constant pressure reduction device, and the vibration data of the pump and compressor in the constant pressure reduction device.

[0127] In an embodiment of the present invention, the processor is configured to:

[0128] The equipment risks of the refining unit are determined based on the equipment risk index of each equipment point, including:

[0129] Classify the equipment risk index according to the type of equipment parameter data to obtain multiple categories of equipment risk index;

[0130] The maximum value of the equipment risk index of each category is used as the target equipment risk index of that category;

[0131] Obtain the weight value corresponding to each category of equipment risk index;

[0132] The equipment risk of the refining unit is determined based on the sum of the products of the target equipment risk index of each category and the corresponding weight value.

[0133] In this embodiment of the present invention, the processor is further configured to:

[0134] The frequency range for acquiring process parameter data or equipment parameter data is 0.8 Hz to 1.2 Hz.

[0135] In this embodiment of the present invention, the processor is further configured to:

[0136] The process risks of the refining unit are determined based on the process risk index of each process point, including:

[0137] The process risk index is classified according to the type of process parameter data to obtain multiple categories of process risk index;

[0138] The maximum value of the process risk index of each category is taken as the target process risk index of that category;

[0139] Obtain the weight value corresponding to each category of process risk index;

[0140] The process risk of the refining unit is determined based on the sum of the products of the target risk index of each category and the corresponding weight value.

[0141] An embodiment of the present invention provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute any one of the methods for determining the risk of a refinery in the above embodiments.

[0142] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0146] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0147] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0148] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0149] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0150] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining the risk of a refinery, characterized in that: include: Acquiring process parameter data of each process point of the refining device; Determine a first threshold value for an upper limit alarm, a second threshold value for a lower limit alarm, and a first base point coefficient according to the type of the process parameter data; determining a first difference between the first threshold and the second threshold; Determine a first calculation base point according to the product of the first difference and the first base point coefficient; Determine a second difference between the process parameter data and the first calculation base point, and a third difference between the first threshold and the first calculation base point; Determining a process risk index of a process point corresponding to the process parameter data according to a ratio of the second difference to the third difference; The process risk of the refining unit is determined according to each process risk index of each process point.

2. The method according to claim 1, characterized in that The refining device includes a normal pressure reducing device; the process parameter data includes at least one of the following: The setting current of the electric desalting tank transformer in the constant pressure reduction device, the flow rate of the furnace tube of the heating furnace in the constant pressure reduction device, the pump outlet flow rate at the bottom of the atmospheric pressure tower in the constant pressure reduction device, the pressure of the primary distillation tower in the constant pressure reduction device, the furnace temperature of the heating furnace in the constant pressure reduction device, the furnace tube temperature in the constant pressure reduction device, the tower top temperature of the pressure reducing tower in the constant pressure reduction device, and the furnace outlet temperature of the atmospheric pressure furnace in the constant pressure reduction device.

3. The method according to claim 1, characterized in that The method further comprises: Acquire equipment parameter data of each equipment point of the refining device; Determine the corresponding third threshold value of the upper limit alarm, the fourth threshold value of the lower limit alarm and the second base point coefficient according to the type of the device parameter data; determining a fourth difference between the third threshold and the fourth threshold; Determine a second calculation base point according to the product of the fourth difference and the second base point coefficient; Determine a fifth difference between the device parameter data and the second calculation base point, and a sixth difference between the third threshold and the second calculation base point; Determine the equipment risk index of the equipment point corresponding to the equipment parameter data according to the ratio of the fifth difference to the sixth difference; The equipment risk of the refining device is determined according to each equipment risk index of each equipment point.

4. The method according to claim 3, characterized in that The method further comprises: The greater of the process risk and the equipment risk is used to determine the risk index of the refinery.

5. The method according to claim 3, characterized in that: The refining device includes a normal pressure reducing device; the equipment parameter data includes at least one of the following: The pressure of the sealed oil tank of the primary bottom pump in the constant pressure reduction device, the liquid level of the sealed oil tank of the primary bottom pump in the constant pressure reduction device, and the vibration data of the pump and compressor in the constant pressure reduction device.

6. The method according to claim 3, characterized in that Determining the equipment risk of the refinery according to each equipment risk index of each equipment point includes: Classifying the equipment risk index according to the type of the equipment parameter data to obtain multiple categories of equipment risk indexes; The maximum value of the equipment risk index of each category is used as the target equipment risk index of that category; Obtain the weight value corresponding to each category of equipment risk index; The equipment risk of the refining device is determined according to the sum of the products of the target equipment risk index of each category and the corresponding weight value.

7. The method according to claim 3, characterized in that The frequency range for acquiring the process parameter data or the equipment parameter data is 0.8 Hz to 1.2 Hz.

8. The method according to claim 1, characterized in that Determining the process risk of the refining unit according to each process risk index of each process point includes: Classifying the process risk index according to the type of the process parameter data to obtain multiple categories of process risk indexes; The maximum value of the process risk index of each category is taken as the target process risk index of that category; Obtain the weight value corresponding to each category of process risk index; The process risk of the refining unit is determined according to the sum of the products of the target risk index of each category and the corresponding weight value.

9. A processor, characterized in that: The device is configured to perform the method for determining the risk of a refinery according to any one of claims 1 to 8.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: The instructions are used to cause the machine to execute the method for determining the risk of a refinery according to any one of claims 1 to 8.

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