Pipeline engineering digital management method and system

Abstract

The present disclosure provides a pipeline engineering digital management method and system, belonging to the field of engineering management, which comprises the following steps: determining a target data set of a pipeline, the target data set comprising pipeline information data, geographic information data and environmental information data; inputting the target data set of the pipeline into a target analysis model to obtain a target analysis result of the pipeline; predicting the life cycle of the pipeline based on the target analysis result of the pipeline; and determining a management strategy for the pipeline engineering based on the life cycle of the pipeline. The pipeline engineering digital management method and system provided by the present disclosure can improve the management efficiency of the pipeline engineering.

Description

Technical Field

[0001] The present disclosure belongs to the field of engineering management, and more specifically, relates to a pipeline engineering digital management method and system. Background Art

[0002] Pipeline engineering involves the planning, design, construction, and maintenance of various piping systems and is widely used across multiple industries. Pipeline engineering can transport materials over long distances, so when transporting certain special materials, such as corrosive chemicals, pipelines must be corrosion-resistant, safe, and stable. Digital management of pipeline engineering can optimize and prevent problems at every stage of the project. Traditional pipeline engineering management suffers from inefficiencies, making it difficult to identify potential safety hazards in a timely manner. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a digital management method and system for pipeline engineering to improve the management efficiency of pipeline engineering.

[0004] A first aspect of the embodiments of the present disclosure provides a method for digital management of pipeline projects, comprising:

[0005] Determining a target data set for a pipeline, wherein the target data set includes pipeline information data, geographic information data, and environmental information data;

[0006] Inputting the target data set of the pipeline into a target analysis model to obtain a target analysis result of the pipeline;

[0007] Predicting a life cycle of the pipeline based on a target analysis result of the pipeline;

[0008] A management strategy for the pipeline project is determined based on the life cycle of the pipeline.

[0009] A second aspect of the embodiments of the present disclosure provides a pipeline engineering digital management system, including:

[0010] A data determination module is used to determine a target data set of a pipeline, wherein the target data set includes pipeline information data, geographic information data, and environmental information data;

[0011] An analysis result output module, configured to input the target data set of the pipeline into a target analysis model to obtain the target analysis result of the pipeline;

[0012] A life cycle prediction module, configured to predict the life cycle of the pipeline based on the target analysis result of the pipeline;

[0013] A management strategy determination module is used to determine a management strategy for a pipeline project based on the life cycle of the pipeline.

[0014] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for digital management of pipeline engineering when executing the computer program.

[0015] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for digital management of pipeline engineering are implemented.

[0016] The beneficial effects of the digital management method and system for pipeline engineering provided by the embodiments of the present disclosure are:

[0017] The present disclosure integrates pipeline information data, geographic information data, and environmental information data to form a comprehensive target data set, providing a basis for the management of pipeline projects and improving the accuracy and efficiency of pipeline management. Secondly, the present disclosure uses a target analysis model to conduct an in-depth analysis of the target data set to obtain target analysis results for the pipeline, which helps to timely discover potential safety hazards, not only reducing the risk of pipeline failures, but also providing a scientific basis for pipeline maintenance and upkeep. Finally, based on the pipeline life cycle prediction results, a more reasonable and effective pipeline project management strategy can be formulated, which helps to optimize the resource allocation of pipeline projects and increase the service life of pipelines. Therefore, the present disclosure can improve the management efficiency of pipeline projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A schematic diagram of a process flow of a digital management method for pipeline engineering provided in one embodiment of the present disclosure;

[0020] Figure 2 This is a structural block diagram of a pipeline engineering digital management system provided by one embodiment of the present disclosure;

[0021] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0023] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0024] Please refer to Figure 1 , Figure 1 A schematic diagram of a process flow of a pipeline engineering digital management method provided in one embodiment of the present disclosure, the method comprising:

[0025] S101: Determine a target data set of a pipeline, where the target data set includes pipeline information data, geographic information data, and environmental information data.

[0026] In this embodiment, determining the target data set of the pipeline includes:

[0027] Digitally model the design drawings of pipeline construction based on the building information model to determine design parameters and material information;

[0028] Deploy various sensors at the construction site to determine pipeline construction data;

[0029] Use monitoring equipment to monitor the operating status of the pipeline and determine the pipeline operation data;

[0030] Integrate design parameters, material information, pipeline construction data and pipeline operation data to determine pipeline information data;

[0031] Determine the geographic information data of the pipeline laying area based on the geographic information system;

[0032] Determine environmental information data based on information collection;

[0033] Integrate pipeline information data, geographic information data and environmental information data to determine the target data set of the pipeline.

[0034] Pipeline information data refers to various properties and operating status of the pipeline itself, including but not limited to the pipeline material, diameter, length, age, connection method, operating pressure, and flow rate. Pipeline materials can include cast iron, steel, or plastic pipes; diameters refer to diameters of varying sizes; length refers to the actual length of the pipeline; age refers to the time from construction and commissioning to the present; connection methods can include welding or flange connections; operating pressure refers to the pressure of objects within the pipeline; and flow rate refers to the volume of objects passing through the pipeline per unit time. Pipeline information data can be obtained based on engineering drawings and by installing sensors on the pipeline.

[0035] Geographic information data is information about the pipeline's geographic location, including topography, geological structure, altitude, and longitude and latitude coordinates. Topography can include plains, mountains, hills, or basins; geological structure can include rock type, soil texture, and soil pH; altitude is the elevation of the pipeline's location; and longitude and latitude coordinates are precise geographic coordinates that can be used to locate the pipeline on a map. Geographic information data can be obtained using a geographic information system (GIS) for the area where the pipeline is laid.

[0036] Environmental information data refers to various factors of the pipeline's surrounding environment, including temperature, humidity, precipitation, and whether there are pollution sources nearby. This embodiment can obtain historical meteorological data of the pipeline area and whether there are industrial pollution sources around the pipeline through information collection.

[0037] The target dataset of pipelines is a set of data collected and organized for digital management and analysis of pipeline projects, and provides an analysis basis for pipeline construction, pipeline maintenance, etc.

[0038] S102: Inputting the target data set of the pipeline into the target analysis model to obtain the target analysis result of the pipeline.

[0039] In this embodiment, the target analysis model is a mathematical model, algorithmic model, etc. selected or constructed to implement pipeline analysis. It has certain analysis logic and calculation rules, and can process and analyze the input target data set to mine the potential information and patterns in the data.

[0040] The target analysis results for pipelines are obtained by processing the target dataset of the pipeline according to the target analysis model. The pipeline-related analysis results can be used to evaluate the pipeline status and predict future trends. The pipeline status evaluation can be based on corrosion rate, remaining life, etc., and the future trend prediction can be based on the pressure change or corrosion condition of the pipeline in the next few years, providing a basis for pipeline design and planning.

[0041] The target analysis model can be a trained regression model. The target analysis model construction process can be as follows:

[0042] First, perform data preprocessing on the target dataset of the pipeline to determine the standardized pipeline dataset.

[0043] Data preprocessing includes data conversion, data cleaning, and data standardization. Data conversion converts individual data into numerical data to facilitate processing by the regression model. The purpose of data cleaning is to check for missing values ​​or outliers in the dataset. Missing values ​​can be filled with the mean or median, and outliers can be removed. Data standardization normalizes or normalizes the data to enable more uniform subsequent processing.

[0044] Second, the standardized pipeline dataset is divided to determine the training set and test set for the regression model.

[0045] The standardized pipeline dataset is divided into a training set and a test set according to a certain ratio (e.g., 8:2 or 7:3). The training set is used to train the regression model, and the test set is used to evaluate the regression model's performance. Furthermore, this embodiment employs random sampling to ensure the randomness and representativeness of the partitioning, ensuring that the training and test sets can better reflect the characteristics of the overall data.

[0046] Third, an initial training regression model is determined based on the training set training regression model.

[0047] The regression model uses the feature variables in the training set (i.e., the standardized pipeline dataset) as input and the target variable (i.e., pipeline status) as output, allowing the model to learn the relationship between the features and the target. For example, to predict the corrosion rate of a pipeline, all factors affecting corrosion are used as input features, and the model is trained using the features in the training set and the corresponding known corrosion rates.

[0048] Fourth, the initial training regression model is evaluated and optimized to determine the target analysis model.

[0049] This embodiment uses data from the test set to evaluate the initially trained regression model. Evaluation metrics may include root mean square error (RMS), coefficient of determination, and the like. If the evaluation result indicates that the model performance does not meet the requirements, the parameters of the initially trained regression model may be adjusted to continue training until the evaluation result indicates that the model performance meets the requirements. The target analysis model is then determined. Model performance that does not meet the requirements is defined as an evaluation metric less than a preset evaluation threshold, while model performance that meets the requirements is defined as an evaluation metric greater than or equal to a preset evaluation threshold. The evaluation threshold is dynamically set based on actual needs.

[0050] S103: Predicting the life cycle of the pipeline based on the target analysis result of the pipeline.

[0051] In this embodiment, the life cycle of a pipeline is the entire period from the design, construction, commissioning to the final scrapping or replacement of the pipeline, and may also be the remaining time from the current state to the point of being unusable.

[0052] If the target analysis result of the pipeline is the corrosion rate, the life cycle of the pipeline is predicted based on the first formula;

[0053] The first formula is as follows:

[0054] ;

[0055] ;

[0056] in, is the wall thickness of the pipe, is the corrosion rate, is the design safety factor of the pipeline, For the life cycle of the pipeline, is the allowable stress of the material, It is the maximum stress that the pipeline bears under working conditions;

[0057] is the wall thickness of the pipe, which indicates the initial thickness of the pipe and is an inherent property of the pipe and can be obtained from the design specifications of the pipe;

[0058] The design safety factor of the pipeline is a ratio determined based on the design requirements and service conditions of the pipeline, reflecting the degree of corrosion at which the pipeline is considered unreliable or unsafe and needs to be replaced or repaired.

[0059] is the allowable stress of the material, which is determined according to the strength characteristics of the material;

[0060] ,in is the pressure in the pipe, is the average diameter of the pipe;

[0061] If the pipeline project meets the first condition, the design safety factor of the pipeline is greater than or equal to the first safety threshold, and the design safety factor of the pipeline is increased by the first step;

[0062] If the pipeline project does not meet the first condition, the design safety factor of the pipeline is less than the first safety threshold, and the design safety factor of the pipeline is reduced by the first step;

[0063] The first condition is that the resident density is greater than or equal to the first density. The first density can be obtained based on experience or calculated based on the number of residents and the area. The first safety threshold is preset based on the actual scenario. The first step is long. The calculation formula is as follows:

[0064] ;

[0065] in, is the maximum value of the design safety factor, is the minimum value of the design safety factor, is the risk level of the pipeline project; for example, the maximum value of the design safety factor can be 10, the minimum value of the design safety factor can be 1, the risk level of the pipeline project can be the first risk, that is, low risk, with a value of 1, and the risk level of the pipeline project can also be the second risk, that is, high risk, with a value of 2.

[0066] S104: Determine a management strategy for the pipeline project based on the pipeline life cycle.

[0067] In this embodiment, by analyzing the pipeline lifecycle, management strategies for different pipeline project stages can be determined. These strategies can include maintenance and upgrade strategies. This embodiment develops corresponding management plans based on the pipeline lifecycle stage, including but not limited to daily inspection plans, maintenance measures, repair decisions, and upgrade and renovation plans.

[0068] If the pipeline life cycle is in the first stage of the life cycle, the first management strategy is used as the management strategy for the pipeline project;

[0069] If the pipeline life cycle is in the second stage of the life cycle, the second management strategy is used as the management strategy for the pipeline project;

[0070] If the pipeline life cycle is in the third stage of the life cycle, the third management strategy is used as the management strategy for the management project;

[0071] Among them, the first stage, the second stage and the third stage are in different periods, and the first management strategy, the second management strategy and the third management strategy are handled differently.

[0072] The first phase involves the construction of new pipelines, before they enter operation. The primary management strategy is quality supervision and acceptance. For example, when laying a new natural gas pipeline, management strategies include quality inspection of pipeline materials to ensure compliance with design standards. During construction, professionals monitor welding quality and pipeline laying slopes in real time to ensure quality. Furthermore, detailed pipeline records are established to record various pipeline parameters and construction process information, providing foundational data for subsequent management.

[0073] The second stage is a normal operation stage of the pipeline, and the second management strategy is to perform daily inspection or maintenance at a first frequency. Taking a city drainage pipeline as an example, a professional is regularly arranged to inspect the pipeline to check whether there are problems such as blockage and leakage. According to the use condition of the pipeline and the surrounding environmental factors, a reasonable cleaning plan is made, such as high-pressure flushing of the sewage pipeline once every half year to prevent the pipeline from being blocked due to dirt accumulation. At the same time, the sensor technology is used to monitor the flow, pressure and other parameters in the pipeline in real time, and once an abnormality is found, a timely warning is given so that corresponding maintenance measures can be taken.

[0074] The third stage is an aging stage of the pipeline, that is, the pipeline close to or has exceeded the designed service life, and the third management strategy is to evaluate the remaining life of the pipeline and determine the updating and reconstruction plan.

[0075] From the above, it can be concluded that the present disclosure forms a comprehensive target data set by integrating pipeline information data, geographic information data and environmental information data, provides a basis for the management of pipeline engineering, and improves the accuracy and efficiency of pipeline management. Secondly, the present disclosure uses a target analysis model to deeply analyze the target data set to obtain a target analysis result of the pipeline, which helps to timely discover potential safety hazards, not only can reduce the risk of pipeline failure, but also can provide a scientific basis for the maintenance and care of the pipeline. Finally, based on the life cycle prediction result of the pipeline, a more reasonable and effective pipeline engineering management strategy can be made, which helps to optimize the resource allocation of the pipeline engineering and improve the service life of the pipeline. Therefore, the present disclosure can improve the management efficiency of the pipeline engineering.

[0076] In an embodiment of the present disclosure, the target data set of the pipeline is determined, comprising:

[0077] The pipeline information data and the geographic information data are fused to obtain the associated features of the pipeline;

[0078] The pipeline information data, the geographic information data, the environmental information data and the associated features are integrated to obtain the target data set of the pipeline.

[0079] In an embodiment of the present disclosure, the pipeline information data and the geographic information data are fused to obtain the associated features of the pipeline, comprising:

[0080] The pipeline information data and the geographic information data are fused based on an association rule mining algorithm to determine the associated features of the pipeline.

[0081] In an embodiment of the present disclosure, the pipeline information data and the geographic information data are fused based on an association rule mining algorithm to determine the associated features of the pipeline, comprising:

[0082] A plurality of pipeline data of the pipeline information data are determined;

[0083] determining a plurality of geographic data of the geographic information data;

[0084] determining a first support degree corresponding to any pipeline data based on the any pipeline data, and generating a first frequent item set based on the first support degree;

[0085] determining a second support degree corresponding to any geographic data based on the any geographic data, and generating a second frequent item set based on the second support degree;

[0086] constructing a frequent pattern tree based on the first frequent item set and the second frequent item set;

[0087] mining a frequent item set based on the frequent pattern tree, and determining an association feature of the pipeline.

[0088] In the embodiment, there is a potential association relationship and feature between the pipeline information data and the geographic information data, so that the fusion analysis of the two types of data can determine the association feature of the pipeline information data and the geographic information data. The association feature is feature information capable of reflecting the internal relationship between the pipeline information data and the geographic information data.

[0089] For example, in a city gas pipeline project, the pipeline information data includes information such as that the material of the gas pipeline is a steel pipe, the pipe diameter is 300 mm, and the service life is 10 years; and the geographic information data shows that the pipeline is laid in a mountainous area and the geological structure is rock. Through the fusion of the two types of data, it can be found that under the mountainous rock geological environment, the 300 mm steel pipe with a service life of 10 years is more likely to have a certain specific corrosion condition, which is an association feature.

[0090] After the association feature is determined, the pipeline information data, the geographic information data, the environmental information data, and the association feature can be summarized to form a target data set of the pipeline. The data set can be formed by a plurality of vectors.

[0091] The association rule mining algorithm is a data mining algorithm, which is used to find the association or correlation between item sets in the data set. In the embodiment, the appearance frequency and the co-occurrence mode of different items in the data set are analyzed to find out which items have a strong association.

[0092] For example, when analyzing the gas pipeline data, the association rule mining algorithm can find that in the area of the mountain (geographic information) and the soil with acidity (geographic information), the gas pipeline of cast iron material (pipeline information) is more likely to have corrosion (an association feature), which is the association relationship mined from the two types of data by the association rule mining algorithm.

[0093] Extract multiple specific data items from the pipeline information data, each of which represents a specific attribute or characteristic value of the pipeline. For example, each pipeline data item can include specific information such as the material, diameter, and service life of the pipeline.

[0094] Extract multiple specific data items from the geographic information data, each of which represents a specific attribute or characteristic value in the geographic environment. For example, each geographic data item can be topography type, soil pH, altitude, etc.

[0095] The first support is the frequency with which an itemset (i.e., a single piece of pipeline data) appears in all pipeline data, reflecting the prevalence of the itemset. For example, the proportion of a particular pipeline material that appears in all pipeline data represents the support of that material. A first frequent itemset is a set of pipeline data whose first support is greater than or equal to a preset first support threshold. The first support threshold can be dynamically set based on actual needs.

[0096] For each pipeline data point, its frequency of occurrence in the entire pipeline information data (i.e., the first support) is calculated. Then, pipeline data points whose first support is greater than or equal to the first support threshold are grouped together to form a first frequent itemset. This is done to identify important and common pipeline data features in the pipeline information data. Pipeline information data also includes multiple types of pipeline data, each corresponding to a different type.

[0097] The calculation formula for the first support is:

[0098]

[0099] in, For item sets The support of any pipeline data support level; For item sets the number of relevant types; For the The weight of the pipeline data; For item sets In the The number of times a pipeline data type appears in the sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on support; For the Itemsets in pipeline data With the Itemsets in pipeline data The correlation between them can be calculated using the Pearson correlation coefficient; The total number of pipeline information datasets.

[0100] The second support is calculated based on the geographic data, and the second frequent itemset is a set of geographic data whose second support is greater than or equal to a second support threshold. The second support threshold can be dynamically adjusted according to actual conditions.

[0101] For each geographic data, its frequency of occurrence in the entire geographic information data (i.e., the second support) is calculated, and then the geographic data sets whose second support is greater than or equal to the second support threshold are grouped together to form a second frequent item set in order to screen out the more important and common geographic data features in the geographic information data.

[0102] The calculation formula for the second support is:

[0103]

[0104] in, For item sets The support of any geographic data support level; For item sets the number of relevant types; For the The weight of geographical data; For item sets In the The number of times a geographic data type appears in a sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on support; For the Itemsets in geographic data With the Itemsets in geographic data The correlation between them can be calculated using the Pearson correlation coefficient; The total number of geographic information datasets.

[0105] A frequent pattern tree is a data structure used to mine frequent item sets (i.e., first frequent item sets and second frequent item sets). The frequent pattern tree consists of a root node, an item prefix subtree, and an item header table. The root node is marked "null," the item prefix subtree contains all frequent item set information, and the item header table stores each frequent item and its corresponding linked list in the tree. This embodiment compresses and stores the frequent item set information in the pipeline information data and geographic information data, avoiding multiple scans of the dataset and improving the efficiency of mining frequent itemsets.

[0106] By mining the constructed frequent pattern tree and determining the item sets that frequently co-occur between pipeline information data and geographic information data, the correlation characteristics of the pipeline can be obtained.

[0107] As can be seen from the above, this embodiment, by fusing pipeline information data with geographic information data, can deeply mine the associated characteristics of pipelines. These characteristics provide an important reference for pipeline management and maintenance. In addition, the use of association rule mining algorithms for data fusion can efficiently extract valuable information from large amounts of data, avoiding the tedious and subjective nature of manual screening and improving the efficiency and accuracy of data processing.

[0108] In one embodiment of the present disclosure, inputting a target data set of a pipeline into a target analysis model to obtain a target analysis result of the pipeline includes:

[0109] Divide the target dataset into multi-class pipeline datasets based on clustering algorithm;

[0110] Based on the analysis of each type of pipeline data set, the impact index of the pipeline data set is determined, and the impact index is the target analysis result.

[0111] In this embodiment, the target dataset is divided into multiple categories of pipeline datasets based on a clustering algorithm, including:

[0112] Determine multiple initial cluster centers of the target data set;

[0113] Each initial cluster center is updated based on the distance between the target dataset sample and the initial cluster center to obtain multiple target cluster centers, each of which corresponds to a type of pipeline dataset.

[0114] Assume that the target dataset has pipeline samples, each sample is a dimensional vector , ,in, Indicates the The first sample Features

[0115] First, random selection Points as initial cluster centers , , each cluster center is also a dimensional vector, i.e. ;

[0116] Second, for each sample , calculate it to The distance between the initial cluster centers is:

[0117] ;

[0118] The sample Assigned to the cluster with the nearest initial cluster center In Chinese, that is:

[0119]

[0120] Third, recalculate each cluster The center of the new cluster center It is a cluster The mean of all samples in is calculated as:

[0121]

[0122] in, Represents a cluster The number of samples in

[0123] Fourth, the second and third steps are repeated continuously until the cluster center no longer changes or the preset maximum number of iterations is reached. At this time, the target dataset is divided into multiple pipeline datasets.

[0124] Influencing indicators are parameters or variables that have a significant impact on the operation, performance, lifespan, etc. of each type of pipeline dataset. For example, for a certain type of pipeline, it can be found that "corrosion rate" and "pressure loss" are key indicators affecting its normal operation and lifespan.

[0125] In this embodiment, each group of pipeline data sets with similar characteristics obtained by clustering is analyzed separately to find factors or indicators that have an impact on this type of pipeline. These factors or indicators can be target analysis results and can be used to guide management decisions for different pipelines.

[0126] As can be seen from the above, this embodiment uses a clustering algorithm to divide complex target datasets into multiple categories, making subsequent analysis more targeted and improving efficiency. Secondly, detailed analysis of each pipeline dataset accurately identifies key influencing indicators for each type of pipeline, providing a scientific basis for pipeline management and maintenance. This embodiment not only enhances analysis accuracy but also helps to promptly identify potential issues, optimize pipeline operation strategies, and ensure the safe and stable operation of pipeline projects.

[0127] In one embodiment of the present disclosure, analyzing each type of pipeline dataset to determine an impact index of the type of pipeline dataset includes:

[0128] Each type of pipeline dataset is input into the target analysis model to obtain the impact index of this type of pipeline dataset.

[0129] In one embodiment of the present disclosure, the target analysis model is trained based on each type of historical pipeline dataset and the impact index of the historical pipeline dataset.

[0130] In this embodiment, the target analysis model may be a trained regression model or a statistical analysis model.

[0131] The historical pipeline dataset is a collection of historical pipeline data, encompassing information on pipelines of different time periods and types. Each historical pipeline dataset contains information on pipelines of the same type at different times. The analytical model is a regression model, using each historical pipeline dataset as input and the impact indicators of that historical pipeline dataset as output. By adjusting and optimizing the model parameters, the regression model learns the relationship between input and output, enabling it to accurately predict and analyze new data. After training and evaluation, the target analytical model is obtained.

[0132] By inputting each type of pipeline data set into the target analysis model, the corresponding impact indicators can be predicted.

[0133] As can be seen from the above, this embodiment ensures that the analysis model accurately captures the unique characteristics of each type of pipeline data through model training, thereby improving the accuracy of determining influencing indicators. This embodiment enhances the pertinence and practicality of pipeline data analysis, helping to improve the overall operational efficiency of pipeline projects.

[0134] Corresponding to a digital management method for pipeline engineering in the above embodiment, Figure 2 This is a structural block diagram of a digital management system for pipeline engineering provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The pipeline engineering digital management system 20 includes: a data determination module 21, an analysis result output module 22, a life cycle prediction module 23 and a management strategy determination module 24.

[0135] The data determination module 21 is used to determine the target data set of the pipeline, which includes pipeline information data, geographic information data and environmental information data;

[0136] An analysis result output module 22 is used to input the target data set of the pipeline into the target analysis model to obtain the target analysis result of the pipeline;

[0137] A life cycle prediction module 23 is used to predict the life cycle of the pipeline based on the target analysis results of the pipeline;

[0138] The management strategy determination module 24 is used to determine the management strategy of the pipeline project based on the life cycle of the pipeline.

[0139] In one embodiment of the present disclosure, the data determination module 21 is specifically configured to:

[0140] Fuse pipeline information data and geographic information data to obtain pipeline correlation features;

[0141] The pipeline information data, geographic information data, environmental information data and related features are integrated to obtain the target pipeline data set.

[0142] In one embodiment of the present disclosure, the data determination module 21 is further configured to:

[0143] Based on the association rule mining algorithm, pipeline information data and geographic information data are integrated to determine the association characteristics of the pipeline.

[0144] In one embodiment of the present disclosure, the data determination module 21 is further configured to:

[0145] Determine multiple pipeline data of pipeline information data;

[0146] determining a plurality of geographic data of geographic information data;

[0147] Determine a first support corresponding to any pipeline data based on any pipeline data, and generate a first frequent itemset based on the first support;

[0148] determining a second support corresponding to any geographic data based on any geographic data, and generating a second frequent itemset based on the second support;

[0149] Construct a frequent pattern tree based on the first frequent item set and the second frequent item set;

[0150] Mining frequent item sets based on frequent pattern trees to determine the associated features of pipelines.

[0151] In one embodiment of the present disclosure, the analysis result output module 22 is specifically configured to:

[0152] Divide the target dataset into multi-class pipeline datasets based on clustering algorithm;

[0153] Based on the analysis of each type of pipeline data set, the impact index of the pipeline data set is determined, and the impact index is the target analysis result.

[0154] In one embodiment of the present disclosure, the analysis result output module 22 is further configured to:

[0155] Each type of pipeline dataset is input into the target analysis model to obtain the impact index of this type of pipeline dataset.

[0156] In one embodiment of the present disclosure, the target analysis model is trained based on each type of historical pipeline dataset and the impact index of the historical pipeline dataset.

[0157] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 24 are shown.

[0158] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0159] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0160] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0161] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of a pipeline engineering digital management method provided by the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0162] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0163] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0164] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0165] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0167] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0168] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0169] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A digital management method for pipeline engineering, characterized in that: include: Determining a target data set for a pipeline, wherein the target data set includes pipeline information data, geographic information data, and environmental information data; Inputting the target data set of the pipeline into a target analysis model to obtain a target analysis result of the pipeline; Predicting a life cycle of the pipeline based on a target analysis result of the pipeline; determining a management strategy for the pipeline project based on the life cycle of the pipeline; The target dataset of the pipeline is determined, including: fusing the pipeline information data and the geographic information data to obtain associated features of the pipeline; Integrating the pipeline information data, the geographic information data, the environmental information data, and the associated features to obtain a target data set for the pipeline; The fusing of the pipeline information data and the geographic information data to obtain the associated features of the pipeline includes: fusing the pipeline information data and the geographic information data based on an association rule mining algorithm to determine association features of the pipeline; The fusing of the pipeline information data and the geographic information data based on the association rule mining algorithm to determine the association features of the pipeline includes: Determine a plurality of pipeline data of the pipeline information data; determining a plurality of geographic data of the geographic information data; Based on any pipeline data, a first support corresponding to any pipeline data is determined, and a first frequent itemset is generated based on the first support. The calculation formula of the first support is: in, For item sets The support of any pipeline data support level; For item sets the number of relevant types; For the The weight of the pipeline data; For item sets In the The number of times a pipeline data type appears in the sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on support; For the Itemsets in pipeline data With the Itemsets in pipeline data The correlation between them can be calculated using the Pearson correlation coefficient; is the total number of pipeline information datasets; Based on any geographic data, a second support corresponding to any geographic data is determined, and a second frequent item set is generated based on the second support. The calculation formula of the second support is: in, For item sets The support of any geographic data support level; For item sets the number of relevant types; For the The weight of geographical data; For item sets In the The number of times a geographic data type appears in a sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on support; For the Itemsets in geographic data With the Itemsets in geographic data The correlation between them can be calculated using the Pearson correlation coefficient; is the total number of geographic information datasets; constructing a frequent pattern tree based on the first frequent item set and the second frequent item set; Frequent item sets are mined based on the frequent pattern tree to determine the associated features of the pipeline.

2. A pipeline engineering digital management method according to claim 1, characterized in that: Inputting the target data set of the pipeline into the target analysis model to obtain the target analysis result of the pipeline includes: Dividing the target data set into multiple categories of pipeline data sets based on a clustering algorithm; An analysis is performed based on each type of pipeline data set to determine an impact index of the type of pipeline data set, where the impact index is the target analysis result.

3. A digital management method for pipeline engineering according to claim 2, characterized in that: The analysis based on each type of pipeline dataset to determine the impact indicator of the pipeline dataset includes: Each type of pipeline dataset is input into the target analysis model to obtain the impact index of this type of pipeline dataset.

4. A digital management method for pipeline engineering according to claim 1, characterized in that: The target analysis model is trained based on each type of historical pipeline dataset and the impact indicators of this type of historical pipeline dataset.

5. A digital management system for pipeline engineering, characterized in that: include: A data determination module is used to determine a target data set of a pipeline, wherein the target data set includes pipeline information data, geographic information data, and environmental information data; An analysis result output module, configured to input the target data set of the pipeline into a target analysis model to obtain the target analysis result of the pipeline; A life cycle prediction module, configured to predict the life cycle of the pipeline based on the target analysis result of the pipeline; A management strategy determination module, configured to determine a management strategy for a pipeline project based on the life cycle of the pipeline; The data determination module is specifically used to: Fuse pipeline information data and geographic information data to obtain pipeline correlation features; Integrate pipeline information data, geographic information data, environmental information data and related features to obtain the target pipeline data set; The data determination module is further used to: Based on the association rule mining algorithm, pipeline information data and geographic information data are integrated to determine the associated characteristics of the pipeline; The data determination module is further used to: Determine multiple pipeline data of pipeline information data; determining a plurality of geographic data of geographic information data; Based on any pipeline data, a first support corresponding to any pipeline data is determined, and a first frequent itemset is generated based on the first support. The calculation formula of the first support is: in, For item sets The support of any pipeline data support level; For item sets the number of relevant types; For the The weight of the pipeline data; For item sets In the The number of times a pipeline data type appears in the sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on support; For the Itemsets in pipeline data With the Itemsets in pipeline data The correlation between them can be calculated using the Pearson correlation coefficient; is the total number of pipeline information datasets; Based on any geographic data, the second support corresponding to any geographic data is determined, and a second frequent item set is generated based on the second support. The calculation formula of the second support is: in, For item sets The support of any geographic data support level; For item sets the number of relevant types; For the The weight of geographical data; For item sets In the The number of times a geographic data type appears in a sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on support; For the Itemsets in geographic data With the Itemsets in geographic data The correlation between them can be calculated using the Pearson correlation coefficient; is the total number of geographic information datasets; Construct a frequent pattern tree based on the first frequent item set and the second frequent item set; Mining frequent item sets based on frequent pattern trees to determine the associated features of pipelines.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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