CN118408590B - Pipeline health monitoring system based on machine learning - Google Patents

Abstract

The present invention relates to the technical field of pipeline health monitoring systems, and specifically to a pipeline health monitoring system based on machine learning, the system comprising an information storage module, an auxiliary monitoring module, a control module, and a communication module; the control module evaluates pipeline health based on stored information and detection information and obtains pipeline health auxiliary judgment information, and transmits the pipeline health auxiliary judgment information to the communication module; the communication module transmits the pipeline health auxiliary judgment information to the user end. The control module obtains pipeline health auxiliary judgment information through environmental characteristics, geological characteristics, and human activities, and can estimate pipeline health through pipeline health auxiliary judgment information. The user end can directly view the information and make corresponding decisions, and the above judgment method does not affect the transmission of the medium in the pipeline.

Description

Pipeline health monitoring system based on machine learning

Technical Field

The present invention relates to the technical field of pipeline health monitoring systems, and in particular to a pipeline health monitoring system based on machine learning.

Background Art

A system that uses machine learning technology to achieve real-time monitoring, analysis, and prediction of pipeline system status. Such a system combines sensor technology and data collection to improve the safety, reliability, and efficiency of pipeline operation. The pipeline health monitoring system based on machine learning can not only monitor the current status of the pipeline in real time, but also predict possible problems by learning historical data. This capability enables the system to provide early warning and take measures to prevent potential failures. For pipelines buried underground, a corresponding monitoring system is particularly needed.

A comprehensive detection method for underground gas pipelines with announcement number CN110779860A includes the following steps: using the close interval potential method (CIPS) and direct current potential gradient detection (DCVG) combined detection technology to detect the outer anti-corrosion layer of the pipeline to determine the damage location, damage size and cathodic protection status; using an ultrasonic guided wave detector to detect the corrosion of the inner wall of the pipeline to determine the corrosion location and size; using a diameter detector to detect the deformation of the pipeline to determine the location and degree of deformation of the pipeline pits, elliptical deformation, and wrinkles.

However, the detection method of the existing technology is relatively simple, which affects the detection results.

Summary of the invention

The purpose of the present invention is to analyze pipeline health from multiple aspects and propose a pipeline health monitoring system based on machine learning to address the above-mentioned shortcomings.

The present invention adopts the following technical solution:

A pipeline health monitoring system based on machine learning, the system includes an information storage module, an auxiliary monitoring module, a control module, and a communication module;

The information storage module is used to store information;

The auxiliary monitoring module includes a humidity detection submodule, a pH detection submodule, a salt content detection submodule, a conductivity detection submodule, an oxygen content detection submodule, a detection submodule, and a water level detection submodule. The humidity detection submodule, the pH detection submodule, the salt content detection submodule, the conductivity detection submodule, the oxygen content detection submodule, the detection submodule, and the water level detection submodule are used to detect and obtain detection information;

The control module evaluates the pipeline health according to the stored information and the detection information and obtains the pipeline health auxiliary judgment information, and transmits the pipeline health auxiliary judgment information to the communication module;

The communication module transmits the pipeline health auxiliary judgment information to the user end.

Optionally, when the control module calculates the pipeline health auxiliary judgment information, the following formula is satisfied:

;

;

;

;

;

;

in, Provides information to assist in determining pipeline health. It is an auxiliary factor for judging pipeline health. is the selection threshold of pipeline health auxiliary judgment factor, when When the auxiliary judgment result is that the pipeline is healthy, When the auxiliary judgment result is that the pipeline is unhealthy;

is the influencing factor of environmental characteristics on pipeline health, is the environmental characteristic weight index, is the impact factor of human activities on pipeline health, is the human activity weight index, is the influence factor of geological characteristics on pipeline health, is the geological characteristic weight index;

For the The measured soil moisture value during the first test is is the ideal soil moisture value, is the total number of soil tests, is the soil pH reference index, The values are as follows: or ,when When the average value of the soil pH measured is within the range of the average value of the ideal soil pH, the rest are , The actual soil salt content during the first test. For the The actual soil salt content during the first test was For the The actual soil salt content during the first test was is the measured conductivity of the soil during the first test. For the The measured conductivity of the soil during the first test was For the The measured conductivity of the soil during the first test was is the reference index of soil oxygen content, The values are as follows: or ,when When the average value of the measured soil oxygen content is within the range of the ideal soil oxygen content, the rest are ;

is the buried depth of the pipeline, is the total number of excavations in the area through which the pipeline passes, is the maximum depth of excavation during the bth excavation, is the duration of the bth excavation;

Measure the groundwater level in the area where the pipeline passes. The ideal groundwater level in the area through which the pipeline passes.

Optionally, the information storage module includes a weight information storage submodule, an ideal information storage submodule, and an excavation information storage submodule. The weight information storage submodule is used to store the environmental feature weight index, the human activity weight index, and the geological feature weight index and transmit them to the control module. The ideal information storage submodule is used to store the ideal soil moisture value, the total number of soil detections, and the ideal groundwater level in the area where the pipeline passes through and transmit them to the control module. The excavation information storage submodule is used to store the total number of excavations in the area where the pipeline passes through, the maximum excavation depth during the bth excavation, and the duration of the bth excavation and transmit them to the control module.

The humidity detection submodule is used to detect and obtain the The actual soil humidity value during the first detection is transmitted to the control module;

The pH detection submodule is used to detect the actual pH of the soil and obtain a soil pH reference index, and transmit it to the control module;

The salt content detection submodule is used to detect and obtain the actual salt content of the soil during each detection, and transmit it to the control module;

The conductivity detection submodule is used to detect and obtain the actual conductivity of the soil during each detection, and transmit it to the control module;

The oxygen content detection submodule is used to detect the actual oxygen content of the soil and obtain a soil oxygen content reference index, and transmit it to the control module;

The detection submodule is used to detect the maximum depth of excavation during each excavation and obtain the pre-buried depth of the pipeline, and transmit it to the control module;

The water level detection submodule is used to detect and obtain the actual groundwater level in the area where the pipeline passes, and transmit it to the control module;

The control module obtains the influence factor of geological characteristics on pipeline health according to the measured groundwater level in the area where the pipeline passes, obtains the influence factor of human activities on pipeline health according to the pre-buried depth of the pipeline, the total number of excavations in the area where the pipeline passes, the maximum depth of the excavation during the bth excavation, and the duration of the bth excavation. The measured soil moisture value during each test, the ideal soil moisture value, the total number of soil tests, the soil pH reference index, the measured soil salt content during each test, the measured soil conductivity during each test, and the soil oxygen content reference index are used to obtain the impact factor of environmental characteristics on pipeline health. Based on the impact factors of environmental characteristics on pipeline health, the impact factors of human activities on pipeline health, the impact factors of geological characteristics on pipeline health, the environmental characteristics weight index, the human activities weight index, and the geological characteristics weight index, the pipeline health auxiliary judgment factor is obtained. Based on the pipeline health auxiliary judgment factor, the pipeline health auxiliary judgment information is obtained.

Optionally, the pH detection submodule includes a pH meter and a pH comparator;

The acidity meter is used to detect and obtain the actual pH value of the soil;

The pH comparator obtains the average value of the measured pH of the soil based on the measured pH of the soil, and obtains the soil pH reference index after comparing the average value of the measured pH of the soil and the average value of the ideal pH of the soil, and transmits it to the control module.

Optionally, the oxygen content detection submodule includes a gas analyzer and an oxygen content comparator;

The gas analyzer is used to detect and obtain the actual oxygen content of the soil;

The oxygen content comparator obtains the average value of the actual soil oxygen content according to the actual soil oxygen content, and obtains the soil oxygen content reference index after comparing the average value of the actual soil oxygen content and the average value of the ideal soil oxygen content, and transmits it to the control module.

Optionally, the salt content detection submodule includes a sample preparer and a salt content meter;

The sample preparation device is used to collect soil samples and remove impurities such as stones and plant residues;

The salt content meter is used to detect and obtain the actual salt content of the soil during each detection, and transmit it to the control module.

Optionally, the conductivity detection submodule includes a sample processor and a conductivity meter;

The sample processor is used to collect soil samples and add distilled water to form a soil solution;

The conductivity meter is used to detect and obtain the actual conductivity of the soil during each detection, and transmit it to the control module.

The beneficial effects achieved by the present invention are:

The control module obtains auxiliary judgment information on pipeline health through environmental characteristics, geological characteristics, and human activities. The pipeline health can be estimated through the auxiliary judgment information on pipeline health. The user end can directly view the information and make corresponding decisions. The above judgment method does not affect the transmission of the medium in the pipeline.

To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a schematic diagram of the overall structure of the present invention;

FIG2 is a schematic diagram of the structure of the pH detection submodule in the present invention;

FIG3 is a schematic diagram of the structure of the oxygen content detection submodule in the present invention;

FIG4 is a schematic diagram of the structure of the salt content detection submodule in the present invention;

FIG5 is a schematic diagram of the structure of the conductivity detection submodule in the present invention;

FIG6 is a schematic diagram of the overall structure of Embodiment 2 of the present invention;

FIG7 is a schematic diagram of the structure of the eddy current detection submodule in the present invention;

FIG8 is a schematic diagram of the structure of the ray detection submodule in the present invention.

DETAILED DESCRIPTION

The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

Embodiment 1: This embodiment provides a pipeline health monitoring system based on machine learning, as shown in combination with FIG. 1 to FIG. 5 .

A pipeline health monitoring system based on machine learning, the system includes an information storage module, an auxiliary monitoring module, a control module, and a communication module;

The information storage module is used to store information;

The auxiliary monitoring module includes a humidity detection submodule, a pH detection submodule, a salt content detection submodule, a conductivity detection submodule, an oxygen content detection submodule, a detection submodule, and a water level detection submodule. The humidity detection submodule, the pH detection submodule, the salt content detection submodule, the conductivity detection submodule, the oxygen content detection submodule, the detection submodule, and the water level detection submodule are used to detect and obtain detection information;

The control module evaluates the pipeline health according to the stored information and the detection information and obtains the pipeline health auxiliary judgment information, and transmits the pipeline health auxiliary judgment information to the communication module;

The communication module transmits the pipeline health auxiliary judgment information to the user end.

Optionally, when the control module calculates the pipeline health auxiliary judgment information, the following formula is satisfied:

;

;

;

;

;

;

in, Provides information to assist in determining pipeline health. It is an auxiliary factor for judging pipeline health. is the selection threshold of pipeline health auxiliary judgment factor, when When the auxiliary judgment result is that the pipeline is healthy, When the auxiliary judgment result is that the pipeline is unhealthy;

is the influencing factor of environmental characteristics on pipeline health, is the environmental characteristic weight index, is the impact factor of human activities on pipeline health, is the human activity weight index, is the influence factor of geological characteristics on pipeline health, is the geological characteristic weight index;

For the The measured soil moisture value during the first test is is the ideal soil moisture value, is the total number of soil tests, is the soil pH reference index, The values are as follows: or ,when When the average value of the soil pH measured is within the range of the average value of the ideal soil pH, the rest are , The actual soil salt content during the first test. For the The actual soil salt content during the first test was For the The actual soil salt content during the first test was is the measured conductivity of the soil during the first test. For the The measured conductivity of the soil during the first test was For the The measured conductivity of the soil during the first test was is the reference index of soil oxygen content, The values are as follows: or ,when When the average value of the measured soil oxygen content is within the range of the ideal soil oxygen content, the rest are ;

is the buried depth of the pipeline, is the total number of excavations in the area through which the pipeline passes, is the maximum depth of excavation during the bth excavation, is the duration of the bth excavation;

Measure the groundwater level in the area where the pipeline passes. The ideal groundwater level in the area through which the pipeline passes.

Specifically, the environmental characteristic weight index, the human activity weight index, and the geological characteristic weight index are set by those skilled in the art, who set specific values based on actual circumstances. For example, when the soil erosion in the geographical area through which the pipeline passes is extremely serious, the environmental characteristic weight index may be set to twice the geological characteristic weight index; "environmental characteristics" refers to the fact that the geographical area through which the pipeline passes may be affected by different environmental factors, such as soil type and moisture; "geological characteristics" refers to the fact that the geology of the geographical area through which the pipeline passes may affect the stability of the pipeline, such as groundwater level and infiltration; "human activities" refers to the fact that there may be construction and human excavation activities in the geographical area through which the pipeline passes, which may cause mechanical damage to the pipeline; the ideal soil moisture value is determined by those skilled in the art based on the material of the pipeline. , the ideal soil moisture value is not the lower the better. The lower the ideal soil moisture value is, the drier the soil is, which is also prone to cause mechanical wear of the pipeline. The total number of soil tests is set by technical personnel in this field according to the service life of the testing equipment. For example, the longer the testing equipment is used, the greater the degree of aging of the testing equipment is, and the corresponding total number of soil tests is set to a larger value. The following matters need to be noted when calculating the soil pH reference index: based on the total number of soil tests, the pH value of different locations is tested and the average value of the actual soil pH value is calculated. The average value of the ideal soil pH value is set by technical personnel in this field according to the pipeline material. Generally, acidic soil will accelerate the oxidation reaction on the metal surface, and alkaline soil will cause pipeline cracking. The actual soil conductivity during the first test, the second test, the average value of the ideal soil pH value is set by technical personnel in this field according to the pipeline material. Generally, acidic soil will accelerate the oxidation reaction on the metal surface, and alkaline soil will cause pipeline cracking. The measured conductivity of the soil during the first test, The unit of the measured conductivity of the soil during each test is Siemens per meter; the ideal soil oxygen content is set by technicians in this field according to the pipeline material, because different pipeline materials have different sensitivities to oxygen content; the following matters need to be noted when calculating the soil oxygen content reference index: according to the total number of soil tests, the oxygen content at different locations is tested and then the average value of the measured soil oxygen content is calculated. The average value of the ideal soil oxygen content is set by technicians in this field according to the pipeline material. For example, for plastic pipes, high oxygen content will not directly lead to corrosion problems in the pipes, but for metal pipes, high oxygen content will accelerate the corrosion of metal. The oxidation reaction of the metals leads to corrosion of the pipeline; the unit of the pre-buried depth of the pipeline is meter, which refers to the vertical distance between the bottom of the pipeline and the surface of the ground; the following matters need to be noted when calculating the total number of excavations in the area through which the pipeline passes: "the total number of excavations in the area through which the pipeline passes" refers to the total number of excavations carried out directly above the length of the pipeline; the unit of the maximum depth of excavation during the bth construction is meter; the duration of the bth excavation is day, and a day is counted as a day if it is less than a day, and "the duration of excavation" refers to the total time from excavation to completion of construction; the measured ground of the area through which the pipeline passes The unit of the water level and the ideal groundwater level in the area where the pipeline passes is meter; the ideal groundwater level in the area where the pipeline passes is determined by technical personnel in this field based on the pipeline material, soil type, and hydrological cycle; the following matters need to be noted when calculating the impact factor of environmental characteristics on pipeline health. Since the pipeline is pre-buried underground, the corresponding detection information of the detection part corresponding to the impact factor of environmental characteristics on pipeline health has been detected before the pipeline is pre-buried. The data obtained for the first time can be used in subsequent calculations. When the pipeline needs to be excavated for maintenance during use, the impact of environmental characteristics on pipeline health should be re-detected. For example, if the pipeline is pre-buried on January 1, 2023, the detection information corresponding to the impact factors of environmental characteristics on pipeline health is obtained through detection before January 1, 2023. The detection information corresponding to the impact factors of environmental characteristics on pipeline health obtained through detection before January 1, 2023 is directly used in the period calculation. The pipeline is excavated on May 1, 2023 for maintenance, and the detection information corresponding to the impact factors of environmental characteristics on pipeline health can be obtained through detection after May 1, 2023, that is, the updated detection information is used after May 1, 2023.

The above units are only examples, and those skilled in the art may set different conductivity and groundwater level units according to actual needs when implementing the present solution.

Optionally, the information storage module includes a weight information storage submodule, an ideal information storage submodule, and an excavation information storage submodule. The weight information storage submodule is used to store the environmental feature weight index, the human activity weight index, and the geological feature weight index and transmit them to the control module. The ideal information storage submodule is used to store the ideal soil moisture value, the total number of soil detections, and the ideal groundwater level in the area where the pipeline passes through and transmit them to the control module. The excavation information storage submodule is used to store the total number of excavations in the area where the pipeline passes through, the maximum excavation depth during the bth excavation, and the duration of the bth excavation and transmit them to the control module.

The humidity detection submodule is used to detect and obtain the The actual soil humidity value during the first detection is transmitted to the control module;

The pH detection submodule is used to detect the actual pH of the soil and obtain a soil pH reference index, and transmit it to the control module;

The salt content detection submodule is used to detect and obtain the actual salt content of the soil during each detection, and transmit it to the control module;

The conductivity detection submodule is used to detect and obtain the actual conductivity of the soil during each detection, and transmit it to the control module;

The oxygen content detection submodule is used to detect the actual oxygen content of the soil and obtain a soil oxygen content reference index, and transmit it to the control module;

The detection submodule is used to detect the maximum depth of excavation during each excavation and obtain the pre-buried depth of the pipeline, and transmit it to the control module;

The water level detection submodule is used to detect and obtain the actual groundwater level in the area where the pipeline passes, and transmit it to the control module;

The control module obtains the influence factor of geological characteristics on pipeline health according to the measured groundwater level in the area where the pipeline passes, obtains the influence factor of human activities on pipeline health according to the pre-buried depth of the pipeline, the total number of excavations in the area where the pipeline passes, the maximum depth of the excavation during the bth excavation, and the duration of the bth excavation. The measured soil moisture value during each test, the ideal soil moisture value, the total number of soil tests, the soil pH reference index, the measured soil salt content during each test, the measured soil conductivity during each test, and the soil oxygen content reference index are used to obtain the impact factor of environmental characteristics on pipeline health. Based on the impact factors of environmental characteristics on pipeline health, the impact factors of human activities on pipeline health, the impact factors of geological characteristics on pipeline health, the environmental characteristics weight index, the human activities weight index, and the geological characteristics weight index, the pipeline health auxiliary judgment factor is obtained. Based on the pipeline health auxiliary judgment factor, the pipeline health auxiliary judgment information is obtained.

Optionally, the pH detection submodule includes a pH meter and a pH comparator;

The acidity meter is used to detect and obtain the actual pH value of the soil;

The pH comparator obtains the average value of the measured pH of the soil based on the measured pH of the soil, and obtains the soil pH reference index after comparing the average value of the measured pH of the soil and the average value of the ideal pH of the soil, and transmits it to the control module.

Optionally, the oxygen content detection submodule includes a gas analyzer and an oxygen content comparator;

The gas analyzer is used to detect and obtain the actual oxygen content of the soil;

The oxygen content comparator obtains the average value of the actual soil oxygen content according to the actual soil oxygen content, and obtains the soil oxygen content reference index after comparing the average value of the actual soil oxygen content and the average value of the ideal soil oxygen content, and transmits it to the control module.

Optionally, the salt content detection submodule includes a sample preparer and a salt content meter;

The sample preparation device is used to collect soil samples and remove impurities such as stones and plant residues;

The salt content meter is used to detect and obtain the actual salt content of the soil during each detection, and transmit it to the control module.

Optionally, the conductivity detection submodule includes a sample processor and a conductivity meter;

The sample processor is used to collect soil samples and add distilled water to form a soil solution;

The conductivity meter is used to detect and obtain the actual conductivity of the soil during each detection, and transmit it to the control module.

This embodiment solves the problem that the traditional monitoring system has a relatively single way of evaluating pipeline health. Specifically, the control module obtains pipeline health auxiliary judgment information through environmental characteristics, geological characteristics, and human activities. The pipeline health can be estimated through the pipeline health auxiliary judgment information. The user end can directly view the information and make corresponding decisions, and the above judgment method does not affect the transmission of the medium in the pipeline.

Embodiment 2: This embodiment includes all the contents of Embodiment 1, and provides a pipeline health monitoring system based on machine learning, as shown in combination with Figures 6 to 8.

A pipeline health monitoring system based on machine learning, which also includes an information setting module and a main monitoring module;

The information setting module is used to set information;

The main monitoring modules include eddy current detection submodule, ray detection submodule, pressure detection submodule, flow velocity detection submodule, temperature detection submodule and roughness detection submodule. The eddy current detection submodule, ray detection submodule, pressure detection submodule, flow velocity detection submodule, temperature detection submodule and roughness detection submodule are used for detection and obtaining detection data.

The control module evaluates the pipeline health according to the setting information and the detection data and obtains the main judgment information of the pipeline health, and transmits the main judgment information of the pipeline health to the communication module;

The communication module transmits the main judgment information of pipeline health to the user end.

Optionally, when the control module calculates the main judgment information of pipeline health, the following formula is satisfied:

;

;

;

;

in, It is the main information for judging pipeline health. It is the main factor to determine the health of pipeline. is the threshold value for the main factor in determining pipeline health. When the pipeline is directly judged to be healthy, When the pipeline is directly judged to be unhealthy;

is the total number of cracks on the pipeline surface, is the length of the dth crack, is the crack detection error compensation index, is the total number of internal inspections of the pipeline, For the The actual wall thickness of the pipe during the first test. is the theoretical wall thickness of the pipe, For the The actual pressure value of the medium inside the pipeline during the first test. is the theoretical pressure value of the medium inside the pipeline, For the The actual flow rate of the medium inside the pipeline during the first test. is the theoretical flow rate of the medium inside the pipeline, For the The actual temperature value of the medium inside the pipeline during the first test. is the theoretical temperature value of the medium inside the pipeline;

is the total number of pipeline surface roughness tests, For the The roughness of the pipe surface during the first test, is the initial roughness of the pipeline surface;

is the total length of the pipeline.

Specifically, the threshold of the main judgment factor of pipeline health is determined by technicians in this field according to the pipeline material and the medium transported by the pipeline; the roughness of the pipeline surface will cause the attenuation of the eddy current signal, so a corresponding crack detection error compensation index is added; The actual wall thickness of the pipeline during the first test and the theoretical wall thickness of the pipeline are both in millimeters; the theoretical wall thickness of the pipeline is obtained by testing before the pipeline is buried; The units of the actual pressure value of the medium inside the pipeline and the theoretical pressure value of the medium inside the pipeline during the first test are both Pa; The actual flow rate of the medium inside the pipeline during the first test and the theoretical flow rate of the medium inside the pipeline are both in meters per second; The actual temperature value of the medium inside the pipeline and the theoretical temperature value of the medium inside the pipeline during the first test are both in degrees Celsius; The units of the roughness of the pipeline surface during the first inspection and the initial roughness of the pipeline surface are both microns. The initial roughness of the pipeline surface refers to the surface roughness of the pipeline after leaving the factory. The pipeline is tested before it is buried. Under ideal conditions, the test obtains the corresponding theoretical pressure value of the medium inside the pipeline, the theoretical flow rate of the medium inside the pipeline, the theoretical temperature value of the medium inside the pipeline, and the initial roughness of the pipeline surface.

The above units are only examples, and those skilled in the art can set different flow rates, temperature values, pressure values, and roughness units according to actual needs when implementing this solution.

Optionally, the information setting module is used to set the total number of internal pipeline inspections, the theoretical wall thickness of the pipeline, the theoretical pressure value of the medium inside the pipeline, the theoretical flow rate of the medium inside the pipeline, the theoretical temperature value of the medium inside the pipeline, the total length of the pipeline, and the initial roughness of the pipeline surface, and transmit them to the control module;

The eddy current detection submodule is used to detect and obtain the total number of cracks on the pipeline surface and the length of the dth crack, and transmit them to the control module;

The ray detection submodule is used to detect and obtain the The actual wall thickness of the pipe during the first test is transmitted to the control module;

The pressure detection submodule is used to detect and obtain the The actual pressure value of the medium inside the pipeline during the first detection is transmitted to the control module;

The flow rate detection submodule is used to detect and obtain the The actual flow rate of the medium inside the pipeline during the first detection is transmitted to the control module;

The temperature detection submodule is used to detect and obtain the The actual temperature value of the medium inside the pipeline during the first detection is transmitted to the control module;

The roughness detection submodule is used to detect and obtain the The roughness of the pipeline surface during the first detection is transmitted to the control module;

The control module obtains the total number of pipeline surface roughness detections based on the total length of the pipeline, and The crack detection error compensation index is obtained based on the roughness of the pipeline surface during the first detection. The crack detection error compensation index, the total number of cracks on the pipeline surface, the length of the dth crack, the total number of internal pipeline detections, and the dth crack length are used to calculate the crack detection error compensation index. The actual wall thickness of the pipe during the first test, the theoretical wall thickness of the pipe, The actual pressure value of the medium inside the pipeline during the first test, the theoretical pressure value of the medium inside the pipeline, The actual flow rate of the medium inside the pipeline during the first test, the theoretical flow rate of the medium inside the pipeline, The actual temperature value of the medium inside the pipeline during the first detection and the theoretical temperature value of the medium inside the pipeline are used to determine the main factors for judging the health of the pipeline, and the main judgment information on the health of the pipeline is obtained based on the main factors for judging the health of the pipeline.

Optionally, the eddy current detection submodule includes an excitation coil, a detection coil, a recorder, and an eddy current analyzer;

The excitation coil is used to generate an alternating current;

The detection coil is used to sense the eddy current caused by the alternating current;

The recorder is used to record the changing alternating current caused when the eddy current encounters the crack;

The eddy current analyzer is used to analyze the changing alternating current, determine the location, shape, and length of the cracks, and obtain the total number of cracks on the pipeline surface and the length of the dth crack, and transmit them to the control module.

Optionally, the radiation detection submodule includes a radiation source, a detector, and a radiation analyzer;

The ray source is used for emitting rays to the pipeline;

Detectors are used to measure the intensity of the radiation;

The X-ray analyzer obtains the first The actual wall thickness of the pipe during the first detection is determined and transmitted to the control module.

This embodiment solves the problem that the traditional monitoring system has a relatively single method for evaluating pipeline health. Specifically, the control module obtains the main judgment information of pipeline health by monitoring the inside of the pipeline. The user end can directly view the information and make corresponding decisions, and the above judgment method is relatively accurate.

The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention description and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims (6)

1. A pipeline health monitoring system based on machine learning, characterized in that the system includes an information storage module, an auxiliary monitoring module, a control module, and a communication module; The information storage module is used to store information; The auxiliary monitoring module includes a humidity detection submodule, a pH detection submodule, a salt content detection submodule, a conductivity detection submodule, an oxygen content detection submodule, a detection submodule, and a water level detection submodule. The humidity detection submodule, the pH detection submodule, the salt content detection submodule, the conductivity detection submodule, the oxygen content detection submodule, the detection submodule, and the water level detection submodule are used for detection and obtaining detection information; the detection submodule is used for detecting the maximum depth of excavation during each excavation; The control module evaluates the pipeline health according to the stored information and the detection information and obtains the pipeline health auxiliary judgment information, and transmits the pipeline health auxiliary judgment information to the communication module; The communication module transmits the pipeline health auxiliary judgment information to the user end; when the control module calculates the pipeline health auxiliary judgment information, the following formula is satisfied: ; in, Provides information to assist in determining pipeline health. It is an auxiliary factor for judging pipeline health. is the selection threshold of pipeline health auxiliary judgment factor, when When the auxiliary judgment result is that the pipeline is healthy, When the auxiliary judgment result is that the pipeline is unhealthy; is the influencing factor of environmental characteristics on pipeline health, is the environmental characteristic weight index, is the impact factor of human activities on pipeline health, is the human activity weight index, is the influence factor of geological characteristics on pipeline health, is the geological characteristic weight index; For the The measured soil moisture value during the first test is is the ideal soil moisture value, is the total number of soil tests, is the soil pH reference index, The values are as follows: ,when When the average value of the soil pH measured is within the range of the average value of the ideal soil pH, the rest are , The actual soil salt content during the first test. For the The actual soil salt content during the first test was For the The actual soil salt content during the first test was is the measured conductivity of the soil during the first test. For the The measured conductivity of the soil during the first test was For the The measured conductivity of the soil during the first test was is the reference index of soil oxygen content, The values are as follows: ,when When the average value of the measured soil oxygen content is within the range of the ideal soil oxygen content, the rest are ; is the buried depth of the pipeline, is the total number of excavations in the area through which the pipeline passes, is the maximum depth of excavation during the bth excavation, is the duration of the bth excavation; Measure the groundwater level in the area where the pipeline passes. The ideal groundwater level in the area through which the pipeline passes. 2. The machine learning-based pipeline health monitoring system according to claim 1, characterized in that the information storage module comprises a weight information storage submodule, an ideal information storage submodule, and an excavation information storage submodule, wherein the weight information storage submodule is used to store an environmental feature weight index, a human activity weight index, and a geological feature weight index and transmit them to the control module, the ideal information storage submodule is used to store an ideal soil moisture value, a total number of soil detections, and an ideal groundwater level in the area through which the pipeline passes and transmit them to the control module, and the excavation information storage submodule is used to store a total number of excavations in the area through which the pipeline passes, a maximum excavation depth during the bth excavation, and a duration of the bth excavation and transmit them to the control module; The humidity detection submodule is used to detect and obtain the The actual soil humidity value during the first detection is transmitted to the control module; The pH detection submodule is used to detect the actual pH of the soil and obtain a soil pH reference index, and transmit it to the control module; The salt content detection submodule is used to detect and obtain the actual salt content of the soil during each detection, and transmit it to the control module; The conductivity detection submodule is used to detect and obtain the actual conductivity of the soil during each detection, and transmit it to the control module; The oxygen content detection submodule is used to detect the actual oxygen content of the soil and obtain a soil oxygen content reference index, and transmit it to the control module; The detection submodule is used to detect the maximum depth of excavation during each excavation and obtain the pre-buried depth of the pipeline, and transmit it to the control module; The water level detection submodule is used to detect and obtain the actual groundwater level in the area where the pipeline passes, and transmit it to the control module; The control module obtains the influence factor of geological characteristics on pipeline health according to the measured groundwater level in the area where the pipeline passes, obtains the influence factor of human activities on pipeline health according to the pre-buried depth of the pipeline, the total number of excavations in the area where the pipeline passes, the maximum depth of the excavation during the bth excavation, and the duration of the bth excavation. The measured soil moisture value during each test, the ideal soil moisture value, the total number of soil tests, the soil pH reference index, the measured soil salt content during each test, the measured soil conductivity during each test, and the soil oxygen content reference index are used to obtain the impact factor of environmental characteristics on pipeline health. Based on the impact factors of environmental characteristics on pipeline health, the impact factors of human activities on pipeline health, the impact factors of geological characteristics on pipeline health, the environmental characteristics weight index, the human activities weight index, and the geological characteristics weight index, the pipeline health auxiliary judgment factor is obtained. Based on the pipeline health auxiliary judgment factor, the pipeline health auxiliary judgment information is obtained. 3. The pipeline health monitoring system based on machine learning according to claim 2, characterized in that the pH detection submodule includes a pH meter and a pH comparator; The acidity meter is used to detect and obtain the actual pH value of the soil; The pH comparator obtains the average value of the measured pH of the soil based on the measured pH of the soil, and obtains the soil pH reference index after comparing the average value of the measured pH of the soil and the average value of the ideal pH of the soil, and transmits it to the control module. 4. The pipeline health monitoring system based on machine learning according to claim 3, characterized in that the oxygen content detection submodule includes a gas analyzer and an oxygen content comparator; The gas analyzer is used to detect and obtain the actual oxygen content of the soil; The oxygen content comparator obtains the average value of the actual soil oxygen content according to the actual soil oxygen content, and obtains the soil oxygen content reference index after comparing the average value of the actual soil oxygen content and the average value of the ideal soil oxygen content, and transmits it to the control module. 5. The pipeline health monitoring system based on machine learning according to claim 4, characterized in that the salt content detection submodule includes a sample preparer and a salt content meter; The sample preparation device is used to collect soil samples and remove impurities such as stones and plant residues; The salt content meter is used to detect and obtain the actual salt content of the soil during each detection, and transmit it to the control module. 6. The pipeline health monitoring system based on machine learning according to claim 5, characterized in that the conductivity detection submodule includes a sample processor and a conductivity meter; The sample processor is used to collect soil samples and add distilled water to form a soil solution; The conductivity meter is used to detect and obtain the actual conductivity of the soil during each detection, and transmit it to the control module.