Pipeline engineering digital management method and system

Abstract

The invention provides a pipeline engineering digital management method and system, and belongs to the field of engineering management.The method comprises the steps that a target data set of a pipeline is determined, and the target data set comprises pipeline information data, geographic information data and environment 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 of the pipeline project based on the life cycle of the pipeline. According to the digital management method and system for the pipeline project, the management efficiency of the pipeline project can be improved.

Description

Technical Field

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

[0002] Pipeline engineering involves the planning, design, construction and maintenance of various pipeline systems and is widely used in multiple industries. Pipeline engineering can be responsible for long-distance transportation, so when transporting some special objects, such as corrosive chemical materials, the pipeline is required to have corrosion resistance, safety and stability. Digital management of pipeline engineering can optimize and prevent various stages of pipeline engineering. In traditional pipeline engineering management, there is a problem of lack of efficiency in pipeline engineering management, and it is difficult to detect potential safety hazards in a timely manner. Summary of the invention

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

[0004] A first aspect of an embodiment of the present disclosure provides a pipeline engineering digital management method, including: Determine a target data set for the 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; A management strategy for the pipeline project is determined based on the life cycle of the pipeline.

[0005] A second aspect of the embodiments of the present disclosure provides a pipeline engineering digital management system, including: A data determination module, 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, used for inputting 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, used to predict the life cycle of the pipeline based on the target analysis result of the pipeline; A management strategy determination module is used to determine a management strategy for a pipeline project based on the life cycle of the pipeline.

[0006] 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.

[0007] 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.

[0008] The beneficial effects of the pipeline engineering digital management method and system provided by the embodiments of the present disclosure are: The present disclosure integrates pipeline information data, geographic information data and environmental information data to form a comprehensive target data set, which provides a basis for the management of pipeline projects and improves 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 the target analysis results of the pipeline, which helps to timely discover potential safety hazards, not only to reduce the risk of pipeline failures, but also to provide a scientific basis for the maintenance and care of the pipeline. Finally, based on the life cycle prediction results of the pipeline, a more reasonable and effective pipeline project management strategy can be formulated, which helps to optimize the resource allocation of the pipeline project and increase the service life of the pipeline. Therefore, the present disclosure can improve the management efficiency of pipeline projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0010] Figure 1 A schematic diagram of a process flow of a pipeline engineering digital management method provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of a pipeline engineering digital management system provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear 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 obstructing the description of the present disclosure with unnecessary details.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0013] Please refer to Figure 1 , Figure 1 A schematic diagram of a pipeline engineering digital management method provided in an embodiment of the present disclosure, the method comprising: 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.

[0014] In this embodiment, determining the target data set of the pipeline includes: Digitally model the design drawings of pipeline construction based on the building information model to determine the design parameters and material information; Deploy various sensors at the construction site to determine pipeline construction data; Use monitoring equipment to monitor the operation status of the pipeline and determine the pipeline operation data; Integrate design parameters, material information, pipeline construction data and pipeline operation data to determine pipeline information data; Determine the geographic information data of the pipeline laying area based on the geographic information system; Determine environmental information data based on information collection; Integrate pipeline information data, geographic information data and environmental information data to determine the target data set of the pipeline.

[0015] Pipeline information data refers to data on various properties and operating status of the pipeline itself, including but not limited to the material, diameter, length, service life, connection method, operating pressure, and flow rate of the pipeline. The material of the pipeline can be cast iron, steel pipe, plastic pipe, etc. The diameter is the diameter size of different specifications, the length is the actual length of the pipeline, the service life is the time from the completion and commissioning to the present, the connection method can be welding or flange connection, etc., the operating pressure is the pressure value of the object in the pipeline, and the flow rate is the volume of the object passing through the pipeline per unit time. Pipeline information data can be obtained based on engineering drawings and by installing sensors on the pipeline.

[0016] Geographic information data is the relevant information about the geographical location of the pipeline, which may include topography, geological structure, altitude, and longitude and latitude coordinates. Among them, topography can be plains, mountains, hills or basins, etc., geological structure can be rock type, soil texture and soil acidity, etc., altitude is the altitude value of the location of the pipeline, and longitude and latitude coordinates are accurate geographical location coordinates, which can be used to locate the pipeline on the map. Geographic information data can be obtained with the help of a geographic information system in the area where the pipeline is laid.

[0017] Environmental information data refers to various factors of the environment around the pipeline, including temperature, humidity, precipitation, and whether there are pollution sources around the pipeline, etc. This embodiment can obtain historical meteorological data of the area where the pipeline is located and whether there are industrial pollution sources around the pipeline, etc. through information collection.

[0018] The target data set of the pipeline 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.

[0019] S102: Inputting the target data set of the pipeline into the target analysis model to obtain the target analysis result of the pipeline.

[0020] In this embodiment, the target analysis model is a mathematical model, algorithm 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 rules in the data.

[0021] The target analysis result of the pipeline is to process the target data set of the pipeline according to the target analysis model. The obtained analysis results related to the pipeline can be the evaluation of the pipeline status, the prediction of future trends, etc. The evaluation of the pipeline status can be the corrosion rate, remaining life, etc., and the prediction of future trends can be the pressure change or corrosion of the pipeline in the next few years, providing a basis for the design and planning of the pipeline.

[0022] The target analysis model can be a trained regression model, and the construction process of the target analysis model can be as follows: First, preprocess the target dataset of the pipeline and determine the standardized pipeline dataset.

[0023] Data preprocessing can include data conversion, data cleaning, and data standardization. Data conversion is to convert each data into numerical data to facilitate regression model processing. The purpose of data cleaning is to check whether there are missing values ​​or outliers in the data set. For missing values, you can consider using the mean or median value to fill, and for outliers, you can consider removing them. Data standardization is to standardize or normalize the data so that subsequent processing can be more unified.

[0024] Second, the standardized pipeline dataset is divided to determine the training set and test set of the regression model.

[0025] The standardized pipeline data set is divided into a training set and a test set according to a certain ratio (such as 8:2 or 7:3). The training set is used to train the regression model, and the test set is used to evaluate the performance of the regression model. At the same time, this embodiment can use a random sampling method to ensure the randomness and representativeness of the division, ensuring that the training set and the test set can better reflect the characteristics of the overall data.

[0026] Third, an initial training regression model is determined based on the training set training regression model.

[0027] The feature variables in the training set (i.e., the standardized pipeline dataset) are used as input, and the target variable (i.e., pipeline status) is used as output, so that the regression model can learn the relationship between the features and the target. For example, to predict the corrosion rate of a pipeline, all factors that affect corrosion are used as input features, and the features of the training set and the corresponding known corrosion rates are used for model training.

[0028] Fourth, the initial training regression model is evaluated and optimized to determine the target analysis model.

[0029] This embodiment uses the data of the test set to evaluate the initial training regression model, and the evaluation indicators may be root mean square error, determination coefficient, etc. If the evaluation result is that the model performance does not meet the requirements, the parameters of the initial training regression model may be adjusted to continue training until the evaluation result is that the model performance meets the requirements, and then the target analysis model is determined. Among them, the model performance does not meet the requirements when the evaluation index is less than the preset evaluation threshold, and the model performance meets the requirements when the evaluation index is greater than or equal to the preset evaluation threshold. The evaluation threshold is determined dynamically according to actual needs.

[0030] S103: Predicting the life cycle of the pipeline based on the target analysis result of the pipeline.

[0031] In this embodiment, the life cycle of the pipeline is the entire period of time from the design, construction, commissioning to the final scrapping or replacement of the pipeline, and can also be the remaining time from the current state to the point of being unusable.

[0032] 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; The first formula is as follows: ; ; 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; 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 through the design specifications of the pipe; The design safety factor of the pipeline is a ratio determined according to 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; is the allowable stress of the material, which is determined according to the strength characteristics of the material; ,in is the pressure in the pipe, is the average diameter of the pipe; 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; 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; The first condition is that the population density is greater than or equal to the first density. The first density can be obtained from experience or calculated based on the number of residents and the area. The first safety threshold is preset according to the actual scenario. The first step is long. The calculation formula is as follows: ; 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.

[0033] S104: Determine a management strategy for the pipeline project based on the life cycle of the pipeline.

[0034] In this embodiment, by analyzing the life cycle of the pipeline, the management strategy of the pipeline project at different stages can be determined, and the management strategy can include the maintenance strategy and update strategy of the pipeline project. According to the life cycle stage of the pipeline, this embodiment formulates a corresponding management plan, including but not limited to daily inspection plans, maintenance measures, maintenance decisions, and update and transformation plans.

[0035] If the life cycle of the pipeline is in the first stage of the life cycle, the first management strategy is used as the management strategy for the pipeline project; If the life cycle of the pipeline is in the second stage of the life cycle, the second management strategy is used as the management strategy for the pipeline project; 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; 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.

[0036] The first stage is the construction of new pipelines, which have not yet entered the operation stage. The first management strategy is quality supervision and acceptance. For example, when laying a new natural gas pipeline, the management strategy includes quality inspection of pipeline materials to ensure that they meet the design standards; during the construction process, professionals are arranged to monitor the welding quality, pipeline laying slope, etc. in real time to ensure the construction quality. At the same time, a detailed pipeline file is established to record various parameters of the pipeline and construction process information to provide basic data for subsequent management.

[0037] The second stage is the normal operation stage of the pipeline. The second management strategy is to conduct daily inspections or maintenance at the first frequency. Taking the urban drainage pipeline as an example, professional personnel are regularly arranged to inspect the pipeline to check whether there are problems such as blockage and leakage. According to the use of the pipeline and surrounding environmental factors, a reasonable cleaning plan is formulated, such as high-pressure flushing of the sewage pipeline every six months to prevent dirt accumulation from causing pipeline blockage. At the same time, sensor technology is used to monitor the flow, pressure and other parameters in the pipeline in real time, and timely warnings are issued once abnormalities are found so that corresponding maintenance measures can be taken.

[0038] The third stage is the aging stage of the pipeline, that is, the pipeline is approaching or has exceeded its design service life. The third management strategy is to evaluate the remaining life of the pipeline and determine the renewal and transformation plan.

[0039] From the above, it can be concluded that the present disclosure integrates pipeline information data, geographic information data and environmental information data to form a comprehensive target data set, which provides a basis for the management of pipeline projects and improves 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 the target analysis results of the pipeline, which helps to timely discover potential safety hazards, not only to reduce the risk of pipeline failures, but also to provide a scientific basis for the maintenance and care of the pipeline. Finally, based on the life cycle prediction results of the pipeline, a more reasonable and effective pipeline project management strategy can be formulated, which helps to optimize the resource allocation of the pipeline project and increase the service life of the pipeline. Therefore, the present disclosure can improve the management efficiency of pipeline projects.

[0040] In one embodiment of the present disclosure, determining a target data set of a pipeline includes: The pipeline information data and geographic information data are integrated to obtain the associated features of the pipeline; The pipeline information data, geographic information data, environmental information data and related features are integrated to obtain the target data set of the pipeline.

[0041] In one embodiment of the present disclosure, pipeline information data and geographic information data are fused to obtain associated features of the pipeline, including: Based on the association rule mining algorithm, pipeline information data and geographic information data are integrated to determine the associated characteristics of the pipeline.

[0042] In one embodiment of the present disclosure, pipeline information data and geographic information data are fused based on an association rule mining algorithm to determine the association features of the pipeline, including: Determine multiple pipeline data of pipeline information data; Determine multiple geographic data of geographic information data; 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; Determine a second support corresponding to any geographic data based on any geographic data, and generate a second frequent itemset based on the second support; Construct a frequent pattern tree based on the first frequent item set and the second frequent item set; Mining frequent itemsets based on frequent pattern trees to determine the associated features of pipelines.

[0043] In this embodiment, there are potential associations and features between pipeline information data and geographic information data, so the above two types of data are fused and analyzed to determine the association features of pipeline information data and geographic information data. The association features are feature information that can reflect the internal connection between pipeline information data and geographic information data.

[0044] For example, in a city gas pipeline project, the pipeline information data includes information such as the material of the gas pipeline is steel pipe, the pipe diameter is 300mm, and the service life is 10 years; the geographic information data shows that the pipeline is laid in a mountainous area and the geological structure is rock. By integrating these two types of data, this embodiment can find that in a mountainous rock geological environment, a 300mm steel pipe that has been used for 10 years is more likely to suffer from a certain corrosion condition, which is an associated feature.

[0045] After the associated features are determined, the pipeline information data, geographic information data, environmental information data and the associated features may be aggregated to form a target data set of the pipeline, which may be expressed in the form of multiple vectors.

[0046] The association rule mining algorithm is a data mining algorithm used to find associations or correlations between item sets in a data set. This embodiment analyzes the frequency of occurrence and co-occurrence patterns of different items in the data set to find out which items have strong associations.

[0047] For example, when analyzing gas pipeline data, the association rule mining algorithm can be used to find that in mountainous areas (geographic information) and areas with acidic soil (geographic information), gas pipelines made of cast iron (pipeline information) are more prone to corrosion (an associated feature). This is the association relationship mined from the two types of data through the association rule mining algorithm.

[0048] A plurality of specific data items are extracted from the pipeline information data, each of which represents a specific attribute or characteristic value of the pipeline. For example, each pipeline data item may be specific information such as the material, diameter, and service life of the pipeline.

[0049] A plurality of specific data items are extracted 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 may be a topographic type, soil pH, altitude, etc.

[0050] The first support is the frequency of an item set (i.e., a single pipeline data) in all pipeline data, which can reflect the prevalence of the item set. For example, the proportion of a certain pipeline material in all pipeline data is the support of the material. The first frequent item set is a set of pipeline data whose first support is greater than or equal to the preset first support threshold. The first support threshold can be dynamically set according to actual needs.

[0051] For each pipeline data, its frequency of occurrence in the entire pipeline information data (i.e., the first support) is calculated, and then the pipeline data with the first support greater than or equal to the first support threshold are grouped together to form the first frequent item set, in order to filter out the more important and common pipeline data features in the pipeline information data. The pipeline information data also includes a variety of pipeline data, and the corresponding types of pipeline data are different.

[0052] The calculation formula for the first support is:

[0053] 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 the pipeline data appears in the sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on the support degree; For the Itemsets in pipeline data With Itemsets in pipeline data The correlation between them can be calculated using the Pearson correlation coefficient; The total number of pipeline information datasets.

[0054] The second support is the support 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, wherein the second support threshold can be dynamically adjusted according to actual conditions.

[0055] 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.

[0056] The calculation formula for the second support is:

[0057] 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 the geographical data; For item sets In the The number of times a geographic data set appears in a sub-dataset; is the adjustment coefficient, which is used to control the influence of the correlation between data on the support degree; For the Itemsets in Geographic Data With Itemsets in Geographic Data The correlation between them can be calculated using the Pearson correlation coefficient; The total number of geographic information datasets.

[0058] A frequent pattern tree is a data structure used to mine frequent item sets (i.e., a first frequent item set and a second frequent item set). The structure of the frequent pattern tree consists of a root node, an item prefix subtree, and an item header table. The root node is marked as "null", the item prefix subtree contains all the frequent item set information, and the item header table stores each frequent item and its corresponding linked list information in the tree. This embodiment compresses and stores the frequent item set information in the pipeline information data and the geographic information data, thereby avoiding multiple scans of the data set and improving the efficiency of mining frequent item sets.

[0059] 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 associated features of the pipeline can be obtained.

[0060] It can be concluded from the above that this embodiment can deeply mine the associated features of the pipeline by fusing the pipeline information data and the geographic information data, and the above features provide an important reference for the management and maintenance of the pipeline. In addition, the use of association rule mining algorithm for data fusion can efficiently extract valuable information from a large amount of data, avoid the tediousness and subjectivity of manual screening, and improve the efficiency and accuracy of data processing.

[0061] 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: Divide the target data set into multiple categories of pipeline data sets based on clustering algorithm; Based on the analysis of each type of pipeline data set, the impact index of this type of pipeline data set is determined, and the impact index is the target analysis result.

[0062] In this embodiment, the target data set is divided into multiple types of pipeline data sets based on a clustering algorithm, including: Determine multiple initial cluster centers of the target data set; Each initial cluster center is updated based on the distance between the target data set sample and the initial cluster center to obtain multiple target cluster centers, each of which corresponds to a type of pipeline data set.

[0063] Assume that the target dataset has pipeline samples, each sample is a Dimensional vector , ,in, Indicates The sample Features First, random selection Points as initial cluster centers , , each cluster center is also a dimensional vector, i.e. ; Second, for each sample , calculate it to The distance between the initial cluster centers is: ; The sample Assigned to the cluster with the closest initial cluster center In Chinese, that is:

[0064] Third, recalculate each cluster The center of the new cluster It is a cluster The mean of all samples in is calculated as:

[0065] in, Representation Cluster The number of samples in ; 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 data set is divided into multiple categories of pipeline data sets.

[0066] The influencing indicators are parameters or variables that have a significant impact on the operation, performance, life, etc. of each type of pipeline data set. For example, for a certain type of pipeline, it can be found that "corrosion rate" and "pressure loss" are key indicators that affect its normal operation and life.

[0067] In this embodiment, each group of pipeline data sets with similar characteristics obtained by clustering is analyzed separately to find out the factors or indicators that have an impact on this type of pipeline, and the above factors or indicators can be the target analysis results, which can be used to guide management decisions for different pipelines.

[0068] It can be concluded from the above that this embodiment divides the complex target data set into multiple categories through the clustering algorithm, making the subsequent analysis more targeted and improving the analysis efficiency. Secondly, a detailed analysis of each type of pipeline data set can accurately determine the key influencing indicators of each type of pipeline, providing a scientific basis for pipeline management and maintenance. This embodiment not only enhances the accuracy of the analysis, but also helps to timely discover potential problems, optimize pipeline operation strategies, and ensure the safe and stable operation of pipeline projects.

[0069] In one embodiment of the present disclosure, analyzing each type of pipeline data set to determine the impact index of the type of pipeline data set includes: Each type of pipeline data set is input into the target analysis model to obtain the impact index of this type of pipeline data set.

[0070] In one embodiment of the present disclosure, the target analysis model is trained based on each type of historical pipeline data set and the influence index of the type of historical pipeline data set.

[0071] In this embodiment, the target analysis model may be a trained regression model or a statistical analysis model.

[0072] The historical pipeline dataset is a collection of data about pipelines accumulated in the past, including various information about pipelines of different periods and types, and each type of historical pipeline dataset is information data about pipelines of the same type at different periods. The analysis model is a regression model, which takes each type of historical pipeline dataset as the input dataset of the model, and the impact index of this type of historical pipeline dataset as the output dataset of the model. By adjusting and optimizing the parameters of the model, the regression model can learn the relationship between input and output, so as to have the ability to accurately predict and analyze new data. After training is completed and evaluated, the target analysis model is obtained.

[0073] By inputting each type of pipeline data set into the target analysis model, the corresponding impact indicators can be predicted.

[0074] It can be concluded from the above that this embodiment ensures that the analysis model can accurately capture 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 and helps to improve the overall operating efficiency of pipeline projects.

[0075] Corresponding to a pipeline engineering digital management method in the above embodiment, Figure 2 This is a structural block diagram of a pipeline engineering digital management system 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.

[0076] The data determination module 21 is used to determine the target data set of the pipeline, and the target data set includes pipeline information data, geographic information data and environmental information data; 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; A life cycle prediction module 23, used to predict the life cycle of the pipeline based on the target analysis result of the pipeline; 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.

[0077] In one embodiment of the present disclosure, the data determination module 21 is specifically configured to: The pipeline information data and geographic information data are integrated to obtain the associated features of the pipeline; The pipeline information data, geographic information data, environmental information data and related features are integrated to obtain the target data set of the pipeline.

[0078] In one embodiment of the present disclosure, the data determination module 21 is further configured 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.

[0079] In one embodiment of the present disclosure, the data determination module 21 is further configured to: Determine multiple pipeline data of pipeline information data; Determine multiple geographic data of geographic information data; 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; Determine a second support corresponding to any geographic data based on any geographic data, and generate a second frequent itemset based on the second support; Construct a frequent pattern tree based on the first frequent item set and the second frequent item set; Mining frequent itemsets based on frequent pattern trees can determine the associated features of pipelines.

[0080] In one embodiment of the present disclosure, the analysis result output module 22 is specifically used to: Divide the target data set into multiple categories of pipeline data sets based on clustering algorithm; Based on the analysis of each type of pipeline data set, the impact index of this type of pipeline data set is determined, and the impact index is the target analysis result.

[0081] In one embodiment of the present disclosure, the analysis result output module 22 is further configured to: Each type of pipeline data set is input into the target analysis model to obtain the impact index of this type of pipeline data set.

[0082] In one embodiment of the present disclosure, the target analysis model is trained based on each type of historical pipeline data set and the influence index of the type of historical pipeline data set.

[0083] See also Figure 3 , Figure 3 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 each module / unit in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 24 are shown.

[0084] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also 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 the processor may also be any conventional processor, etc.

[0085] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0086] The memory 304 may include a read-only memory and a random access memory, and provide 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.

[0087] 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 in 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.

[0088] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0089] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also 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 may also be used to temporarily store data that has been output or is to be output.

[0090] Those of ordinary skill 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 composition and steps of each example have been generally described in terms of function in the above description. 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.

[0091] Those skilled in the art can 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.

[0092] In the several embodiments provided in the present 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 only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, 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 it can be an electrical, mechanical or other form of connection.

[0093] 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 on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0094] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A digital management method for pipeline engineering, characterized in that: include: Determine a target data set for the 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; A management strategy for the pipeline project is determined based on the life cycle of the pipeline.

2. A pipeline engineering digital management method as claimed in claim 1, characterized in that: The target data set of the pipeline is determined, including: Merging the pipeline information data and the geographic information data to obtain associated features of the pipeline; The pipeline information data, the geographic information data, the environmental information data and the associated features are integrated to obtain a target data set for the pipeline.

3. A pipeline engineering digital management method as claimed in claim 2, characterized in that: The step of fusing the pipeline information data with the geographic information data to obtain the associated features of the pipeline includes: The pipeline information data and the geographic information data are fused based on an association rule mining algorithm to determine the association features of the pipeline.

4. A pipeline engineering digital management method as claimed in claim 3, characterized in that: The method of fusing 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; Determine a plurality of geographic data of the geographic information data; 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; Determine a second support corresponding to any geographic data based on any geographic data, and generate a second frequent itemset based on the second support; 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.

5. A pipeline engineering digital management method as claimed in claim 1, characterized in that: The step of 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.

6. A pipeline engineering digital management method as claimed in claim 5, characterized in that: The analysis based on each type of pipeline data set to determine the impact index of the type of pipeline data set includes: Each type of pipeline data set is input into the target analysis model to obtain the impact index of this type of pipeline data set.

7. A pipeline engineering digital management method as claimed in claim 1, characterized in that: The target analysis model is trained based on each type of historical pipeline data set and the impact indicators of this type of historical pipeline data set.

8. A digital management system for pipeline engineering, characterized in that: include: A data determination module, 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, used for inputting 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, used to predict the life cycle of the pipeline based on the target analysis result of the pipeline; A management strategy determination module is used to determine a management strategy for a pipeline project based on the life cycle of the pipeline.

9. 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 7 are implemented.

10. 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 7 are implemented.

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