Risk assessment method and device for oil and gas transmission pipeline

Abstract

The embodiment of the invention provides a risk assessment method and device for an oil and gas transmission pipeline. The method comprises the following steps: acquiring real-time parameter data and historical data of a target oil and gas transmission pipeline, and performing data preprocessing; data analysis is conducted on the preprocessed data, a single-variable analysis result and a multi-variable correlation analysis result are obtained, the single-variable analysis result comprises a single variable in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result comprises correlation of multiple variables in the real-time parameter data similar to the historical data; and performing risk assessment on the target oil and gas transmission pipeline based on the analysis result. Therefore, by combining the historical data of the pipeline with the real-time pipeline data for comparative analysis, the real-time data similar to the historical data can be quickly identified, so that the potential risk can be found in time, and meanwhile, the risk degree can be accurately judged according to the environmental factors of pipeline laying.

Description

Technical Field

[0001] The present invention relates to the technical fields of data processing and risk assessment of oil and gas pipelines, and particularly relates to a risk assessment method and device for oil and gas transportation pipelines. Background Art

[0002] An oil and gas pipeline refers to a long-distance transportation channel for transporting oil and gas resources such as petroleum and natural gas, which can efficiently and safely transport oil and gas from the production site to refineries, oil storage depots or end-users, etc. Compared with transportation methods such as highways and railways, the use of oil and gas pipelines can achieve uninterrupted transportation of oil and gas resources, and the oil and gas pipelines laid underground are less affected by the outside world. In the case of long-distance transportation, the operating cost of oil and gas pipelines is relatively low. Therefore, pipeline transportation is usually the first choice for the transportation process of oil and gas resources at present.

[0003] Due to the long transportation distance of oil and gas pipelines, in order to ensure the safety of oil and gas resources during transportation, it is necessary to monitor the pipelines in real time to avoid major accidents caused by pipeline failures. A large amount of monitoring data will be generated during the monitoring process of the pipelines, and the data needs to be processed and analyzed to obtain the operating status of the oil and gas pipelines. However, at present, the data of oil and gas pipelines only analyzes the known monitored data and cannot judge the subsequent risks of the pipelines based on the pipeline data, thus affecting the prevention of oil and gas pipeline failures. Summary of the Invention

[0004] The present invention provides a risk assessment method and device for oil and gas transportation pipelines to solve the problem in the prior art that only the known monitored data is analyzed and the subsequent risks of the pipelines cannot be judged based on the pipeline data, and to realize quickly identifying real-time oil and gas pipeline data similar to historical data, thereby timely discovering potential risks, and at the same time accurately judging the risk degree according to the environmental factors of the pipeline laying.

[0005] The present invention provides a risk assessment method for oil and gas transportation pipelines, including: Obtaining real-time parameter data and historical data of a target oil and gas transportation pipeline and performing data preprocessing on the real-time parameter data and historical data; Performing data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result, wherein the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlations of multiple variables in the real-time parameter data similar to the historical data; Performing risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

[0006] In a possible implementation manner, the method further includes: The single variable includes the average value of the pipeline's own data, the normal operating range of the pipeline, and the abnormal sections of the pipeline; Performing data analysis on the real-time parameter data and historical data after preprocessing the data to obtain a single variable analysis result and a multi-variable correlation analysis result, including: Performing mean analysis, extreme value analysis, standard deviation analysis, and Pearson correlation analysis on the real-time parameter data and historical data; Determining the average value of the pipeline's own data of the target oil and gas transportation pipeline based on the mean analysis result; Comparing the extreme value analysis result with the pipeline design data of the target oil and gas transportation pipeline to determine the normal operating range of the target oil and gas transportation pipeline; Determining the abnormal sections of the target oil and gas transportation pipeline based on the standard deviation analysis result; Determining the correlation of multiple variables based on the Pearson correlation analysis result.

[0007] In a possible implementation manner, the method further includes: If the real-time parameter data is greater than the average value of the pipeline's own data and greater than a preset multiple, a risk alarm is issued; If the real-time parameter data is not within the normal operating range of the target oil and gas transportation pipeline, a risk alarm is issued; Clustering the abnormal sections of the target oil and gas transportation pipeline according to similarity, and performing corresponding risk monitoring on different types of abnormal sections; Constructing a linear regression model based on the Pearson correlation coefficient corresponding to the correlation of multiple variables to obtain a risk prediction model; Performing risk assessment on the target oil and gas transportation pipeline based on the risk prediction model.

[0008] In a possible implementation manner, the method further includes: Clustering the abnormal sections of the target oil and gas transportation pipeline according to similarity to obtain normal operating sections, potential risk sections, and abnormal sections; Monitoring the risk sections and alarming the abnormal sections.

[0009] In a possible implementation manner, the method further includes: Performing missing value processing, outlier processing, and duplicate value processing on the real-time parameter data and historical data; If the proportion of the missing data part is greater than a preset threshold, the missing data part is deleted and the missing section is marked.

[0010] In a possible implementation manner, the method further includes: Upload the real-time parameter data and historical data after data preprocessing to a local server for storage in the form of a data string.

[0011] The present invention also provides a risk assessment device for an oil and gas transportation pipeline, including the following modules: A data acquisition module, configured to acquire real-time parameter data and historical data of a target oil and gas transportation pipeline and perform data preprocessing on the real-time parameter data and the historical data; A data analysis module, configured to perform data analysis on the real-time parameter data and the historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result, wherein the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlations of multiple variables in the real-time parameter data similar to the historical data; A risk assessment module, configured to perform risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the risk assessment method for an oil and gas transportation pipeline as described in any one of the above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the risk assessment method for an oil and gas transportation pipeline as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the risk assessment method for an oil and gas transportation pipeline as described in any one of the above is implemented.

[0015] The risk assessment method and device for oil and gas transmission pipelines provided by the present invention obtain real-time parameter data and historical data of a target oil and gas transmission pipeline and perform data preprocessing on the real-time parameter data and historical data; perform data analysis on the real-time parameter data and historical data after data preprocessing to obtain univariate analysis results and multivariate correlation analysis results, wherein the univariate analysis results include univariate in the real-time parameter data similar to the historical data, and the multivariate correlation analysis results include the correlations of multiple variables in the real-time parameter data similar to the historical data; perform risk assessment on the target oil and gas transmission pipeline based on the univariate analysis results and multivariate correlation analysis results. Compared with the prior art that only analyzes the monitored known data and cannot judge the subsequent risks of the pipeline based on the pipeline data, which affects the prevention of oil and gas pipeline failures, in this solution, real-time data during the oil and gas pipeline transportation process is collected and analyzed, and historical data of the oil and gas pipeline is collected. By comparing and analyzing the historical data of the pipeline combined with the real-time pipeline data, real-time data similar to the historical data can be quickly identified, thereby timely discovering potential risks. At the same time, according to the environmental factors of the pipeline laying, the risk degree can be accurately judged. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 is one of the flow schematic diagrams of the risk assessment method for oil and gas transmission pipelines provided by the present invention.

[0018] Figure 2 is the second flow schematic diagram of the risk assessment method for oil and gas transmission pipelines provided by the present invention.

[0019] Figure 3 is the structural schematic diagram of the data acquisition part provided by the present invention.

[0020] Figure 4 is the structural schematic diagram of the data preprocessing part provided by the present invention.

[0021] Figure 5 is the structural schematic diagram of the data storage part provided by the present invention.

[0022] Figure 6 is the structural schematic diagram of the data analysis part provided by the present invention.

[0023] Figure 7It is a schematic structural diagram of the data application part provided by the present invention.

[0024] Figure 8 It is a schematic structural diagram of the risk assessment device for the oil and gas transportation pipeline provided by the present invention.

[0025] Figure 9 It is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0026] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0027] For the convenience of understanding the embodiments of the present invention, the following will further explain and illustrate with specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation to the embodiments of the present invention.

[0028] Figure 1 It is one of the flow schematic diagrams of the risk assessment method for the oil and gas transportation pipeline provided by the present invention. As Figure 1 shown, the method includes the following: S11. Obtain the real-time parameter data and historical data of the target oil and gas transportation pipeline and perform data preprocessing on the real-time parameter data and historical data.

[0029] In the embodiments of the present invention, the real-time data in the process of oil and gas pipeline transportation is collected and analyzed, and the historical data of the oil and gas pipeline is collected. By comparing and analyzing the historical data of the pipeline in combination with the real-time pipeline data, the real-time data similar to the historical data can be quickly identified, so as to discover potential risks in time. At the same time, according to the environmental factors of the pipeline laying, the risk degree can be accurately judged.

[0030] Specifically, obtain the real-time parameter data and historical data of the target oil and gas transportation pipeline. The real-time parameter data is various parameter data of the pipeline in the process of oil and gas transportation, including pressure index data, flow rate index data and temperature index data. The pressure index data includes the pipeline operating pressure and the pipeline pressure fluctuation. The pipeline operating pressure is the pressure generated when the oil and gas flow in the pipeline, and the pipeline pressure fluctuation is the change of the operating pressure in the pipeline; the flow rate index data includes the instantaneous flow rate and the cumulative flow rate. The instantaneous flow rate is the oil and gas flow rate passing through the cross-section of the oil and gas pipeline at a certain moment, and the cumulative flow rate is the total amount of oil and gas passing through the pipeline in a period of time; the temperature index data includes the medium temperature and the pipe wall temperature. The medium temperature is the temperature condition of the oil and gas itself during the transportation in the pipeline, and the pipe wall temperature is the temperature of the oil and gas pipeline.

[0031] The pipeline historical data are the self - data of the oil - gas pipeline and the corresponding external environment data before the oil - gas transportation task, including environmental data, pipeline design data, and pipeline maintenance data. The environmental data are the topographic and geomorphic features, soil corrosion rate, and groundwater level data of the pipeline laying area; the pipeline design data include the oil - gas pipeline diameter, pipeline material, and pipeline design flow rate; the pipeline maintenance data are the historical maintenance data of the oil - gas pipeline.

[0032] As Figure 3 shown, when collecting pipeline data, it includes pipeline historical data. For example, for the topographic and geomorphic features of the pipeline laying area, different topographic regions have different degrees of influence on the pipeline. For example, mountainous areas may be affected by debris flows, etc. Multiple factors can affect the pipeline risk assessment process. Similarly, the soil corrosion rate and groundwater level data can judge the corrosion rate of the pipeline and assist the risk assessment process. The pipeline design data include the oil - gas pipeline diameter, pipeline material, and pipeline design flow rate. According to the pipeline design data, it can be compared with the real - time data to quickly judge abnormal data. For example, when the transported flow rate exceeds the pipeline design flow rate, an alarm is sent to the operation and maintenance personnel in a timely manner. And the real - time data of the pipeline are the real - time monitoring data during the transportation process. The collected data are processed and analyzed subsequently to judge the risk of the pipeline.

[0033] Furthermore, data pre - processing can also be performed on the obtained data. Data pre - processing includes missing value processing, outlier processing, and duplicate value processing. Missing value processing includes interpolation filling and deletion - marking method. When the data of a certain moment of the oil - gas pipeline data is missing, the interpolation method is used to fill the missing part according to the linear relationship of the data within this period. If the proportion of the missing part of the data is greater than the preset threshold (for example, 20%), the missing part of this section of data is deleted and the missing section is marked. Outlier processing is to mark the data points whose data exceeds the standard deviation of the mean by a preset multiple (for example, 3 times) positive or negative. Duplicate value processing is to remove the repeatedly collected data to reduce data redundancy.

[0034] S12. Perform data analysis on the pre - processed real - time parameter data and historical data to obtain the univariate analysis result and the multivariate correlation analysis result.

[0035] Perform mean analysis, extreme value analysis, standard deviation analysis, and Pearson correlation analysis on real-time parameter data and historical data. Based on the mean analysis result, the average value of the pipeline's own data can be determined; by comparing the extreme value analysis result with the pipeline design data of the target oil and gas pipeline, the normal operating range of the target oil and gas pipeline can be determined; based on the standard deviation analysis result, the abnormal pipe sections of the target oil and gas pipeline can be determined; based on the Pearson correlation analysis result, the multi-variable correlation can be determined, and the status of the oil and gas pipeline can be judged based on the multi-variable correlation.

[0036] S13. Perform a risk assessment on the target oil and gas pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

[0037] If the real-time parameter data is greater than the average value of the pipeline's own data and greater than a preset multiple, a risk alarm is issued; if the real-time parameter data is not within the normal operating range of the target oil and gas pipeline, a risk alarm is issued.

[0038] Optionally, cluster the abnormal pipe sections of the target oil and gas pipeline according to similarity, and perform corresponding risk monitoring on different types of abnormal pipe sections.

[0039] Optionally, construct a linear regression model based on the Pearson correlation coefficient corresponding to the correlation of multiple variables to obtain a risk prediction model; perform a risk assessment on the target oil and gas pipeline based on the risk prediction model.

[0040] The risk assessment method for an oil and gas pipeline provided by the present invention preprocesses the real-time parameter data and historical data by obtaining the real-time parameter data and historical data of the target oil and gas pipeline; performs data analysis on the preprocessed real-time parameter data and historical data to obtain a single-variable analysis result and a multi-variable correlation analysis result, where the single-variable analysis result includes the single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlation of multiple variables in the real-time parameter data similar to the historical data; performs a risk assessment on the target oil and gas pipeline based on the single-variable analysis result and the multi-variable correlation analysis result. Compared with the prior art where only the monitored known data is analyzed and the subsequent risks of the pipeline cannot be judged based on the pipeline's data, which affects the prevention of oil and gas pipeline failures, by this method, the real-time data during the oil and gas pipeline transportation process is collected and analyzed, and the historical data of the oil and gas pipeline is collected. By comparing and analyzing the historical data of the pipeline combined with the real-time pipeline data, the real-time data similar to the historical data can be quickly identified, thereby timely discovering potential risks. At the same time, according to the environmental factors of the pipeline laying, the risk degree can be accurately judged.

[0041] Figure 2 It is the second process schematic diagram of the risk assessment method for oil and gas transportation pipelines provided by the present invention. As Figure 2 shown, the method includes the following: S1. Data acquisition, collecting real-time parameter data of the oil and gas transportation pipeline and obtaining historical data of the oil and gas transportation pipeline.

[0042] S2. Data preprocessing, preprocessing the collected data to facilitate the subsequent processing and analysis process of the data.

[0043] S3. Data storage, classifying and storing the preprocessed data in a database for easy real-time access and retrieval.

[0044] S4. Data analysis, calling the oil and gas pipeline data in the database and performing univariate analysis and correlation analysis between multiple variables on the data.

[0045] S5. Data application, performing risk assessment on the oil and gas pipeline according to the analysis results of the data.

[0046] Specifically, before performing data analysis on the oil and gas transportation pipeline, collect the historical data and real-time data of the pipeline, and preprocess the collected data to improve the subsequent analysis efficiency. The preprocessed data is stored in the database for easy real-time access and retrieval. When performing data analysis, retrieve part of the pipeline data from the database, perform univariate analysis and correlation analysis between multiple variables on the data, and perform risk assessment on the pipeline according to the analysis results of the data.

[0047] The data acquisition in step S1 includes a real-time data acquisition part and a historical data acquisition part. The pipeline real-time data is various parameter data of the pipeline during the oil and gas transportation process. The pipeline historical data is the own data of the oil and gas pipeline before the oil and gas transportation task and the corresponding external environment data. The real-time data includes pressure index data, flow rate index data, and temperature index data. The pressure index data includes the pipeline operating pressure and the pipeline pressure fluctuation. The pipeline operating pressure is the pressure generated when the oil and gas flow in the pipeline. The pipeline pressure fluctuation is the change situation of the operating pressure in the pipeline. The flow rate index data includes the instantaneous flow rate and the cumulative flow rate. The instantaneous flow rate is the oil and gas flow rate passing through the cross-section of the oil and gas pipeline at a certain moment. The cumulative flow rate is the total amount of oil and gas passing through the pipeline within a period of time. The temperature index data includes the medium temperature and the pipe wall temperature. The medium temperature is the temperature situation of the oil and gas during the transportation process in the pipeline. The pipe wall temperature is the temperature of the oil and gas pipeline. The pipeline historical data includes environmental data, pipeline design data, and pipeline maintenance data. The environmental data is the terrain and landform, soil corrosion rate, and groundwater level data of the pipeline laying area. The pipeline design data includes the oil and gas pipeline diameter, pipeline material, and pipeline design flow rate. The pipeline maintenance data is the historical maintenance data of the oil and gas pipeline.

[0048] AsFigure 3 As shown in the figure, when collecting pipeline data, it includes pipeline historical data, such as the topography and geomorphology of the pipeline laying area. Different topographic regions have different degrees of influence on the pipeline. For example, mountainous areas may be affected by debris flows, etc. Multiple factors can affect the pipeline risk assessment process. Similarly, the soil corrosion rate and groundwater level data can be used to judge the corrosion rate of the pipeline and assist the risk assessment process. The pipeline design data includes the diameter of the oil and gas pipeline, the pipeline material, and the pipeline design flow. According to the pipeline design data, it can be compared with the real-time data to quickly judge abnormal data. For example, when the transported flow exceeds the pipeline design flow, an alarm is sent to the operation and maintenance personnel in a timely manner. The real-time data of the pipeline is the real-time monitoring data during the transportation process. The collected data is processed and analyzed subsequently to judge the risk of the pipeline.

[0049] Furthermore, the data preprocessing in step S2 includes missing value processing, outlier processing, and duplicate value processing. Missing value processing includes interpolation filling and deletion marking method. When data is missing at a certain moment in the oil and gas pipeline data, the interpolation method is used to fill the missing part based on the linear relationship of the data during this period. If the proportion of the missing part of the data is greater than the preset threshold (for example, 20%), the missing part of this section of data is deleted and the missing section is marked. Outlier processing is to mark the data points whose data exceeds the standard deviation of the mean plus or minus the preset multiple (for example, 3 times). Duplicate value processing is to remove the repeatedly collected data to reduce data redundancy.

[0050] As Figure 4 shown in the figure, when the collected data is processed for missing values, if the proportion of the missing part is less than 20%, the interpolation method is used to fill the data. When the proportion of the missing part is greater than 20%, the missing part is deleted and the missing part is marked. If the outlier data exceeds the standard deviation of the mean plus or minus 3 times, it is marked, and the duplicate values of the data are deleted to reduce data redundancy.

[0051] In the data storage step of step S3, the preprocessed data is uploaded to the local server for storage. The pipeline design data and pipeline maintenance data of the oil and gas pipeline are stored in the local server in the form of text files. The real-time data of the oil and gas pipeline is stored in the database of the local server. The real-time data of the oil and gas pipeline is stored in the database in the form of a data string corresponding one by one according to the pipeline number, data monitoring time, parameter category, and data. The local server is connected to the cloud server, and the data in the local server is communicated with the cloud server in real time.

[0052] As Figure 5As shown in the figure, the preprocessed data is uploaded to the local server for storage. It is stored in the database in the form of data strings corresponding one by one according to the pipeline number, data monitoring time, parameter category, and data. The local server can respond quickly when calling data, and the local server communicates with the cloud server in real time. The cloud server is used to back up the data to avoid data loss.

[0053] The data analysis in step S4 includes mean analysis, extreme value analysis, standard deviation analysis, and Pearson correlation analysis. Mean analysis is to calculate the average of various types of data. Mean analysis analyzes the average value of pipeline data to obtain the average level of the data. Extreme value analysis is to calculate the maximum and minimum values of the same type of data. Extreme value analysis is compared with the design data of the pipeline to evaluate the normal operation range of the oil and gas pipeline. Standard deviation analysis is to calculate the standard deviation of pipeline data. Standard deviation analysis can quickly judge the abnormality of the data, facilitating the maintenance personnel to quickly locate the abnormal pipe section. Pearson correlation analysis is used to calculate the linear relationship between two variables. The Pearson correlation coefficient calculated by Pearson correlation analysis is denoted as X. Taking the absolute value of X as the judgment basis, if the absolute value of the correlation coefficient X is in the range of 0.8 - 1.0, then the two variables are extremely strongly correlated; if the absolute value of the correlation coefficient X is in the range of 0.6 - 0.8, then the two variables are strongly correlated; if the absolute value of the correlation coefficient X is in the range of 0.4 - 0.6, then the two variables are moderately correlated; if the absolute value of the correlation coefficient X is in the range of 0.2 - 0.4, then the two variables are weakly correlated; if the absolute value of the correlation coefficient X is in the range of 0.0 - 0.2, then the two variables are extremely weakly correlated or uncorrelated. According to the Pearson correlation coefficient, the correlation between variables can be judged, and the status of the oil and gas pipeline can be judged according to the correlation.

[0054] As Figure 6 shown in the figure, when performing pipeline data analysis, the average of various types of data is calculated. The average value can be used to obtain the average level of a certain parameter. The standard deviation is calculated using the average value. Standard deviation analysis can quickly judge the abnormal area of the data, facilitating the maintenance personnel to quickly locate the abnormal pipe section. Extreme value analysis is to calculate the maximum and minimum values of the same type of data. Extreme value analysis is compared with the design data of the pipeline to evaluate the normal operation range of the oil and gas pipeline. Pearson correlation analysis can be used to calculate the correlation between two variables. For example, the correlation analysis between pressure and flow can be calculated using Pearson correlation to obtain the Pearson correlation coefficient between pressure and flow, so as to judge the flow according to the pressure data or calculate the pressure according to the flow, facilitating the construction of the subsequent risk prediction model.

[0055] In the data application of step S5, it includes abnormal data detection and early warning, clustering analysis to divide pipeline segments, and the establishment of a risk prediction model. In the abnormal data detection and early warning part, the pipeline monitoring data is compared with the pipeline data after data analysis in real time. When the data exceeds the extreme value, an alarm is processed. When it exceeds 3 times the average value, an alarm is also processed. In the clustering analysis to divide pipeline segments, the monitoring data points of the oil and gas pipeline are clustered according to similarity, so as to divide the pipeline segments into different categories, marked as normal operation pipeline segments and potential risk pipeline segments, and key monitoring is carried out on the potential risk pipeline segments. The risk prediction model constructs a linear regression model for variables based on the calculated Pearson correlation coefficient, and obtains the regression coefficient by fitting the data with the least squares method. Thus, during the operation of the pipeline, the risk of the oil and gas pipeline is evaluated according to the risk prediction model. As Figure 7 shown, according to the results calculated by the data analysis steps, the real-time data is compared and analyzed. When the data exceeds the extreme value, an alarm is processed. When it exceeds 3 times the average value, an alarm is also processed, and an alarm is sent to the operation and maintenance personnel in time to remind the staff to check for faults in time. The oil and gas pipeline is clustered according to the similarity of the monitoring data points, and the pipeline segments are divided into different categories, marked as normal operation pipeline segments and potential risk pipeline segments, among which key monitoring is carried out on the potential risk pipeline segments. Using the calculated Pearson correlation coefficient between variables, a risk prediction model is constructed, and the regression coefficient is obtained by fitting the data with the least squares method. The risk of the oil and gas pipeline is evaluated according to the constructed risk prediction model.

[0056] In the embodiment of the present invention, the historical data of the oil and gas transportation pipeline is collected and the real-time parameter data of the oil and gas transportation pipeline is acquired. The collected data is preprocessed, including missing value processing, outlier processing, and duplicate value processing. The processed data is uploaded to the database for easy real-time access and retrieval, improving the efficiency of subsequent data output. And the database is a local server, which communicates with the cloud server in real time. The cloud server is used to back up the data to avoid data loss. The oil and gas pipeline data in the database is called, and univariate analysis and correlation analysis between multiple variables are carried out on the data. According to the correlation analysis calculation, a risk prediction model is constructed, and the risk of the oil and gas pipeline is evaluated by integrating the analysis results of the data. By comparing and analyzing the historical data of the pipeline and combining the real-time pipeline data, real-time data similar to the historical data can be quickly identified, so as to discover potential risks in time. At the same time, according to the environmental factors of the pipeline laying, the risk degree can be accurately judged.

[0057] Next, the risk assessment device for the oil and gas transportation pipeline provided by the present invention is described. The risk assessment device for the oil and gas transportation pipeline described below can be mutually corresponding and referred to the risk assessment method for the oil and gas transportation pipeline described above.

[0058] Figure 8It is a schematic structural diagram of a risk assessment device for an oil and gas transportation pipeline provided by the present invention, specifically including: A data acquisition module 801, configured to acquire real-time parameter data and historical data of a target oil and gas transportation pipeline and perform data preprocessing on the real-time parameter data and historical data. For detailed description, refer to the relevant description corresponding to the above method embodiment, which will not be elaborated here.

[0059] A data analysis module 802, configured to perform data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result. Among them, the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlation of multiple variables in the real-time parameter data similar to the historical data. For detailed description, refer to the relevant description corresponding to the above method embodiment, which will not be elaborated here.

[0060] A risk assessment module 803, configured to perform risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result. For detailed description, refer to the relevant description corresponding to the above method embodiment, which will not be elaborated here.

[0061] Figure 9 It exemplifies a schematic structural diagram of an electronic device. As Figure 9 shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930, and a communication bus 940. Among them, the processor 910, the communication interface 920, and the memory 930 complete mutual communication through the communication bus 940. The processor 910 may call logical instructions in the memory 930 to execute a risk assessment method for an oil and gas transportation pipeline, and the method includes: acquiring real-time parameter data and historical data of a target oil and gas transportation pipeline and performing data preprocessing on the real-time parameter data and historical data; performing data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result. Among them, the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlation of multiple variables in the real-time parameter data similar to the historical data; performing risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

[0062] In addition, when the logical instructions in the above-mentioned memory 930 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0063] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the risk assessment method for an oil and gas transportation pipeline provided by the above-mentioned various methods. The method includes: obtaining real-time parameter data and historical data of a target oil and gas transportation pipeline and performing data preprocessing on the real-time parameter data and historical data; performing data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result. Among them, the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlations of multiple variables in the real-time parameter data similar to the historical data; performing risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

[0064] In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the risk assessment method for an oil and gas transportation pipeline provided by the above-mentioned various methods. The method includes: obtaining real-time parameter data and historical data of a target oil and gas transportation pipeline and performing data preprocessing on the real-time parameter data and historical data; performing data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result. Among them, the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlations of multiple variables in the real-time parameter data similar to the historical data; performing risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0066] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk assessment method for oil and gas transmission pipelines, characterized in that, Including: Obtaining real-time parameter data and historical data of the target oil and gas transmission pipeline and performing data preprocessing on the real-time parameter data and historical data; Performing data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result, where the single-variable analysis result includes single variables in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlation of multiple variables in the real-time parameter data similar to the historical data; Performing risk assessment on the target oil and gas transmission pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

2. The method according to claim 1, characterized in that, The single variable includes the average value of the pipeline's own data, the normal operation range of the pipeline, and the abnormal pipe sections of the pipeline; The performing data analysis on the real-time parameter data and historical data after data preprocessing to obtain a single-variable analysis result and a multi-variable correlation analysis result includes: Performing mean analysis, extreme value analysis, standard deviation analysis, and Pearson correlation analysis on the real-time parameter data and historical data; Determining the average value of the pipeline's own data of the target oil and gas transmission pipeline based on the mean analysis result; Comparing the extreme value analysis result with the pipeline design data of the target oil and gas transmission pipeline to determine the normal operation range of the target oil and gas transmission pipeline; Determining the abnormal pipe sections of the target oil and gas transmission pipeline based on the standard deviation analysis result; Determining the correlation of multiple variables based on the Pearson correlation analysis result.

3. The method according to claim 2, wherein The performing risk assessment on the target oil and gas transmission pipeline based on the single-variable analysis result and the multi-variable correlation analysis result includes: If the real-time parameter data is greater than the average value of the pipeline's own data and greater than a preset multiple, a risk alarm is issued; If the real-time parameter data is not within the normal operation range of the target oil and gas transmission pipeline, a risk alarm is issued; Clustering the abnormal pipe sections of the target oil and gas transmission pipeline according to similarity and performing corresponding risk monitoring on different types of abnormal pipe sections; Constructing a linear regression model based on the Pearson correlation coefficient corresponding to the correlation of multiple variables to obtain a risk prediction model; Performing risk assessment on the target oil and gas transmission pipeline based on the risk prediction model.

4. The method according to claim 3, characterized in that The clustering the abnormal pipe sections of the target oil and gas transmission pipeline according to similarity and performing corresponding risk monitoring on different types of abnormal pipe sections includes: Clustering the abnormal pipe sections of the target oil and gas transmission pipeline according to similarity to obtain normal operation pipe sections, potential risk pipe sections, and abnormal pipe sections; Monitoring the potential risk pipe sections and alarming the abnormal pipe sections.

5. The method according to claim 1, characterized in that The obtaining real-time parameter data and historical data of the target oil and gas transmission pipeline and performing data preprocessing on the real-time parameter data and historical data includes: Performing missing value processing, outlier processing, and duplicate value processing on the real-time parameter data and historical data; If the proportion of the missing data part is greater than a preset threshold, the missing data part is deleted and the missing section is marked.

6. The method according to claim 5, characterized in that, The method further includes: Upload the preprocessed real-time parameter data and historical data to the local server for storage in the form of data strings, so as to improve the response speed during the data call process.

7. A risk assessment device for an oil and gas transportation pipeline, characterized in that It includes: A data acquisition module, configured to acquire the real-time parameter data and historical data of the target oil and gas transportation pipeline and perform data preprocessing on the real-time parameter data and historical data; A data analysis module, configured to perform data analysis on the preprocessed real-time parameter data and historical data to obtain a single-variable analysis result and a multi-variable correlation analysis result. Among them, the single-variable analysis result includes the single variable in the real-time parameter data similar to the historical data, and the multi-variable correlation analysis result includes the correlation of multiple variables in the real-time parameter data similar to the historical data; A risk assessment module, configured to perform a risk assessment on the target oil and gas transportation pipeline based on the single-variable analysis result and the multi-variable correlation analysis result.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the risk assessment method of the oil and gas transportation pipeline according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the risk assessment method of the oil and gas transportation pipeline according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the risk assessment method of the oil and gas transportation pipeline according to any one of claims 1 to 6.

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