Intelligent detection method and device for pipeline fault and computer equipment

Abstract

The invention relates to an intelligent detection method and device for a pipeline fault and computer equipment. The method comprises the steps of obtaining current sensing data, current environment change data, pipeline work detection data and current structure scanning data of a pipeline, and identifying environment interference data of an environment where the pipeline is located; adjusting to obtain each target sensing data of the pipeline, and identifying structure work abnormal information of each pipeline part of the pipeline based on the pipeline work detection number of the pipeline and the current structure scanning data; pipeline operation abnormal information of each pipeline part of the pipeline is identified based on each target sensing data of the pipeline, and the pipeline operation abnormal information of each pipeline part is analyzed through a pipeline abnormal analysis strategy based on the structure work abnormal information of each pipeline part and the pipeline operation abnormal information of each pipeline part; and identifying the pipeline fault type of each abnormal pipeline part and the pipeline abnormal probability of each abnormal pipeline part. By adopting the method, the comprehensiveness and accuracy of pipeline fault detection, analysis and identification can be improved.

Description

Technical Field

[0001] This application relates to the technical fields of intelligent fault detection and multi-dimensional data analysis, and particularly to an intelligent detection method, device, and computer equipment for pipeline faults. Background Art

[0002] In modern industrial production, the pipeline system is an essential component, which is used to transport various liquid media, such as water, oil, chemical raw materials, etc. With the progress of technology, intelligence has become an important development trend of the pipeline system. An intelligent liquid dispenser is a new type of pipeline system, which can monitor the operating status of the pipeline in real time through sensors and control algorithms, such as liquid output volume, liquid output rate, internal pressure data, and the operating status of the transfer pump. These data have important reference value for judging the status of the pipeline. Therefore, how to accurately detect pipeline faults is the current research focus.

[0003] The traditional technical solution is to collect sensing data through multiple sensors, and then analyze the sensor data to detect the faults of the pipeline. However, the data that can be collected by the sensor data is limited, resulting in greater limitations in the detected pipeline faults. Moreover, in actual collection, the data obtained solely through sensor data collection is affected by interference factors such as current and environment, and is prone to false alarms and missed alarms, resulting in low accuracy of pipeline fault detection. Summary of the Invention

[0004] Based on this, it is necessary to provide an intelligent detection method, device, computer equipment, computer-readable storage medium, and computer program product for pipeline faults in view of the above technical problems.

[0005] In a first aspect, this application provides an intelligent detection method for pipeline faults, including:

[0006] Obtain the current sensing data of the pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline working detection data of the pipeline, and the current structure scan data of the pipeline, and identify the environmental interference data of the environment where the pipeline is located based on the environmental change data;

[0007] Adjust each of the current sensing data based on the environmental interference data to obtain the target sensing data of the pipeline, and identify the abnormal structural working information of each pipeline part of the pipeline based on the pipeline working detection data of the pipeline and the current structure scan data of the pipeline;

[0008] Based on the target sensing data of the pipeline, identify the pipeline operation anomaly information of each pipeline section of the pipeline, and based on the structural working anomaly information of each pipeline section and the pipeline operation anomaly information of each pipeline section, through the pipeline anomaly analysis strategy, identify the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section.

[0009] Optionally, the identifying the environmental interference data of the environment where the pipeline is located based on the environmental change data includes:

[0010] Split the environmental change data into environmental data distribution information of each environmental type, and based on the environmental data distribution information of each environmental type, query the environmental interference factor values corresponding to each environmental interference factor through the environmental interference database;

[0011] Use the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the environment where the pipeline is located.

[0012] Optionally, the adjusting each current sensing data based on the environmental interference data to obtain the target sensing data of the pipeline includes:

[0013] Identify the sensing data type corresponding to each current sensing data, and query the sensing data interference database to identify each target environmental interference factor corresponding to each sensing data type and the data conversion program between each sensing data type and each target environmental interference factor corresponding to each sensing data type;

[0014] Through each data conversion program, convert each target environmental interference factor corresponding to each sensing data type into each sensing data adjustment value of each sensing data type;

[0015] Based on the current sensing data of each sensing data type and each sensing data adjustment value of each sensing data type, calculate the target sensing data corresponding to each sensing data type.

[0016] Optionally, the identifying the structural working anomaly information of each pipeline section of the pipeline based on the pipeline working detection data of the pipeline and the current structural scan data of the pipeline includes:

[0017] Split the pipeline working detection data of the pipeline into the current working data of each working data type of each pipeline section, and based on the current structural scan data of the pipeline, construct the pipeline structure model of each pipeline section of the pipeline;

[0018] For each pipeline section, based on the current working data of each working data type of the pipeline section, through a working data anomaly judgment program, identify the abnormal working data of the abnormal working data types of the pipeline section, and based on the pipeline structure model of the pipeline section and the normal pipeline structure models of each pipeline section, identify the structural difference information of the pipeline section;

[0019] Take the abnormal working data of the abnormal working data types of the pipeline section and the structural difference information of the pipeline section as the structural working anomaly information of the pipeline section.

[0020] Optionally, the identifying the pipeline operation anomaly information of each pipeline section of the pipeline based on the respective target sensing data of the pipeline includes:

[0021] Split each target sensing data into sub-target sensing data of each pipeline section;

[0022] For each pipeline section, based on the sub-target sensing data of the pipeline section and the sensing data types corresponding to the respective sub-target sensing data, through a pipeline anomaly analysis network, identify the anomaly degree values of each pipeline anomaly type of the pipeline section;

[0023] Take the anomaly degree values of each pipeline anomaly type of the pipeline section as the pipeline operation anomaly information of the pipeline section.

[0024] Optionally, the identifying the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section through a pipeline anomaly analysis strategy based on the structural working anomaly information of each pipeline section and the pipeline operation anomaly information of each pipeline section includes:

[0025] Based on the abnormal working data of the abnormal working data types of each pipeline section and the structural difference information of each pipeline section, construct a pipeline structure state distribution map of the pipeline, and based on the anomaly degree values of each pipeline anomaly type of each pipeline section, perform anomaly marking processing in the pipeline structure state distribution map to obtain the pipeline state distribution map of the pipeline;

[0026] Based on the pipeline state distribution map of the pipeline, through a pipeline fault analysis network, identify the fault types of each pipeline fault area of the pipeline and the fault probability values of each pipeline fault area of the pipeline;

[0027] Take the pipeline section to which each pipeline fault area belongs as an abnormal pipeline section, and take the fault types of each pipeline fault area and the fault probability values of each pipeline fault area as the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section.

[0028] In a second aspect, the present application also provides an intelligent detection device for pipeline faults, including:

[0029] An acquisition module, configured to acquire the current sensing data of each pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline working detection data of the pipeline, and the current structure scan data of the pipeline, and based on the environmental change data, identify the environmental interference data of the environment where the pipeline is located;

[0030] An adjustment module, configured to adjust each of the current sensing data based on the environmental interference data to obtain the target sensing data of each pipeline, and based on the pipeline working detection data of the pipeline and the current structure scan data of the pipeline, identify the structural working abnormal information of each pipeline part of the pipeline;

[0031] An identification module, configured to identify the pipeline operation abnormal information of each pipeline part of the pipeline based on the target sensing data of each pipeline, and based on the structural working abnormal information of each pipeline part and the pipeline operation abnormal information of each pipeline part, through a pipeline anomaly analysis strategy, identify the pipeline fault type of each abnormal pipeline part and the pipeline anomaly probability of each abnormal pipeline part.

[0032] Optionally, the acquisition module is specifically configured to:

[0033] Split the environmental change data into the environmental data distribution information of each environmental type, and based on the environmental data distribution information of each environmental type, query the environmental interference factor values corresponding to each environmental interference factor through an environmental interference database;

[0034] Use the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the environment where the pipeline is located.

[0035] Optionally, the adjustment module is specifically configured to:

[0036] Identify the sensing data type corresponding to each current sensing data, and query a sensing data interference database to identify the target environmental interference factors corresponding to each sensing data type and the data conversion program between each sensing data type and the target environmental interference factors corresponding to each sensing data type;

[0037] Convert the target environmental interference factors corresponding to each sensing data type into the sensing data adjustment values of each sensing data type through each of the data conversion programs;

[0038] Calculate the target sensing data corresponding to each sensing data type based on the current sensing data of each sensing data type and the sensing data adjustment values of each sensing data type.

[0039] Optionally, the adjustment module is specifically configured to:

[0040] Split the pipeline working detection data of the pipeline into the current working data of each working data type of each pipeline section, and construct a pipeline structure model of each pipeline section of the pipeline based on the current structure scanning data of the pipeline;

[0041] For each pipeline section, based on the current working data of each working data type of the pipeline section, identify the abnormal working data of the abnormal working data type of the pipeline section through a working data anomaly judgment program, and based on the pipeline structure model of the pipeline section and the normal pipeline structure models of each pipeline section, identify the structural difference information of the pipeline section;

[0042] Take the abnormal working data of the abnormal working data type of the pipeline section and the structural difference information of the pipeline section as the structural working anomaly information of the pipeline section.

[0043] Optionally, the identification module is specifically configured to:

[0044] Split each target sensing data into sub-target sensing data of each pipeline section;

[0045] For each pipeline section, based on the sub-target sensing data of the pipeline section and the sensing data types corresponding to the sub-target sensing data, identify the abnormal degree values of each pipeline abnormal type of the pipeline section through a pipeline anomaly analysis network;

[0046] Take the abnormal degree values of each pipeline abnormal type of the pipeline section as the pipeline operation abnormal information of the pipeline section.

[0047] Optionally, the identification module is specifically configured to:

[0048] Construct a pipeline structure state distribution map of the pipeline based on the abnormal working data of the abnormal working data type of each pipeline section and the structural difference information of each pipeline section, and perform abnormal marking processing in the pipeline structure state distribution map based on the abnormal degree values of each pipeline abnormal type of each pipeline section to obtain the pipeline state distribution map of the pipeline;

[0049] Based on the pipeline state distribution map of the pipeline, identify the fault types of each pipeline fault area of the pipeline and the fault probability values of each pipeline fault area of the pipeline through a pipeline fault analysis network;

[0050] Take the pipeline section to which each pipeline failure area belongs as the abnormal pipeline section, and take the failure type of each of the pipeline failure areas and the failure probability value of each of the pipeline failure areas as the pipeline failure type of each of the abnormal pipeline sections and the pipeline abnormality probability of each of the abnormal pipeline sections.

[0051] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of the first aspects are implemented.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium. A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of the first aspects are implemented.

[0053] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of the first aspects are implemented.

[0054] The intelligent detection method, device, and computer equipment for the above pipeline faults obtain the current sensing data of the pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline operation detection data of the pipeline, and the current structure scan data of the pipeline, and identify the environmental interference data of the environment where the pipeline is located based on the environmental change data; adjust each of the current sensing data based on the environmental interference data to obtain the target sensing data of the pipeline, and identify the structural operation abnormality information of each pipeline part of the pipeline based on the pipeline operation detection data of the pipeline and the current structure scan data of the pipeline; identify the pipeline operation abnormality information of each pipeline part of the pipeline based on the target sensing data of the pipeline, and identify the pipeline fault type of each abnormal pipeline part and the pipeline abnormality probability of each abnormal pipeline part through a pipeline abnormality analysis strategy based on the structural operation abnormality information of each pipeline part and the pipeline operation abnormality information of each pipeline part. In this solution, by combining the current environmental change data of the environment where the pipeline is located, the current sensing data of the pipeline is adjusted, so that the obtained target sensing data is the sensing data under low interference, thereby reducing the error rate and deviation degree of the sensing data. Then, this solution combines the pipeline operation detection data of the pipeline and the current structure scan data of the pipeline, and comprehensively analyzes the structural operation abnormality information of each pipeline part and the pipeline operation abnormality information of each pipeline part by considering the abnormal information existing in the pipeline from multiple factors and dimensions. Finally, the granularity is refined to each pipeline part of the pipeline, and then the pipeline fault type of each abnormal pipeline part and the pipeline abnormality probability of each abnormal pipeline part are considered, which improves the comprehensiveness and accuracy of pipeline fault detection, analysis, and identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is a schematic flowchart of the intelligent detection method for pipeline faults in an embodiment;

[0057] Figure 2 It is a schematic flowchart of an intelligent detection example for pipeline faults in an embodiment;

[0058] Figure 3 It is a structural block diagram of the intelligent detection device for pipeline faults in an embodiment;

[0059] Figure 4Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0060] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

[0061] The intelligent detection method for pipeline faults provided by the embodiments of the present application can be applied to an intelligent control system for intelligent detection of pipeline faults. This system can be applied to a terminal, which can be but is not limited to various personal computers, laptop computers, medium-sized computers, etc. Among them, the terminal combines the current environmental change data of the pipeline environment, thereby adjusting the current sensing data of the pipeline, so that the obtained target sensing data is the sensing data under low interference, thereby reducing the error rate and deviation degree of the sensing data. Then, this solution combines the pipeline working detection data of the pipeline and the current structure scan data of the pipeline, thereby considering the abnormal information existing in the pipeline from multiple factors and multiple dimensions, and comprehensively analyzing the structural working abnormal information of each pipeline part and the pipeline operation abnormal information of each pipeline part. Finally, the granularity is refined to each pipeline part of the pipeline to consider the pipeline fault types of each abnormal pipeline part and the pipeline abnormal probability of each abnormal pipeline part, improving the comprehensiveness and accuracy of pipeline fault detection, analysis and identification.

[0062] In an exemplary embodiment, as Figure 1 shown, an intelligent detection method for pipeline faults is provided. Taking the application of this method to a terminal as an example, it includes the following steps S101 to S103. Among them:

[0063] Step S101, obtain each current sensing data of the pipeline, the current environmental change data of the pipeline environment, the pipeline working detection data of the pipeline, and the current structure scan data of the pipeline, and identify the environmental interference data of the pipeline environment based on the environmental change data.

[0064] In this embodiment, the terminal acquires sensors disposed at different positions of the pipeline, collects the sensor data, and obtains the current sensing data of the pipeline. Then, the terminal acquires the environmental data at each time point of different environmental types to obtain the current environmental change data of the environment where the pipeline is located. Among them, the sensors include sensors of different sensing data types. For example, equipment such as a pipeline liquid output sensor, a pipeline liquid output rate sensor, a pipeline internal pressure sensor, and a transfer pump operating state sensor (including a voltage sensor, a current sensor, and a rotation speed sensor) are used to collect the liquid output volume, liquid output rate, internal pressure data, and the operating state data of the transfer pump of the pipeline in real time. Among them, each environmental type includes but is not limited to a temperature type, a humidity type, etc. Then, the terminal acquires the pipeline working detection data of the pipeline, where the pipeline working detection data includes the current working data of each working data type. For example, data such as the operating state of the transfer pump (voltage, current, rotation speed), and the water quality information of each pipeline part. And the pipeline parts include but are not limited to a transfer pump, a liquid delivery pipe, a liquid distribution pipe, a collecting pipe, etc. Among them, the current structure scan data of the pipeline is the structure obtained by a scanning device disposed around the pipeline to perform a real-time structure scan on each pipeline part. Then, the terminal identifies the environmental interference data of the environment where the pipeline is located based on the environmental change data. The environmental interference data is the environmental interference factor value corresponding to each environmental interference factor that affects the sensing data of the sensor. Among them, the environmental interference factors include but are not limited to a liquid output temperature interference factor, a liquid output speed interference factor, a pipeline pressure interference factor, etc. The specific identification process will be described in detail later.

[0065] Step S102: Based on the environmental interference data, adjust each current sensing data to obtain the target sensing data of the pipeline, and identify the structural working abnormal information of each pipeline part of the pipeline based on the pipeline working detection data of the pipeline and the current structure scan data of the pipeline.

[0066] In this embodiment, the terminal adjusts each current sensing data based on the environmental interference data to obtain the target sensing data of the pipeline, and identifies the structural working abnormal information of each pipeline part of the pipeline based on the pipeline working detection data of the pipeline and the current structure scan data of the pipeline. The target sensing data obtained after adjustment is the actual sensing data obtained by eliminating environmental interference and restoring the sensing data, reducing the sensing data error rate and the sensing data deviation information. And the structural working abnormal information of each pipeline part includes the abnormal working data of each abnormal working data type of the pipeline part and the structural difference information of the pipeline part. The specific identification process will be described in detail later.

[0067] Step S103: Based on the target sensing data of the pipeline, identify the pipeline operation abnormal information of each pipeline section of the pipeline, and based on the structural working abnormal information of each pipeline section and the pipeline operation abnormal information of each pipeline section, through the pipeline anomaly analysis strategy, identify the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section.

[0068] In this embodiment, the terminal identifies the pipeline operation abnormal information of each pipeline section of the pipeline based on the target sensing data of the pipeline, and based on the structural working abnormal information of each pipeline section and the pipeline operation abnormal information of each pipeline section, through the pipeline anomaly analysis strategy, identifies the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section. Among them, the pipeline operation abnormal information includes the abnormal degree values of each pipeline abnormal type of each pipeline section. The specific identification process of the pipeline operation abnormal information will be described in detail later.

[0069] Based on the above solution, by combining the current environmental change data of the pipeline environment, the current sensing data of the pipeline is adjusted, so that the obtained target sensing data is the sensing data under low interference, thereby reducing the error rate and deviation degree of the sensing data. Then, this solution combines the pipeline work detection data of the pipeline and the current structure scan data of the pipeline, so as to consider the abnormal information existing in the pipeline from multiple factors and dimensions, and comprehensively analyze the structural working abnormal information of each pipeline section and the pipeline operation abnormal information of each pipeline section. Finally, the granularity is refined to each pipeline section of the pipeline, and then the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section are considered, improving the comprehensiveness and accuracy of pipeline fault detection, analysis, and identification.

[0070] Optionally, based on the environmental change data, identify the environmental interference data of the pipeline environment, including: splitting the environmental change data into the environmental data distribution information of each environmental type, and based on the environmental data distribution information of each environmental type, query the environmental interference factor values corresponding to each environmental interference factor through the environmental interference database; use the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the pipeline environment.

[0071] In this embodiment, the terminal splits the environmental change data into the environmental data distribution information of each environmental type, and based on the environmental data distribution information of each environmental type, queries the environmental interference factor values corresponding to each environmental interference factor through the environmental interference database. Among them, the environmental interference database includes the corresponding relationship between each environmental type and the environmental interference factors, and also includes the conversion relationship formula between each environmental data distribution range and the pipe diameter interference factor value. The terminal identifies the environmental interference factor values corresponding to each environmental interference factor through the above corresponding relationship and the conversion relationship formula.

[0072] Finally, the terminal takes the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the environment where the pipeline is located.

[0073] Based on the above solution, by combining the environmental type and the environmental interference factors for correlation identification, the environmental interference factor values corresponding to each environmental interference factor are analyzed, thereby improving the identification accuracy and comprehensiveness of the environmental interference factor values of each environmental interference factor.

[0074] Optionally, based on the environmental interference data, each current sensing data is adjusted to obtain each target sensing data of the pipeline, including: identifying the sensing data type corresponding to each current sensing data, and querying the sensing data interference database to identify each target environmental interference factor corresponding to each sensing data type, and the data conversion program between each sensing data type and each target environmental interference factor corresponding to each sensing data type; through each data conversion program, each target environmental interference factor corresponding to each sensing data type is converted into each sensing data adjustment value of each sensing data type; based on the current sensing data of each sensing data type and each sensing data adjustment value of each sensing data type, the target sensing data corresponding to each sensing data type is calculated.

[0075] In this embodiment, the terminal identifies the sensing data type corresponding to each current sensing data, and queries the sensing data interference database to identify each target environmental interference factor corresponding to each sensing data type, and the data conversion program between each sensing data type and each target environmental interference factor corresponding to each sensing data type. Among them, each data conversion program is an algorithm program for converting each target environmental interference factor value into the sensing data of the sensing data type. This algorithm program is a conversion program obtained by the staff through a large amount of environmental interference data analysis and sensing data analysis.

[0076] The terminal converts each target environmental interference factor corresponding to each sensing data type into each sensing data adjustment value of each sensing data type through each data conversion program.

[0077] Then, the terminal calculates the target sensing data corresponding to each sensing data type based on the current sensing data of each sensing data type and the adjustment values of the sensing data of each sensing data type. The calculation method is to sum the current sensing data of each sensing data type and the adjustment values of the sensing data of each sensing data type to obtain the target sensing data corresponding to each sensing data type. In another embodiment, the terminal can obtain the target sensing data corresponding to each sensing data type by means of correction methods such as linear correction and non-linear correction, based on the adjustment values of the sensing data of each sensing data type for the current sensing data of each sensing data type.

[0078] Based on the above solution, by combining the target environmental interference factors corresponding to each sensing data type, data correction is performed on the sensing data corresponding to each sensing data type, so that the obtained target sensing data is the sensing data under low interference, thereby reducing the error rate and deviation degree of the sensing data.

[0079] Optionally, based on the pipeline work detection data of the pipeline and the current structure scan data of the pipeline, identify the structural work anomaly information of each pipeline part of the pipeline, including: splitting the pipeline work detection data of the pipeline into the current work data of each work data type of each pipeline part, and based on the current structure scan data of the pipeline, constructing the pipeline structure model of each pipeline part of the pipeline; for each pipeline part, based on the current work data of each work data type of the pipeline part, through the work data anomaly judgment program, identify the abnormal work data of the abnormal work data type of the pipeline part, and based on the pipeline structure model of the pipeline part and the normal pipeline structure models of each pipeline part, identify the structural difference information of the pipeline part; take the abnormal work data of the abnormal work data type of the pipeline part and the structural difference information of the pipeline part as the structural work anomaly information of the pipeline part.

[0080] In this embodiment, the terminal splits the pipeline work detection data of the pipeline into the current work data of each work data type of each pipeline part, and based on the current structure scan data of the pipeline, constructs the pipeline structure model of each pipeline part of the pipeline. Among them, the pipeline structure model is a pipeline three-dimensional structure model. The terminal performs structural three-dimensional modeling through the three-dimensional structure data corresponding to the current structure scan data by means of a three-dimensional modeling program to obtain the pipeline structure model of each pipeline part.

[0081] For each pipeline section, based on the current working data of each working data type of the pipeline section, the terminal uses a working data anomaly judgment program to identify the abnormal working data of the abnormal working data type of the pipeline section, and based on the pipeline structure model of the pipeline section and the normal pipeline structure models of each pipeline section, identifies the structural difference information of the pipeline section. Among them, the working data anomaly judgment program finally includes the data distribution thresholds of each working data type. The terminal identifies the magnitude relationship between the current working data of each working data type and the data distribution thresholds of each working data type, so as to regard the current working data greater than the data distribution threshold as the abnormal working data, and regard the working data type corresponding to the abnormal working data as the abnormal working data type. Among them, the structural difference information is the structural deviation range from the standard structure model. Among them, this structural deviation range can identify structural changes, structural bends, structural corrosion, etc. of the pipeline.

[0082] The terminal regards the abnormal working data of the abnormal working data type of the pipeline section and the structural difference information of the pipeline section as the structural working anomaly information of the pipeline section.

[0083] Based on the above solution, by comprehensively identifying the abnormal working data of the abnormal working data type and the structural difference information, the comprehensiveness and accuracy of pipeline anomaly identification are improved.

[0084] Optionally, based on the respective target sensing data of the pipeline, identify the pipeline operation anomaly information of each pipeline section of the pipeline, including: splitting each target sensing data into sub-target sensing data of each pipeline section; for each pipeline section, based on the respective sub-target sensing data of the pipeline section and the sensing data types corresponding to the respective sub-target sensing data, through a pipeline anomaly analysis network, identify the anomaly degree values of each pipeline anomaly type of the pipeline section; regard the anomaly degree values of each pipeline anomaly type of the pipeline section as the pipeline operation anomaly information of the pipeline section.

[0085] In this embodiment, the terminal splits each target sensing data into sub-target sensing data of each pipeline section.

[0086] For each pipeline section, the terminal, based on the sub-goal sensing data of the pipeline section and the sensing data types corresponding to the sub-goal sensing data, uses a pipeline anomaly analysis network to identify the anomaly degree values of each pipeline anomaly type in the pipeline section. Among them, the pipeline anomaly analysis network is a reinforcement learning machine neural network based on deep learning and a reward function. This neural network is used to identify the pipeline anomaly types and anomaly degree values in the pipeline section based on the target sensing data of each sensing data type. Among them, the pipeline anomaly types include, but are not limited to, pipeline loosening type, pipeline blockage type, pipeline rupture type, pipeline deformation type, etc. And the anomaly degree values of different pipeline anomaly types, for example, the loosening degree value of the pipeline loosening type, the blockage percentage of the pipeline blockage type, the rupture range of the pipeline rupture type, the deformation range of the pipeline deformation type, etc.

[0087] The terminal takes the anomaly degree values of each pipeline anomaly type in the pipeline section as the pipeline operation anomaly information of the pipeline section.

[0088] Based on the above solution, by identifying the anomaly degree values of each pipeline anomaly type, the comprehensiveness and accuracy of pipeline anomaly analysis are improved.

[0089] Optionally, based on the structural working anomaly information of each pipeline section and the pipeline operation anomaly information of each pipeline section, through a pipeline anomaly analysis strategy, identify the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section, including: based on the abnormal working data of each abnormal working data type of each pipeline section and the structural difference information of each pipeline section, construct a pipeline structure state distribution map of the pipeline, and based on the anomaly degree values of each pipeline anomaly type of each pipeline section, perform anomaly marking processing in the pipeline structure state distribution map to obtain a pipeline state distribution map of the pipeline; based on the pipeline state distribution map of the pipeline, through a pipeline fault analysis network, identify the fault types of each pipeline fault area in the pipeline and the fault probability values of each pipeline fault area in the pipeline; take the pipeline section to which each pipeline fault area belongs as an abnormal pipeline section, and take the fault types of each pipeline fault area and the fault probability values of each pipeline fault area as the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section.

[0090] In this embodiment, the terminal constructs a pipeline structure state distribution map based on the abnormal working data of each pipeline part according to the abnormal working data type and the structural difference information of each pipeline part, and performs abnormal marking processing in the pipeline structure state distribution map based on the abnormal degree values of each pipeline abnormal type of each pipeline part to obtain the pipeline state distribution map of the pipeline. Among them, the pipeline state distribution map of the pipeline includes a three-dimensional pipeline structure diagram of each pipeline part of the pipeline, the abnormal working data, and the identification of the abnormal degree values of each pipeline abnormal type.

[0091] Then, based on the pipeline state distribution map of the pipeline, the terminal identifies the fault types of each pipeline fault area of the pipeline and the fault probability values of each pipeline fault area of the pipeline through a pipeline fault analysis network. Among them, the fault analysis network is an image recognition network, and the image recognition network is a convolutional neural network based on deep learning. The terminal takes the pipeline part to which each pipeline fault area belongs as an abnormal pipeline part, and takes the fault types of each pipeline fault area and the fault probability values of each pipeline fault area as the pipeline fault types of each abnormal pipeline part and the pipeline abnormal probability of each abnormal pipeline part.

[0092] Based on the above solution, by constructing the pipeline structure state distribution map and then performing fault analysis, the accuracy and comprehensiveness of the analysis are improved.

[0093] The application also provides an intelligent detection example of pipeline faults, as Figure 2 shown. The specific processing process includes the following steps:

[0094] Step S201, obtain the current sensing data of the pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline working detection data of the pipeline, and the current structure scan data of the pipeline.

[0095] Step S202, split the environmental change data into the environmental data distribution information of each environmental type, and query the environmental interference factor values corresponding to each environmental interference factor through the environmental interference database based on the environmental data distribution information of each environmental type.

[0096] Step S203, take the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the environment where the pipeline is located.

[0097] Step S204, identify the sensing data type corresponding to each current sensing data, and query the sensing data interference database to identify each target environmental interference factor corresponding to each sensing data type and the data conversion program between each sensing data type and each target environmental interference factor corresponding to each sensing data type.

[0098] Step S205: Through each data conversion program, convert each target environmental interference factor corresponding to each sensing data type into each sensing data adjustment value of each sensing data type.

[0099] Step S206: Based on the current sensing data of each sensing data type and each sensing data adjustment value of each sensing data type, calculate the target sensing data corresponding to each sensing data type.

[0100] Step S207: Split the pipeline working detection data of the pipeline into the current working data of each working data type of each pipeline section, and based on the current structure scanning data of the pipeline, construct the pipeline structure model of each pipeline section of the pipeline.

[0101] Step S208: For each pipeline section, based on the current working data of each working data type of the pipeline section, through the working data anomaly judgment program, identify the abnormal working data of the abnormal working data type of the pipeline section, and based on the pipeline structure model of the pipeline section and the normal pipeline structure models of each pipeline section, identify the structural difference information of the pipeline section.

[0102] Step S209: Take the abnormal working data of the abnormal working data type of the pipeline section and the structural difference information of the pipeline section as the structural working anomaly information of the pipeline section.

[0103] Step S210: Split each target sensing data into sub-target sensing data of each pipeline section.

[0104] Step S211: For each pipeline section, based on the sub-target sensing data of the pipeline section and the sensing data type corresponding to each sub-target sensing data, through the pipeline anomaly analysis network, identify the abnormal degree values of each pipeline anomaly type of the pipeline section.

[0105] Step S212: Take the abnormal degree values of each pipeline anomaly type of the pipeline section as the pipeline operation anomaly information of the pipeline section.

[0106] Step S213: Based on the abnormal working data of the abnormal working data type of each pipeline section and the structural difference information of each pipeline section, construct the pipeline structure state distribution map of the pipeline, and based on the abnormal degree values of each pipeline anomaly type of each pipeline section, perform abnormal marking processing in the pipeline structure state distribution map to obtain the pipeline state distribution map of the pipeline.

[0107] Step S214: Based on the pipeline state distribution map of the pipeline, through the pipeline fault analysis network, identify the fault types of each pipeline fault area of the pipeline and the fault probability values of each pipeline fault area of the pipeline.

[0108] Step S215: Take the pipeline part to which each pipeline failure area belongs as the abnormal pipeline part, and take the failure type of each pipeline failure area and the failure probability value of each pipeline failure area as the pipeline failure type and the pipeline abnormality probability of each abnormal pipeline part.

[0109] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0110] Based on the same inventive concept, an embodiment of the present application also provides an intelligent detection device for pipeline failures for implementing the intelligent detection method for pipeline failures involved above. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the intelligent detection device for pipeline failures provided below can refer to the limitations on the intelligent detection method for pipeline failures in the above text, and will not be repeated here.

[0111] In an exemplary embodiment, as Figure 3 shown, an intelligent detection device for pipeline failures is provided, including: an acquisition module 310, an adjustment module 320, and an identification module 330, where:

[0112] The acquisition module 310 is configured to acquire each current sensing data of the pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline operation detection data of the pipeline, and the current structure scan data of the pipeline, and identify the environmental interference data of the environment where the pipeline is located based on the environmental change data;

[0113] The adjustment module 320 is configured to adjust each of the current sensing data based on the environmental interference data to obtain each target sensing data of the pipeline, and identify the structural operation abnormality information of each pipeline part of the pipeline based on the pipeline operation detection data of the pipeline and the current structure scan data of the pipeline;

[0114] An identification module 330, configured to identify pipeline operation anomaly information of each pipeline part of the pipeline based on the target sensing data of the pipeline, and identify the pipeline fault type of each abnormal pipeline part and the pipeline anomaly probability of each abnormal pipeline part through a pipeline anomaly analysis strategy based on the structural working anomaly information of each pipeline part and the pipeline operation anomaly information of each pipeline part.

[0115] Optionally, the obtaining module 310 is specifically configured to:

[0116] Split the environmental change data into environmental data distribution information of each environmental type, and query the environmental interference factor values corresponding to each environmental interference factor through an environmental interference database based on the environmental data distribution information of each environmental type;

[0117] Use the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the environment where the pipeline is located.

[0118] Optionally, the adjustment module 320 is specifically configured to:

[0119] Identify the sensing data type corresponding to each current sensing data, and query the sensing data interference database to identify each target environmental interference factor corresponding to each sensing data type and the data conversion program between each sensing data type and each target environmental interference factor corresponding to each sensing data type;

[0120] Convert each target environmental interference factor corresponding to each sensing data type into each sensing data adjustment value of each sensing data type through each data conversion program;

[0121] Calculate the target sensing data corresponding to each sensing data type based on the current sensing data of each sensing data type and each sensing data adjustment value of each sensing data type.

[0122] Optionally, the adjustment module 320 is specifically configured to:

[0123] Split the pipeline working detection data of the pipeline into the current working data of each working data type of each pipeline part, and construct a pipeline structure model of each pipeline part of the pipeline based on the current structure scan data of the pipeline;

[0124] For each pipeline part, identify the abnormal working data of the abnormal working data type of the pipeline part through a working data anomaly judgment program based on the current working data of each working data type of the pipeline part, and identify the structural difference information of the pipeline part based on the pipeline structure model of the pipeline part and the normal pipeline structure models of each pipeline part;

[0125] Take the abnormal working data of the abnormal working data type of the pipeline part and the structural difference information of the pipeline part as the structural working abnormal information of the pipeline part.

[0126] Optionally, the recognition module 330 is specifically configured to:

[0127] Split each target sensing data into sub-target sensing data of each pipeline part;

[0128] For each pipeline part, based on the sub-target sensing data of the pipeline part and the sensing data types corresponding to the sub-target sensing data, through the pipeline anomaly analysis network, identify the anomaly degree values of each pipeline anomaly type of the pipeline part;

[0129] Take the anomaly degree values of each pipeline anomaly type of the pipeline part as the pipeline operation anomaly information of the pipeline part.

[0130] Optionally, the recognition module 330 is specifically configured to:

[0131] Based on the abnormal working data of the abnormal working data type of each pipeline part and the structural difference information of each pipeline part, construct a pipeline structure state distribution map of the pipeline, and based on the anomaly degree values of each pipeline anomaly type of each pipeline part, perform anomaly marking processing in the pipeline structure state distribution map to obtain the pipeline state distribution map of the pipeline;

[0132] Based on the pipeline state distribution map of the pipeline, through the pipeline fault analysis network, identify the fault types of each pipeline fault area of the pipeline and the fault probability values of each pipeline fault area of the pipeline;

[0133] Take the pipeline part to which each pipeline fault area belongs as the abnormal pipeline part, and take the fault types of each pipeline fault area and the pipeline anomaly probability values of each pipeline fault area as the pipeline fault types of each abnormal pipeline part and the pipeline anomaly probabilities of each abnormal pipeline part.

[0134] Each module in the intelligent detection device for the above pipeline faults can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0135] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 4As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an intelligent detection method for pipeline failures. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0136] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0137] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps of an intelligent detection method for pipeline failures.

[0138] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps of an intelligent detection method for pipeline failures.

[0139] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps of an intelligent detection method for pipeline failures.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0141] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0142] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0143] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An intelligent detection method for pipeline faults, characterized in that, The method includes: Obtain the current sensing data of each pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline working detection data of the pipeline, and the current structure scanning data of the pipeline, and based on the environmental change data, identify the environmental interference data of the environment where the pipeline is located; Based on the environmental interference data, adjust each of the current sensing data to obtain the target sensing data of each pipeline, and based on the pipeline working detection data of the pipeline and the current structure scanning data of the pipeline, identify the structural working anomaly information of each pipeline part of the pipeline; Based on the target sensing data of each pipeline part of the pipeline, identify the pipeline operation anomaly information of each pipeline part of the pipeline, and based on the structural working anomaly information of each pipeline part and the pipeline operation anomaly information of each pipeline part, through the pipeline anomaly analysis strategy, identify the pipeline fault type of each abnormal pipeline part and the pipeline anomaly probability of each abnormal pipeline part; 2. The method according to claim 1, wherein The identifying the environmental interference data of the environment where the pipeline is located based on the environmental change data includes: Split the environmental change data into the environmental data distribution information of each environmental type, and based on the environmental data distribution information of each environmental type, query the environmental interference factor values corresponding to each environmental interference factor through the environmental interference database; Use the environmental interference factor values corresponding to each environmental interference factor as the environmental interference data of the environment where the pipeline is located.

3. The method according to claim 2, wherein The adjusting each of the current sensing data based on the environmental interference data to obtain the target sensing data of each pipeline includes: Identify the sensing data type corresponding to each current sensing data, and query the sensing data interference database to identify the target environmental interference factors corresponding to each sensing data type and the data conversion program between each sensing data type and the target environmental interference factors corresponding to each sensing data type; Through each of the data conversion programs, convert the target environmental interference factors corresponding to each sensing data type into the sensing data adjustment values of each sensing data type; Based on the current sensing data of each sensing data type and the sensing data adjustment values of each sensing data type, calculate the target sensing data corresponding to each sensing data type.

4. The method according to claim 3, characterized in that, The identifying the structural working anomaly information of each pipeline part of the pipeline based on the pipeline working detection data of the pipeline and the current structure scanning data of the pipeline includes: Split the pipeline working detection data of the pipeline into the current working data of each working data type of each pipeline part, and based on the current structure scanning data of the pipeline, construct the pipeline structure model of each pipeline part of the pipeline; For each pipeline part, based on the current working data of each working data type of the pipeline part, through the working data anomaly judgment program, identify the abnormal working data of the abnormal working data type of the pipeline part, and based on the pipeline structure model of the pipeline part and the normal pipeline structure models of each pipeline part, identify the structural difference information of the pipeline part; Use the abnormal working data of the abnormal working data type of the pipeline section and the structural difference information of the pipeline section as the structural working abnormal information of the pipeline section.

5. The method according to claim 4, wherein Identifying the pipeline operation abnormal information of each pipeline section of the pipeline based on the respective target sensing data of the pipeline includes: Split each target sensing data into sub-target sensing data of each pipeline section; For each pipeline section, based on the respective sub-target sensing data of the pipeline section and the sensing data types corresponding to the respective sub-target sensing data, through a pipeline anomaly analysis network, identify the anomaly degree values of each pipeline anomaly type of the pipeline section; Use the anomaly degree values of each pipeline anomaly type of the pipeline section as the pipeline operation abnormal information of the pipeline section.

6. The method according to claim 5, wherein Based on the structural working abnormal information of each pipeline section and the pipeline operation abnormal information of each pipeline section, through a pipeline anomaly analysis strategy, identify the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section, including: Based on the abnormal working data of the abnormal working data type of each pipeline section and the structural difference information of each pipeline section, construct a pipeline structure state distribution map of the pipeline, and based on the anomaly degree values of each pipeline anomaly type of each pipeline section, perform anomaly marking processing in the pipeline structure state distribution map to obtain the pipeline state distribution map of the pipeline; Based on the pipeline state distribution map of the pipeline, through a pipeline fault analysis network, identify the fault types of each pipeline fault area of the pipeline and the fault probability values of each pipeline fault area of the pipeline; Use the pipeline section to which each pipeline fault area belongs as an abnormal pipeline section, and use the fault types of each pipeline fault area and the fault probability values of each pipeline fault area as the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section.

7. An intelligent detection device for pipeline faults, characterized in that, The device includes: An acquisition module, configured to acquire each current sensing data of the pipeline, the current environmental change data of the environment where the pipeline is located, the pipeline work detection data of the pipeline, and the current structure scan data of the pipeline, and based on the environmental change data, identify the environmental interference data of the environment where the pipeline is located; An adjustment module, configured to adjust each current sensing data based on the environmental interference data to obtain each target sensing data of the pipeline, and based on the pipeline work detection data of the pipeline and the current structure scan data of the pipeline, identify the structural working abnormal information of each pipeline section of the pipeline; An identification module, configured to identify the pipeline operation abnormal information of each pipeline section of the pipeline based on the respective target sensing data of the pipeline, and based on the structural working abnormal information of each pipeline section and the pipeline operation abnormal information of each pipeline section, through a pipeline anomaly analysis strategy, identify the pipeline fault types of each abnormal pipeline section and the pipeline anomaly probabilities of each abnormal pipeline section.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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