A visualization-based petrochemical equipment operation monitoring system and method

Abstract

The present invention discloses a petrochemical equipment operation supervision system and method based on visualization, which belongs to the field of data analysis technology. The present invention obtains the division of chemical production areas, generates the positioning data of the operating equipment in each area, and collects the real-time operating data of each device; visualizes the collected data of the target device through the simulation port, obtains the periodic data distribution matrix of the target device, and retrieves the historical environmental data of the target device to perform environmental risk value analysis on the current device; analyzes the distribution deviation degree of the operating data of the equipment in the current monitoring period, and analyzes the operating status of the equipment in combination with the equipment environmental risk value; judges the abnormality of the equipment in combination with the abnormal operation interval of the historical equipment, and analyzes the abnormal level of the target equipment according to the judgment result; outputs the real-time operating data and status analysis data of each device, and performs positioning warning on the abnormal operating equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a visualization-based petrochemical equipment operation monitoring system and method. Background Art

[0002] Visualized petrochemical equipment operation supervision integrates advanced sensor technology, real-time data processing and intuitive graphical user interface to visualize equipment operation parameters and achieve real-time monitoring and analysis of equipment status; it can promptly alert potential abnormal situations, reduce downtime and improve equipment reliability and production efficiency;

[0003] In the current environment, most petrochemical production parks are equipped with remote equipment monitoring systems. However, their monitoring intelligence is mostly insufficient to conduct autonomous analysis of equipment anomalies, and they can often only issue risk alerts through preset values. Their risk detection is too crude, so regular manual inspections are still required. However, manual inspections are inefficient, time-consuming, and labor-intensive for large-scale, long-term equipment maintenance needs. Therefore, existing equipment monitoring technologies are insufficient in their ability to analyze and mine equipment data, and cannot bring about effective real-time equipment monitoring effects. Summary of the invention

[0004] The purpose of the present invention is to provide a petrochemical equipment operation monitoring system and method based on visualization to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for monitoring the operation of petrochemical equipment based on visualization, the method comprising the following steps:

[0007] S100, obtaining the division of chemical production areas, generating positioning data of operating equipment in each area, and collecting real-time operating data of each equipment;

[0008] S200, visualizing the collected data of the target device through the simulation port, obtaining the periodic data distribution matrix of the target device, and retrieving the historical environmental data of the target device to analyze the environmental risk value of the current device;

[0009] S300, analyzing the distribution deviation degree of the equipment's operating data in the current monitoring period and analyzing the equipment's operating status in combination with the equipment's environmental risk value; judging equipment abnormality in combination with the historical equipment operating abnormality interval, and analyzing the abnormality level of the target equipment according to the judgment result;

[0010] S400: Output the real-time operation data and status analysis data of each device, and provide positioning and warning for abnormally operating devices.

[0011] The specific steps of S100 for obtaining the division of chemical production areas, generating the positioning data of the operating equipment in each area, and collecting the real-time operating data of each equipment are as follows:

[0012] S101, using chemical production area monitoring equipment to collect production area division information of the area, obtain spatial information and equipment type data of the corresponding area; number and locate each divided sub-area and equipment; the regional spatial information includes production process type data and area data of the area;

[0013] S102, using a sensor device to collect operating data of the monitoring device, integrating the collected data using the device's positioning number and transmitting the data to the monitoring port; the device operating data includes device operating status data and device environment data.

[0014] The specific steps of S200 visualizing the collected data of the target device through the simulation port, obtaining the periodic data distribution matrix of the target device, and retrieving the historical environmental data of the target device to analyze the environmental risk value of the current device are as follows:

[0015] S201, extract the device location number of the collected data and locate the target device in the field area in combination with the monitoring device; retrieve the device type data and real-time operation data and input them into the simulation port to perform device simulation operation, and construct the periodic operation status data matrix and periodic device environment data matrix of the corresponding device by setting the monitoring cycle;

[0016] S202, by retrieving the historical operation data of the target equipment in the database, taking the historical operation data as the reference analysis object, analyzing the environmental risk value within the current monitoring period of the target equipment; analyzing the risk level of each type of environmental data within the period respectively, and the calculation formula is: in is the risk level of each type of environmental data corresponding to the target device; α is the environmental data type number; ij is the device number, i is the sub-area number, j is the device number corresponding to the i sub-area; T is the monitoring period; is the environmental data of type number α at time t in the equipment cycle of corresponding number ij; is the mean value of the historical environmental data of the equipment with the corresponding number ij and the corresponding type number α; the comprehensive environmental risk value of the target equipment within the cycle is analyzed by integrating the risk values ​​of each type of environmental data of the target equipment, and the calculation formula is: Among them, Erv ij is the comprehensive environmental risk value of the corresponding equipment number ij during the monitoring period; α is the risk impact coefficient corresponding to each type of environmental data.

[0017] The S300 analyzes the distribution deviation of the operation data of the equipment in the current monitoring period and analyzes the operation status of the equipment in combination with the equipment environment risk value; and judges the equipment abnormality in combination with the historical equipment operation abnormality interval. The specific steps of performing abnormality level analysis on the target equipment according to the judgment result are as follows:

[0018] S301, respectively extract the device operation status data matrix and the corresponding historical operation status data matrix of the target device in the current monitoring period; respectively calculate the data distribution deviation degree of each operation status type data in the current monitoring period compared with each historical operation status type data, and the calculation formula is:

[0019] in is the distribution deviation degree of the type operation status data of the corresponding number β of the corresponding number ij device; is the mean value of the type operating status data of the equipment with the corresponding number ij and the corresponding number β during the monitoring period; is the mean of the historical operating status data of the type corresponding to the number β of the device corresponding to the number ij; The type operation status data of the corresponding number β of the corresponding number ij device at time t; is the mean value of the historical operating status data of the type corresponding to the number β of the device corresponding to the number ij; is the standard deviation of the historical operating status data of the type corresponding to the device with number ij and the corresponding number β; the distribution deviation degree of the operating status data within the monitoring period of the target equipment is analyzed by combining the operating status data of various types, and the calculation formula is: Among them, Cd ij Sv is the deviation degree of the comprehensive operation status data distribution of the corresponding equipment number ij; the operation status of the equipment is analyzed in combination with the equipment environmental risk value, and its calculation formula is Sv ij =Erv ij *Cd ij *γ ij ; where Sv ij is the abnormal state deviation value of the device with the corresponding number ij; γ ij is the type status parameter of the device with the corresponding number ij; by analyzing the period distribution deviation degree of the device's operating data, the operating process status of the device can be reflected, which is convenient for judging the abnormal status of the device;

[0020] S302, judging the abnormality of the target device in the current monitoring period in combination with the historical abnormal interval of the target device; obtaining the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device [Sv ij (hl) min , Sv ij (hl)max ];in Among them Sv ij (hl) min and Sv ij (hl) max are the minimum and maximum values ​​of the historical abnormal state deviation value allowed in the target device; ave[Sv ij (hl)] is the mean value of the historical abnormal state deviation of the target device; k is the error tolerance coefficient; if Sv ij ∈[Sv ij (hl) min , Sv ij (hl) max ], the device operates normally; if The device is operating abnormally; since there is no absolute judgment interval for the deviation of the device data, there are many factors that lead to the deviation. Therefore, by setting the error coefficient, the corresponding upper and lower limits of the judgment allowance can be obtained to form the judgment interval;

[0021] S303, according to the abnormal state judgment result of the device, if the device is operating abnormally, the abnormal operation level of the target device is analyzed; the abnormal operation level of the target device is obtained by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table; wherein the abnormal operation index of the abnormal device is analyzed, and its calculation formula is: AOI ij is the abnormal operation index of the abnormal equipment; wherein the abnormal operation index table is a comparison table formed by summarizing the experience of the big data set. By inputting the abnormal index of the abnormal equipment, the abnormal level of the current abnormal equipment can be obtained.

[0022] The S400 outputs the real-time operation data and status analysis data of each device, and the specific steps of locating and alerting abnormal operation devices are as follows:

[0023] S401, visually output the real-time operation data of the equipment with corresponding numbers in each sub-area and the corresponding abnormal state analysis data through the simulation port;

[0024] S402: Number and locate the abnormal equipment, output its abnormal index and abnormal level, and issue a warning.

[0025] A visualization-based petrochemical equipment operation supervision system, the system comprising a regional equipment data acquisition module, a visualization module, an equipment operation status comprehensive analysis unit and an equipment positioning warning module;

[0026] The equipment data acquisition module obtains the division of chemical production areas, generates the positioning data of the operating equipment in each area, and collects the real-time operating data of each device; the visualization module visualizes the collected data of the target device through the simulation port, obtains the periodic data distribution matrix of the target device, and retrieves the historical environmental data of the target device to analyze the environmental risk value of the current device; the equipment operation status comprehensive analysis unit analyzes the distribution deviation degree of the equipment operation data in the current monitoring period, and analyzes the equipment operation status in combination with the equipment environmental risk value; combines the historical equipment operation abnormality interval to make equipment abnormality judgment, and analyzes the abnormality level of the target equipment according to the judgment result; the equipment positioning warning module outputs the real-time operating data and status analysis data of each device, and performs positioning warning on abnormally operating equipment.

[0027] The regional equipment data acquisition module includes a regional equipment information acquisition unit and an equipment operation data acquisition unit;

[0028] The regional equipment information acquisition unit uses the chemical production area monitoring equipment to collect the production area division information of the area, obtain the spatial information and equipment type data of the corresponding area; number and locate each divided sub-area and equipment; the regional spatial information includes the production process type data and area data of the area;

[0029] The equipment operation data collection unit collects the operation data of the monitoring equipment using a sensor device, integrates the collected data using the equipment positioning number, and transmits the collected data to the monitoring port; the equipment operation data includes equipment operation status data and equipment environment data.

[0030] The visualization module includes a simulation visualization unit and an equipment environment risk analysis unit;

[0031] The simulation visualization unit extracts the device location number of the collected data and locates the target device in the field area in combination with the monitoring device; the device type data and real-time operation data are retrieved and input into the simulation port to perform device simulation operation, and the periodic operation status data matrix and periodic device environment data matrix of the corresponding device are constructed by setting the monitoring cycle;

[0032] The equipment environmental risk analysis unit retrieves the historical operation data of the target equipment in the database, uses the historical operation data as the reference analysis object, and analyzes the environmental risk value within the current target equipment monitoring cycle; analyzes the risk level of each type of environmental data within the cycle separately; and analyzes the comprehensive environmental risk value within the target equipment cycle by comprehensively analyzing the risk values ​​of each type of environmental data of the target equipment.

[0033] The equipment operation status comprehensive analysis unit includes an equipment operation data distribution deviation analysis unit, an equipment abnormal state judgment unit and an equipment abnormal index analysis unit;

[0034] The equipment operation data distribution deviation analysis unit extracts the equipment operation status data matrix and the corresponding historical operation status data matrix of the target equipment in the current monitoring period respectively; calculates the data distribution deviation degree of each type of operation status data in the current monitoring period compared with each type of historical operation status data respectively; analyzes the distribution deviation degree of the operation status data in the monitoring period of the target equipment by integrating each type of operation status data; and analyzes the operation status of the equipment in combination with the equipment environmental risk value;

[0035] The device abnormal state judgment unit judges the abnormality of the target device in the current monitoring period in combination with the historical abnormal interval of the target device; obtains the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device; and judges the abnormal state of the target device in the current monitoring period by comparing the historical abnormal interval of the target device;

[0036] The device abnormality index analysis unit analyzes the abnormal operation level of the target device based on the device abnormal state judgment result, if the device is operating abnormally; the abnormal operation level of the target device is obtained by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table.

[0037] The equipment positioning warning module includes a data visualization output unit and an abnormal equipment warning unit;

[0038] The data visualization output unit visualizes and outputs the real-time operation data of the equipment with corresponding numbers in each sub-area and the corresponding abnormal state analysis data through the simulation port;

[0039] The abnormal device warning unit numbers and locates the abnormal device, outputs its abnormal index and abnormal level, and issues a warning.

[0040] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention combines the monitoring equipment and sensor equipment of the petrochemical production park to monitor and collect data from the production area equipment, and performs data conversion processing on the collected data through a visualization unit, and performs equipment status analysis by analyzing the equipment environmental risk value and the degree of deviation of the equipment operation status distribution in combination with historical data; the abnormal state of the equipment is judged by combining the historical abnormal interval, and the abnormal index analysis and abnormal level determination are performed for the abnormal equipment; the present invention collects and analyzes periodic data in real time during the operation of the petrochemical equipment, and judges the real-time state of the equipment and analyzes the abnormal state by mining the degree of data deviation and performing correlation analysis with environmental data, thereby greatly improving the intelligence and accuracy of remote monitoring of traditional petrochemical equipment; and improves the intelligence and timeliness of equipment monitoring by solving the low efficiency caused by regular manual inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 It is a structural schematic diagram of a visualization-based petrochemical equipment operation monitoring system of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 , the present invention provides a technical solution:

[0045] A method for monitoring the operation of petrochemical equipment based on visualization, the method comprising the following steps:

[0046] S100, obtaining the division of chemical production areas, generating positioning data of operating equipment in each area, and collecting real-time operating data of each equipment;

[0047] S200, visualizing the collected data of the target device through the simulation port, obtaining the periodic data distribution matrix of the target device, and retrieving the historical environmental data of the target device to analyze the environmental risk value of the current device;

[0048] S300, analyzing the distribution deviation degree of the equipment's operating data in the current monitoring period and analyzing the equipment's operating status in combination with the equipment's environmental risk value; judging equipment abnormality in combination with the historical equipment operating abnormality interval, and analyzing the abnormality level of the target equipment according to the judgment result;

[0049] S400: Output the real-time operation data and status analysis data of each device, and provide positioning and warning for abnormally operating devices.

[0050] The specific steps of S100 for obtaining the division of chemical production areas, generating the positioning data of the operating equipment in each area, and collecting the real-time operating data of each equipment are as follows:

[0051] S101, using chemical production area monitoring equipment to collect production area division information of the area, obtain spatial information and equipment type data of the corresponding area; number and locate each divided sub-area and equipment; the regional spatial information includes production process type data and area data of the area;

[0052] S102, using a sensor device to collect operating data of the monitoring device, integrating the collected data using the device's positioning number and transmitting the data to the monitoring port; the device operating data includes device operating status data and device environment data.

[0053] The specific steps of S200 visualizing the collected data of the target device through the simulation port, obtaining the periodic data distribution matrix of the target device, and retrieving the historical environmental data of the target device to analyze the environmental risk value of the current device are as follows:

[0054] S201, extract the device location number of the collected data and locate the target device in the field area in combination with the monitoring device; retrieve the device type data and real-time operation data and input them into the simulation port to perform device simulation operation, and construct the periodic operation status data matrix and periodic device environment data matrix of the corresponding device by setting the monitoring cycle;

[0055] S202, by retrieving the historical operation data of the target equipment in the database, taking the historical operation data as the reference analysis object, analyzing the environmental risk value within the current monitoring period of the target equipment; analyzing the risk level of each type of environmental data within the period respectively, and the calculation formula is: in is the risk level of each type of environmental data corresponding to the target device; α is the environmental data type number; ij is the device number, i is the sub-area number, j is the device number corresponding to the i sub-area; T is the monitoring period; is the environmental data of type number α at time t in the equipment cycle of corresponding number ij; is the mean value of the historical environmental data of the equipment with the corresponding number ij and the corresponding type number α; the comprehensive environmental risk value of the target equipment within the cycle is analyzed by integrating the risk values ​​of each type of environmental data of the target equipment, and the calculation formula is: Among them, Erv ij is the comprehensive environmental risk value of the corresponding equipment number ij during the monitoring period; α is the risk impact coefficient corresponding to each type of environmental data.

[0056] The S300 analyzes the distribution deviation of the operation data of the equipment in the current monitoring period and analyzes the operation status of the equipment in combination with the equipment environment risk value; and judges the equipment abnormality in combination with the historical equipment operation abnormality interval. The specific steps of performing abnormality level analysis on the target equipment according to the judgment result are as follows:

[0057] S301, respectively extract the device operation status data matrix and the corresponding historical operation status data matrix of the target device in the current monitoring period; respectively calculate the data distribution deviation degree of each operation status type data in the current monitoring period compared with each historical operation status type data, and the calculation formula is:

[0058] in is the distribution deviation degree of the type operation status data of the corresponding number β of the corresponding number ij device; is the mean value of the type operating status data of the equipment with the corresponding number ij and the corresponding number β during the monitoring period; is the mean of the historical operating status data of the type corresponding to the number β of the device corresponding to the number ij; The type operation status data of the corresponding number β of the corresponding number ij device at time t; is the mean value of the historical operating status data of the type corresponding to the number β of the device corresponding to the number ij; is the standard deviation of the historical operating status data of the type corresponding to the device with number ij and the corresponding number β; the distribution deviation degree of the operating status data within the monitoring period of the target equipment is analyzed by combining the operating status data of various types, and the calculation formula is: Among them, Cd ij Sv is the deviation degree of the comprehensive operation status data distribution of the corresponding equipment number ij; the operation status of the equipment is analyzed in combination with the equipment environmental risk value, and its calculation formula is Sv ij =Erv ij *Cd ij *γ ij ; where Sv ij is the abnormal state deviation value of the device with the corresponding number ij; γ ij is the type status parameter of the device with the corresponding number ij;

[0059] S302, judging the abnormality of the target device in the current monitoring period in combination with the historical abnormal interval of the target device; obtaining the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device [Sv ij (hl) min , Sv ij (hl) max ];in Among them Sv ij (hl) min and Sv ij (hl) max are the minimum and maximum values ​​of the historical abnormal state deviation value allowed in the target device; ave[Sv ij (hl)] is the mean value of the historical abnormal state deviation of the target device; k is the error tolerance coefficient; if Sv ij ∈[Sv ij (hl) min , Sv ij (hl) max ], the device operates normally; if The device operates abnormally;

[0060] S303, according to the abnormal state judgment result of the device, if the device is operating abnormally, the abnormal operation level of the target device is analyzed; the abnormal operation level of the target device is obtained by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table; wherein the abnormal operation index of the abnormal device is analyzed, and its calculation formula is: AOI ij It is the abnormal operation index of abnormal equipment.

[0061] The S400 outputs the real-time operation data and status analysis data of each device, and the specific steps of locating and alerting abnormal operation devices are as follows:

[0062] S401, visually output the real-time operation data of the equipment with corresponding numbers in each sub-area and the corresponding abnormal state analysis data through the simulation port;

[0063] S402: Number and locate the abnormal equipment, output its abnormal index and abnormal level, and issue a warning.

[0064] A visualization-based petrochemical equipment operation supervision system, the system comprising a regional equipment data acquisition module, a visualization module, an equipment operation status comprehensive analysis unit and an equipment positioning warning module;

[0065] The equipment data acquisition module obtains the division of chemical production areas, generates the positioning data of the operating equipment in each area, and collects the real-time operating data of each device; the visualization module visualizes the collected data of the target device through the simulation port, obtains the periodic data distribution matrix of the target device, and retrieves the historical environmental data of the target device to analyze the environmental risk value of the current device; the equipment operation status comprehensive analysis unit analyzes the distribution deviation degree of the equipment operation data in the current monitoring period, and analyzes the equipment operation status in combination with the equipment environmental risk value; combines the historical equipment operation abnormality interval to make equipment abnormality judgment, and analyzes the abnormality level of the target equipment according to the judgment result; the equipment positioning warning module outputs the real-time operating data and status analysis data of each device, and performs positioning warning on abnormally operating equipment.

[0066] The regional equipment data acquisition module includes a regional equipment information acquisition unit and an equipment operation data acquisition unit;

[0067] The regional equipment information acquisition unit uses the chemical production area monitoring equipment to collect the production area division information of the area, obtain the spatial information and equipment type data of the corresponding area; number and locate each divided sub-area and equipment; the regional spatial information includes the production process type data and area data of the area;

[0068] The equipment operation data collection unit collects the operation data of the monitoring equipment using a sensor device, integrates the collected data using the equipment positioning number, and transmits the collected data to the monitoring port; the equipment operation data includes equipment operation status data and equipment environment data.

[0069] The visualization module includes a simulation visualization unit and an equipment environment risk analysis unit;

[0070] The simulation visualization unit extracts the device location number of the collected data and locates the target device in the field area in combination with the monitoring device; the device type data and real-time operation data are retrieved and input into the simulation port to perform device simulation operation, and the periodic operation status data matrix and periodic device environment data matrix of the corresponding device are constructed by setting the monitoring cycle;

[0071] The equipment environmental risk analysis unit retrieves the historical operation data of the target equipment in the database, uses the historical operation data as the reference analysis object, and analyzes the environmental risk value within the current target equipment monitoring cycle; analyzes the risk level of each type of environmental data within the cycle separately; and analyzes the comprehensive environmental risk value within the target equipment cycle by comprehensively analyzing the risk values ​​of each type of environmental data of the target equipment.

[0072] The equipment operation status comprehensive analysis unit includes an equipment operation data distribution deviation analysis unit, an equipment abnormal state judgment unit and an equipment abnormal index analysis unit;

[0073] The equipment operation data distribution deviation analysis unit extracts the equipment operation status data matrix and the corresponding historical operation status data matrix of the target equipment in the current monitoring period respectively; calculates the data distribution deviation degree of each type of operation status data in the current monitoring period compared with each type of historical operation status data respectively; analyzes the distribution deviation degree of the operation status data in the monitoring period of the target equipment by integrating each type of operation status data; and analyzes the operation status of the equipment in combination with the equipment environmental risk value;

[0074] The device abnormal state judgment unit judges the abnormality of the target device in the current monitoring period in combination with the historical abnormal interval of the target device; obtains the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device; and judges the abnormal state of the target device in the current monitoring period by comparing the historical abnormal interval of the target device;

[0075] The device abnormality index analysis unit analyzes the abnormal operation level of the target device based on the device abnormal state judgment result, if the device is operating abnormally; the abnormal operation level of the target device is obtained by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table.

[0076] The equipment positioning warning module includes a data visualization output unit and an abnormal equipment warning unit;

[0077] The data visualization output unit visualizes and outputs the real-time operation data of the equipment with corresponding numbers in each sub-area and the corresponding abnormal state analysis data through the simulation port;

[0078] The abnormal device warning unit numbers and locates the abnormal device, outputs its abnormal index and abnormal level, and issues a warning;

[0079] In the example:

[0080] A certain petrochemical park has introduced the visualized petrochemical equipment operation supervision system of the present invention to effectively supervise various production equipment in the park; the chemical production area monitoring equipment is used to collect production area division information of the area, and the spatial information and equipment type data of the corresponding area are obtained; each divided sub-area and equipment are numbered and located; the sensor device is used to collect operation data of the monitoring equipment, and the collected data is integrated and transmitted to the monitoring port using the positioning number of the equipment;

[0081] Extract the device location number of the collected data and locate the target device in the field area in combination with the monitoring device; retrieve the device type data and real-time operation data and input them into the simulation port to simulate the device operation, and build the periodic operation status data matrix and periodic device environment data matrix of the corresponding device by setting the monitoring cycle; retrieve the historical operation data of the target device in the database, use the historical operation data as the reference analysis object, and analyze the environmental risk value within the current monitoring cycle of the target device; analyze the risk level of each type of environmental data within the cycle, and the calculation formula is: The comprehensive environmental risk value of the target equipment during its life cycle is analyzed by integrating the risk values ​​of various types of environmental data of the target equipment. The calculation formula is:

[0082] The device operation status data matrix and the corresponding historical operation status data matrix of the target device in the current monitoring period are extracted respectively; the data distribution deviation degree of each operation status type data in the current monitoring period compared with each historical operation status type data is calculated respectively, and the calculation formula is: Comprehensively analyze the distribution deviation of the operating status data of the target equipment within the monitoring period by combining various types of operating status data. The calculation formula is: The operating status of the equipment is analyzed in combination with the equipment environmental risk value, and the calculation formula is Sv ij =Erv ij *Cd ij *γ ij ;

[0083] Combine the historical abnormal interval of the target device to judge the abnormality of the target device in the current monitoring period; obtain the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device [Sv ij (hl) min , Sv ij (hl) max ];in If Sv ij ∈[Sv ij (hl) min , Sv ij (hl) max ], the device operates normally; if The device operates abnormally;

[0084] According to the abnormal state judgment result of the device, if the device is operating abnormally, the abnormal operation level of the target device is analyzed; the abnormal operation level of the target device is obtained by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table; the abnormal operation index of the abnormal device is analyzed, and its calculation formula is:

[0085] The real-time operation data of the corresponding numbered equipment in each sub-area and the corresponding abnormal status analysis data are visually output through the simulation port; the abnormal equipment is numbered and located, and its abnormal index and abnormal level are output, and an alarm is issued.

[0086] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device.

[0087] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring the operation of petrochemical equipment based on visualization, characterized in that: The method comprises the following steps: S100, obtaining the division of chemical production areas, generating positioning data of operating equipment in each area, and collecting real-time operating data of each equipment; S200, visualizing the collected data of the target device through the simulation port, obtaining the periodic data distribution matrix of the target device, and retrieving the historical environmental data of the target device to analyze the environmental risk value of the current device; S300, analyzing the distribution deviation degree of the equipment's operating data in the current monitoring period and analyzing the equipment's operating status in combination with the equipment's environmental risk value; judging equipment abnormality in combination with the historical equipment operating abnormality interval, and analyzing the abnormality level of the target equipment according to the judgment result; The specific steps of S300 are as follows: S301, respectively extract the device operation status data matrix and the corresponding historical operation status data matrix of the target device in the current monitoring period; respectively calculate the data distribution deviation degree of each operation status type data in the current monitoring period compared with each historical operation status type data, and the calculation formula is: in is the distribution deviation degree of the type operation status data of the corresponding number β of the corresponding number ij device; is the mean value of the type operating status data of the equipment with the corresponding number ij and the corresponding number β during the monitoring period; is the mean of the historical operating status data of the type corresponding to the number β of the device corresponding to the number ij; The type operation status data of the corresponding number β of the corresponding number ij device at time t; is the mean value of the historical operating status data of the type corresponding to the number β of the device corresponding to the number ij; is the standard deviation of the historical operating status data of the type corresponding to the device with number ij and the corresponding number β; the distribution deviation degree of the operating status data within the monitoring period of the target equipment is analyzed by combining the operating status data of various types, and the calculation formula is: Among them, Cd ij Sv is the deviation degree of the comprehensive operation status data distribution of the corresponding equipment number ij; the operation status of the equipment is analyzed in combination with the equipment environmental risk value, and its calculation formula is Sv ij =Erv ij *Cd ij *γ ij ; where Sv ij is the abnormal state deviation value of the device with the corresponding number ij; γ ij is the type status parameter of the device with the corresponding number ij; S400: Output the real-time operation data and status analysis data of each device, and provide positioning and warning for abnormally operating devices.

2. The method for monitoring the operation of petrochemical equipment based on visualization according to claim 1 is characterized in that: The specific steps of S100 for obtaining the division of chemical production areas, generating the positioning data of the operating equipment in each area, and collecting the real-time operating data of each equipment are as follows: S101, using chemical production area monitoring equipment to collect production area division information of the area, obtain spatial information and equipment type data of the corresponding area; number and locate each divided sub-area and equipment; the regional spatial information includes production process type data and area data of the area; S102, using a sensor device to collect operating data of the monitoring device, integrating the collected data using the device's positioning number and transmitting the data to the monitoring port; the device operating data includes device operating status data and device environment data.

3. The method for monitoring the operation of petrochemical equipment based on visualization according to claim 2 is characterized in that: The specific steps of S200 visualizing the collected data of the target device through the simulation port, obtaining the periodic data distribution matrix of the target device, and retrieving the historical environmental data of the target device to analyze the environmental risk value of the current device are as follows: S201, extract the device location number of the collected data and locate the target device in the field area in combination with the monitoring device; retrieve the device type data and real-time operation data and input them into the simulation port to perform device simulation operation, and construct the periodic operation status data matrix and periodic device environment data matrix of the corresponding device by setting the monitoring cycle; S202, by retrieving the historical operation data of the target equipment in the database, taking the historical operation data as the reference analysis object, analyzing the environmental risk value within the current monitoring period of the target equipment; analyzing the risk level of each type of environmental data within the period respectively, and the calculation formula is: in is the risk level of each type of environmental data corresponding to the target device; α is the environmental data type number; ij is the device number, i is the sub-area number, j is the device number corresponding to the i sub-area; T is the monitoring period; is the environmental data of type number α at time t in the equipment cycle of corresponding number ij; is the mean value of the historical environmental data of the equipment with the corresponding number ij and the corresponding type number α; the comprehensive environmental risk value of the target equipment within the cycle is analyzed by integrating the risk values ​​of each type of environmental data of the target equipment, and the calculation formula is: Among them, Erv ij is the comprehensive environmental risk value of the corresponding equipment number ij during the monitoring period; α is the risk impact coefficient corresponding to each type of environmental data.

4. The method for monitoring the operation of petrochemical equipment based on visualization according to claim 3 is characterized in that: S302, judging the abnormality of the target device in the current monitoring period in combination with the historical abnormal interval of the target device; obtaining the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device [Sv ij (hl) min , Sv ij (hl) max ];in Among them Sv ij (hl) min and Sv ij (hl) max are the minimum and maximum values ​​of the historical abnormal state deviation value allowed in the target device; ave[Sv ij (hl)] is the mean value of the historical abnormal state deviation of the target device; k is the error tolerance coefficient; if Sv ij ∈[Sv ij (hl) min , Sv ij (hl) max ], the device operates normally; if Sv ij (hl) max ], the device operates abnormally; S303, according to the abnormal state judgment result of the device, if the device is operating abnormally, analyzing the abnormal operation level of the target device; obtaining the abnormal operation level of the target device by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table; The abnormal operation index of abnormal equipment is analyzed, and its calculation formula is: AOI ij It is the abnormal operation index of abnormal equipment.

5. The method for monitoring the operation of petrochemical equipment based on visualization according to claim 4 is characterized in that: The S400 outputs the real-time operation data and status analysis data of each device, and the specific steps of locating and alerting abnormal operation devices are as follows: S401, visually output the real-time operation data of the equipment with corresponding numbers in each sub-area and the corresponding abnormal state analysis data through the simulation port; S402: Number and locate the abnormal equipment, output its abnormal index and abnormal level, and issue a warning.

6. A visualization-based petrochemical equipment operation supervision system, using a visualization-based petrochemical equipment operation supervision method as described in any one of claims 1 to 5, characterized in that: The system includes a regional equipment data acquisition module, a visualization module, an equipment operation status comprehensive analysis unit and an equipment positioning warning module; The equipment data acquisition module obtains the division of chemical production areas, generates the positioning data of the operating equipment in each area, and collects the real-time operating data of each equipment; the visualization module visualizes the collected data of the target equipment through the simulation port, obtains the periodic data distribution matrix of the target equipment, and retrieves the historical environmental data of the target equipment to analyze the environmental risk value of the current equipment; the equipment operation status comprehensive analysis unit analyzes the distribution deviation degree of the equipment operation data in the current monitoring period and analyzes the equipment operation status in combination with the equipment environmental risk value; the equipment abnormality judgment is performed in combination with the historical equipment operation abnormality interval, and the abnormality level analysis of the target equipment is performed according to the judgment result; The device positioning and warning module outputs the real-time operation data and status analysis data of each device, and issues positioning and warning for abnormally operating devices.

7. The visualization-based petrochemical equipment operation monitoring system according to claim 6 is characterized by: The regional equipment data acquisition module includes a regional equipment information acquisition unit and an equipment operation data acquisition unit; The regional equipment information acquisition unit uses the chemical production area monitoring equipment to collect the production area division information of the area, obtain the spatial information and equipment type data of the corresponding area; number and locate each divided sub-area and equipment; the regional spatial information includes the production process type data and area data of the area; The equipment operation data collection unit collects the operation data of the monitoring equipment using a sensor device, integrates the collected data using the equipment positioning number, and transmits the collected data to the monitoring port; the equipment operation data includes equipment operation status data and equipment environment data.

8. The visualization-based petrochemical equipment operation monitoring system according to claim 7 is characterized by: The visualization module includes a simulation visualization unit and an equipment environment risk analysis unit; The simulation visualization unit extracts the device location number of the collected data and performs field area location of the target device in conjunction with the monitoring device; By retrieving equipment type data and real-time operation data and inputting them into the simulation port, equipment simulation operation is performed, and the periodic operation status data matrix and periodic equipment environment data matrix of the corresponding equipment are constructed by setting the monitoring cycle; The equipment environmental risk analysis unit retrieves the historical operation data of the target equipment in the database, uses the historical operation data as the reference analysis object, and analyzes the environmental risk value within the current target equipment monitoring cycle; analyzes the risk level of each type of environmental data within the cycle separately; and analyzes the comprehensive environmental risk value within the target equipment cycle by comprehensively analyzing the risk values ​​of each type of environmental data of the target equipment.

9. The visualization-based petrochemical equipment operation monitoring system according to claim 8 is characterized by: The equipment operation status comprehensive analysis unit includes an equipment operation data distribution deviation analysis unit, an equipment abnormal state judgment unit and an equipment abnormal index analysis unit; The equipment operation data distribution deviation analysis unit respectively extracts the equipment operation status data matrix and the corresponding historical operation status data matrix of the target equipment in the current monitoring period; Calculate the data distribution deviation degree of each type of operation status data in the current monitoring cycle compared with each type of historical operation status data; analyze the distribution deviation degree of the operation status data in the target equipment monitoring cycle by combining various types of operation status data; analyze the operation status of the equipment in combination with the equipment environmental risk value; The device abnormal state judgment unit judges the abnormality of the target device in the current monitoring period in combination with the historical abnormal interval of the target device; obtains the historical abnormal interval of the target device by analyzing the minimum and maximum values ​​of the allowed interval of the historical abnormal state deviation value of the target device; and judges the abnormal state of the target device in the current monitoring period by comparing the historical abnormal interval of the target device; The device abnormality index analysis unit analyzes the abnormal operation level of the target device based on the device abnormal state judgment result, if the device is operating abnormally; the abnormal operation level of the target device is obtained by analyzing the abnormal operation index of the device and comparing it with the abnormal operation index table.

10. A visualization-based petrochemical equipment operation monitoring system according to claim 9, characterized in that: The equipment positioning warning module includes a data visualization output unit and an abnormal equipment warning unit; The data visualization output unit visualizes and outputs the real-time operation data of the equipment with corresponding numbers in each sub-area and the corresponding abnormal state analysis data through the simulation port; The abnormal device warning unit numbers and locates the abnormal device, outputs its abnormal index and abnormal level, and issues a warning.

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