Intelligent pipeline wall thickness monitoring system, method, equipment, medium and product

Abstract

The invention relates to the technical field of pipe wall monitoring, and discloses an intelligent pipeline wall thickness monitoring system, method and device, a medium and a product, and the system comprises a collection unit which is configured to collect level data to obtain a first data set; collecting the level data of each Hall sensor again to obtain a second data set; the judgment unit is configured to compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and judge whether the pipeline to be monitored is corroded or not according to a comparison result; the processing unit is configured to adjust the acquisition time interval according to the adjustment coefficient; the adjustment unit is configured to determine whether to adjust the adjustment coefficient of the acquisition time interval. The problem that errors are large due to the fact that the manual degree is large is solved, the pipeline monitoring efficiency and accuracy are improved, and the adaptability and the intelligent level of the pipeline monitoring system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipe wall monitoring, and in particular to an intelligent pipe wall thickness monitoring system, method, equipment, medium and product. Background Art

[0002] In the modern industrial field, pipeline systems are widely used in industries such as oil, gas, chemicals and water treatment. Over time, pipelines are affected by corrosion, wear and other external factors, causing their wall thickness to gradually decrease, which may cause safety hazards such as leakage and explosion. Therefore, real-time monitoring of pipeline wall thickness becomes crucial.

[0003] However, traditional monitoring methods often rely on manual inspection and periodic testing, which has some obvious shortcomings. For example, relying on manual testing not only increases labor costs, but may also cause deviations in test results due to human factors, thereby affecting the assessment of pipeline health.

[0004] Therefore, how to solve the large errors in traditional detection methods is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, the present invention proposes an intelligent pipeline wall thickness monitoring system, method, equipment, medium and product, aiming to solve the problem of large errors caused by high reliance on manual labor in current pipeline monitoring technology.

[0006] In a first aspect, the present invention provides an intelligent pipeline wall thickness monitoring system, comprising:

[0007] A sensor array and a monitoring module, wherein the monitoring module is connected to the sensor array;

[0008] There are a plurality of sensor arrays, each of which is surrounded and arranged on the pipeline to be monitored, and each of which includes a plurality of Hall sensors;

[0009] The monitoring module includes a collection unit, a judgment unit, a processing unit and an adjustment unit;

[0010] The acquisition unit is configured to acquire level data of each Hall sensor in all the sensor arrays to obtain a first data set; after an acquisition time interval, the level data of each Hall sensor is acquired again to obtain a second data set; the acquisition time interval is determined by the acquisition unit according to the operation data of the pipeline to be monitored;

[0011] The judgment unit is configured to compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and judge whether the pipeline to be monitored has corrosion according to the comparison result;

[0012] The processing unit is configured to determine the corrosion area according to the level change, determine the adjustment coefficient of the collection time interval according to the corrosion rate, and adjust the collection time interval according to the adjustment coefficient when the judgment unit determines that the pipeline to be monitored has corrosion; the level change is the level data changed in the first data set and the second data set;

[0013] The adjustment unit is configured to collect the corrosion area and the real-time flow rate of the pipeline to be monitored from the processing unit, establish a feature set based on the corrosion area and the real-time flow rate, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to adjust the adjustment coefficient of the collection time interval based on the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval.

[0014] In some embodiments, the collection unit determines that the collection time interval satisfies the following formula according to the operation data of the pipeline to be monitored:

[0015]

[0016] Wherein, Tc represents the collection time interval, D represents the pipe diameter, ρ represents the density of the fluid in the pipe, Ac represents the cross-sectional area of ​​the pipe, Fs represents the sensitivity of the Hall sensor, β represents the temperature influence coefficient, β takes a value of 0.01-0.05, and Tavg represents the average temperature of the fluid in the pipe to be monitored.

[0017] In some embodiments, the judging unit judges whether the pipeline is corroded according to the comparison result, including:

[0018] When any level data in the second data set changes, the judgment unit determines that corrosion exists in the pipeline to be monitored;

[0019] When the level data in the second data set are all the same as those in the first data set, the judgment unit determines that there is no corrosion in the pipeline to be monitored.

[0020] In some embodiments, the processing unit determines the corrosion area according to the level change, including:

[0021] The processing unit determines the corrosion area according to the number of Hall sensors corresponding to the change position of the level data, and the corrosion area is proportional to the number of the Hall sensors.

[0022] In some embodiments, the processing unit determines an adjustment coefficient of the collection time interval according to the corrosion rate, and adjusts the collection time interval according to the adjustment coefficient, including:

[0023] The processing unit determines the corrosion rate according to the corrosion area and the collection time interval, determines an adjustment coefficient of the collection time interval corresponding to the corrosion rate interval to which the corrosion rate belongs, and adjusts the collection time interval based on the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval.

[0024] In some embodiments, the processing unit determines an adjustment coefficient of a collection time interval corresponding to a corrosion rate interval to which the corrosion rate belongs, and adjusts the collection time interval based on the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval, including:

[0025] When the corrosion rate is less than or equal to a first preset corrosion rate, the processing unit determines that the corrosion rate belongs to a first corrosion rate interval, determines a first preset adjustment coefficient corresponding to the first preset corrosion rate interval, and adjusts the collection time interval based on the first preset adjustment coefficient;

[0026] When the corrosion rate is greater than the first preset corrosion rate and less than or equal to the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to a second corrosion rate interval, determines a second preset adjustment coefficient corresponding to the second corrosion rate interval, and adjusts the collection time interval based on the second preset adjustment coefficient;

[0027] When the corrosion rate is greater than the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to a third corrosion rate interval, determines a third preset adjustment coefficient corresponding to the third corrosion rate interval, and adjusts the collection time interval based on the third preset adjustment coefficient;

[0028] Among them, the first corrosion rate is less than the second corrosion rate, the first preset adjustment coefficient is greater than the second preset adjustment coefficient, the second preset adjustment coefficient is greater than the third preset adjustment coefficient, and the third preset adjustment coefficient is greater than 0.

[0029] In some embodiments, the adjustment unit performs cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, including:

[0030] S1: The adjustment unit initializes K centroids in the historical operation data, and assigns the feature set to the nearest centroid to form K clusters;

[0031] S2: the adjustment unit recalculates the centroid of each cluster;

[0032] S3: the adjustment unit repeats S1 and S2 until the center of mass no longer changes or a preset number of times are completed;

[0033] S4: the adjustment unit constructs the remaining historical operation data in the cluster where the feature set is located into a similar set;

[0034] The centroid calculation formula is as follows:

[0035]

[0036] Among them, Zk represents the centroid of the kth cluster, |Ck| represents the number of data in the kth cluster, and Ti represents the feature vector of the i-th data.

[0037] In some embodiments, the adjustment unit determines whether to adjust the adjustment coefficient of the collection time interval according to the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval, including:

[0038] The adjustment unit extracts the adjustment coefficients of the historical collection time intervals in the similar set to form a historical adjustment group;

[0039] When there is data in the history adjustment group that is greater than the adjustment coefficient of the collection time interval and there is data that is less than the adjustment coefficient of the collection time interval, the adjustment unit determines to modify the adjustment coefficient of the collection time interval;

[0040] When the data in the historical adjustment group are all smaller than or equal to the adjustment coefficient of the collection time interval or are all larger than or equal to the adjustment coefficient of the collection time interval, the adjustment unit determines not to adjust the adjustment coefficient of the collection time interval.

[0041] In some embodiments, when the adjustment unit determines to adjust the adjustment coefficient of the collection time interval,

[0042] The adjustment unit constructs data in the historical adjustment group that is greater than the adjustment coefficient of the inter-collection interval into a first correction set;

[0043] The adjustment unit constructs data of the adjustment coefficient in the historical adjustment group that is smaller than the acquisition time interval into a second correction set;

[0044] The adjustment unit adjusts the adjustment coefficient of the acquisition time interval according to the first correction set and the second correction set.

[0045] In some embodiments, the adjustment unit modifies the adjustment coefficient of the acquisition time interval according to the first correction set and the second correction set to satisfy the following formula:

[0046]

[0047] Among them, Kz is the adjustment coefficient of the corrected collection time interval, m is the number of adjustment coefficients of the historical collection time intervals in the first correction set, Ki is the adjustment coefficient of the i-th historical collection time interval in the first correction set, K0 is the adjustment coefficient of the collection time interval, n is the number of adjustment coefficients of the historical collection time intervals in the second correction set, and Kj is the adjustment coefficient of the j-th historical collection time interval in the second correction set.

[0048] In a second aspect, an intelligent pipeline wall thickness monitoring method is provided, comprising: collecting level data of each Hall sensor in all sensor arrays to obtain a first data set; after a collection time interval, collecting the level data of each Hall sensor again to obtain a second data set; the collection time interval is determined according to the operation data of the pipeline to be monitored;

[0049] Compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor queue in the first data set, and determine whether there is corrosion in the pipeline according to the comparison result;

[0050] When it is determined that the pipeline is corroded, the corrosion area is determined according to the level change, the adjustment coefficient of the collection time interval is determined according to the corrosion rate, and the collection time interval is adjusted based on the adjustment coefficient;

[0051] The corrosion area and the real-time flow velocity of the pipeline to be monitored are collected, a feature set is established according to the corrosion area and the real-time flow velocity, the feature set and the historical operation data of the pipeline to be monitored are clustered to determine a similar set, and according to the relationship between the historical time interval adjustment coefficient and the adjustment coefficient of the collection time interval in the similar set, it is determined whether to correct the adjustment coefficient of the collection time interval.

[0052] In a third aspect, an intelligent pipeline wall thickness monitoring device is provided, comprising:

[0053] A collection unit, used to collect level data of each Hall sensor in all sensor arrays to obtain a first data set; after a collection time interval, collect the level data of each Hall sensor again to obtain a second data set; the collection time interval is determined according to the operation data of the pipeline to be monitored;

[0054] A judging unit, used for comparing the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor queue in the first data set, and judging whether there is corrosion in the pipeline according to the comparison result;

[0055] A processing unit, configured to determine the corrosion area according to the level change when it is determined that the pipeline is corroded, determine the adjustment coefficient of the collection time interval according to the corrosion rate, and adjust the collection time interval based on the adjustment coefficient;

[0056] The adjustment unit is used to collect the corrosion area and the real-time flow velocity of the pipeline to be monitored, establish a feature set according to the corrosion area and the real-time flow velocity, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to correct the adjustment coefficient of the collection time interval according to the relationship between the historical time interval adjustment coefficient in the similar set and the adjustment coefficient of the collection time interval.

[0057] In a fourth aspect, an intelligent pipeline wall thickness monitoring device is provided, comprising a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory through a bus; when the intelligent pipeline wall thickness monitoring device is running, the processor executes the computer execution instructions stored in the memory, so that the intelligent pipeline wall thickness monitoring device executes the intelligent pipeline wall thickness monitoring method described in the first aspect.

[0058] The intelligent pipeline wall thickness monitoring device may be a network device, or a part of a network device, such as a chip system in the network device. The chip system is used to support the network device to implement the functions involved in the first aspect and any possible implementation thereof, such as obtaining, determining, and sending the data and / or information involved in the above-mentioned intelligent pipeline wall thickness monitoring method. The chip system includes a chip, and may also include other discrete devices or circuit structures.

[0059] In a fifth aspect, a computer-readable storage medium is provided, the computer-readable storage medium comprising computer execution instructions, and when the computer execution instructions are run on a computer, the computer executes the intelligent pipeline wall thickness monitoring method described in the first aspect.

[0060] In a sixth aspect, a computer program product is also provided, which includes computer instructions. When the computer instructions are executed on the intelligent pipeline wall thickness monitoring device, the intelligent pipeline wall thickness monitoring device executes the intelligent pipeline wall thickness monitoring method as described in the first aspect above.

[0061] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the intelligent pipeline wall thickness monitoring device, or may be packaged separately from the processor of the intelligent pipeline wall thickness monitoring device, which is not limited in the embodiments of the present application.

[0062] The descriptions of the second, third, fourth, fifth and sixth aspects of the present application may refer to the detailed description of the first aspect.

[0063] In the embodiments of the present application, the name of the intelligent pipeline wall thickness monitoring device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. For example, the receiving unit may also be called a receiving module, a receiver, etc. As long as the functions of each device or functional module are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalent technologies.

[0064] Compared with the prior art, the present application can obtain level data of different time periods through a sensor array surrounded and set on the pipeline to be monitored, and determine whether there is corrosion based on the comparison of level data of different time periods. The monitoring module can also determine the corrosion area based on the level change obtained after comparing the level data, and adjust the collection time interval according to the corrosion rate. For example, when the corrosion is slower, the collection frequency can be reduced to reduce the energy consumption of the equipment, and when the corrosion rate is faster, the collection frequency can be increased, so that the safety of the pipeline can be better monitored. In addition, the monitoring module can also determine whether to adjust the adjustment coefficient of the collection time interval based on the real-time flow rate and the corrosion area, so that the set collection time interval is more in line with the current pipeline corrosion situation, which can better meet the needs of users.

[0065] In summary, the sensor array and monitoring module can timely confirm whether there is corrosion in the pipeline. Compared with manual monitoring, it not only improves the efficiency of monitoring, but also improves the accuracy of monitoring, and can quickly identify corrosion risks. In addition, by adjusting the collection interval and the adjustment coefficient of the collection time interval, the monitoring frequency can be adjusted based on the actual operation of the pipeline, thereby improving the flexibility and accuracy of pipeline monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0067] Figure 1 A structural block diagram of an intelligent pipeline wall thickness monitoring system provided by an embodiment of the present invention;

[0068] Figure 2 A schematic diagram of the hardware structure of an intelligent pipeline wall thickness monitoring device provided by an embodiment of the present invention;

[0069] Figure 3 A flow chart of an intelligent pipeline wall thickness monitoring method provided by an embodiment of the present invention;

[0070] Figure 4 A schematic structural diagram of an intelligent pipeline wall thickness monitoring device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0072] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0073] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and order of execution.

[0074] As described in the background technology, manual detection is not only inefficient, but also prone to large errors. Moreover, under harsh environmental conditions, traditional monitoring equipment may fail or be damaged, affecting the continuity and accuracy of monitoring. In addition, traditional monitoring methods usually only collect data at specific time points and cannot perform dynamic feedback adjustment according to the corrosion status of the pipeline. The lack of feedback adjustment mechanism results in poor detection results.

[0075] In this regard, the present application provides an intelligent pipeline wall thickness monitoring system, including: a sensor array and a monitoring module, wherein a plurality of sensor arrays are provided, each sensor array is surrounded and arranged on the pipeline to be monitored, and each sensor array includes a plurality of Hall sensors. The monitoring module includes a collection unit, a judgment unit, a processing unit and an adjustment unit. Among them, the collection unit is configured to collect the level data of each Hall sensor in all sensor arrays to obtain a first data set; after a collection time interval, the level data of each Hall sensor is collected again to obtain a second data set; the collection time interval is determined by the collection unit according to the operation data of the pipeline to be monitored. The judgment unit is configured to compare the second data set with the first data set to determine whether there is corrosion in the pipeline to be monitored.

[0076] The processing unit is configured to determine the corrosion area according to the level change, determine the adjustment coefficient of the collection time interval according to the corrosion rate, and adjust the collection time interval according to the adjustment coefficient; the level change is the level data changed in the first data set and the second data set.

[0077] The adjustment unit is configured to collect the real-time flow velocity of the pipeline to be monitored with the corrosion area from the processing unit, establish a feature set according to the corrosion area and the real-time flow velocity, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to adjust the adjustment coefficient of the collection time interval according to the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval.

[0078] As can be seen from the above, the level data of different time periods can be obtained by surrounding the sensor array set on the pipeline to be monitored, and the presence of corrosion can be determined based on the comparison of the level data of different time periods. The monitoring module can also determine the corrosion area based on the level change obtained after comparing the level data, and adjust the collection time interval according to the corrosion rate. For example, when the corrosion is slow, the collection frequency can be reduced to reduce the energy consumption of the equipment, and when the corrosion rate is fast, the collection frequency can be increased, so as to better monitor the safety of the pipeline. In addition, the monitoring module can also determine whether to adjust the adjustment coefficient of the collection time interval based on the real-time flow rate and the corrosion area, so that the set collection time interval is more in line with the current pipeline corrosion situation, which can better meet the needs of users.

[0079] In summary, the sensor array and monitoring module can timely confirm whether there is corrosion in the pipeline. Compared with manual monitoring, it not only improves the efficiency of monitoring, but also improves the accuracy of monitoring, and can quickly identify corrosion risks. In addition, by adjusting the collection interval and the adjustment coefficient of the collection time interval, the monitoring frequency can be adjusted based on the actual operation of the pipeline, thereby improving the flexibility and accuracy of pipeline monitoring.

[0080] In addition, through the comprehensive use of sensor arrays and monitoring modules, real-time and continuous monitoring of pipeline status is achieved. The level data of the Hall sensor is automatically collected and compared to promptly determine whether the pipeline is at risk of corrosion, reducing the reliance on manual detection, labor costs and human errors. At the same time, the collection time interval is dynamically adjusted based on the pipeline operation data, making the monitoring process more flexible and efficient, and able to respond quickly to corrosion conditions. Through the analysis of the corrosion area and real-time flow rate, the monitoring strategy is further optimized to ensure the safety and reliability of the pipeline, effectively reducing potential safety hazards and economic losses. This application improves the efficiency and accuracy of pipeline monitoring, and improves its adaptability and intelligence level.

[0081] The embodiment of the present application provides an intelligent pipeline wall thickness monitoring system, such as Figure 1 As shown, the system includes: a sensor array 101 and a monitoring module 102 .

[0082] The sensor array 101 is in communication with the monitoring module 102. There are multiple sensor arrays 101, and each sensor array 101 includes multiple Hall sensors. The sensor array 101 is surrounded and arranged on the pipeline to be monitored. The monitoring module 102 includes a collection unit, a judgment unit, a processing unit and an adjustment unit.

[0083] Optionally, the sensor array 101 may be evenly arranged on the outer wall or inner wall of the pipeline to be monitored. The Hall sensor is an electronic component based on the Hall effect, which can convert a magnetic signal into an electrical signal, so the Hall sensor can collect level data.

[0084] Optionally, the Hall sensors in the sensor array 101 may be wirelessly connected to the monitoring module 102. The monitoring module 102 may send control instructions to each sensor in the sensor array via wireless signals.

[0085] The acquisition unit is configured to acquire the level data of each Hall sensor in all sensor arrays to establish a first data set. After an acquisition time interval, the acquisition unit acquires the level data of each Hall sensor again to obtain a second data set.

[0086] The collection time interval is determined by the collection unit according to the operation data of the pipeline to be monitored.

[0087] Specifically, multiple Hall sensors are evenly arranged in a sensor array around the pipeline to be monitored. Each sensor can independently collect information about the surrounding magnetic field. The sensor array regularly collects Hall voltage signals caused by the fluid in the pipeline and converts these signals into level data. During the data collection process, a first data set is formed. After a set collection time interval, the sensor array collects Hall voltage signals again to generate a second data set.

[0088] It should be pointed out that in natural gas or oil pipelines, the flow of fluids is often associated with changes in magnetic fields. For example, through the magnetic field generated by the fluid itself in the pipeline, when the fluid flows, it will drive the magnet to move or change the magnetic field distribution, thereby causing changes in the magnetic field in the environment where the Hall sensor is located. The Hall sensor can sense this magnetic field change and convert it into level data (such as high and low level states). By monitoring the changes in these level data, the operation of the pipeline to be monitored, such as flow rate, speed, etc., can be indirectly obtained. Therefore, the level data in the first data set can represent the operation of the pipeline to be monitored in the first acquisition time period, and the level data in the second data set can represent the operation of the pipeline to be monitored in the second acquisition time period. In addition, the difference in pipeline wall thickness will also cause different magnetic fields generated when the fluid passes, resulting in different acquired level data. The judgment unit is configured to compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and judge whether the pipeline to be monitored is corroded according to the comparison result.

[0089] Specifically, by comparing these two data sets, the change of the Hall voltage (i.e., level data) is determined. When the level data changes, it means that the pipeline has been corroded, resulting in a change in the pipeline wall thickness. When a ferromagnetic pipeline with the same nominal wall thickness enters the detection interval, a large number of flux lines will be concentrated inside the pipe wall. The uniform magnetic field on the surface of the pipeline passes vertically through the sensor, forming an initial level at its output end; when the thinning part of the pipeline wall enters the monitoring area, there is an area of ​​inner wall thickness loss and there is a magnetic field increment on the pipeline surface. The judgment unit in the monitoring module compares the two data sets to determine whether the pipeline is corroded.

[0090] In a possible implementation, since a small change in the level data is a normal phenomenon, when the amount of change in the level data in the first data set and the second data set is greater than the change amount threshold, it is determined that the pipeline is corroded, otherwise, it is determined that no corrosion exists. The processing unit is configured to determine the corrosion area according to the level change, determine the adjustment coefficient of the collection time interval according to the corrosion rate, and adjust the collection time interval according to the adjustment coefficient when the judgment unit determines that the pipeline to be monitored is corroded.

[0091] The level change is level data that changes in the first data set and the second data set.

[0092] Specifically, when determining whether the pipeline to be monitored is corroded, the processing unit can determine the corroded area of ​​the pipeline to be monitored based on the level data that has changed in the two data sets. After the acquisition time interval, if the area of ​​the pipeline to be monitored that has corroded is larger (or the degree of corrosion is deeper), the more level data that has changed. In this case, the processing unit can determine the corroded area of ​​the pipeline to be monitored based on the amount of level change.

[0093] In addition, since the corrosion area is the corrosion area increased in the pipeline to be monitored during the collection time interval, the ratio of the corrosion area to the collection time interval can be determined as the corrosion rate. The processing unit can collect the adjustment coefficient of the time interval according to the corrosion rate, and adjust the collection time interval according to the adjustment coefficient. For example, when the corrosion is slow, the collection frequency can be reduced to reduce the energy consumption of the equipment, and when the corrosion rate is fast, the collection frequency can be increased, so that the safety of the pipeline can be better monitored.

[0094] In a possible implementation, when the corrosion area is greater than a corrosion area threshold, a corrosion warning may be issued to prompt the staff to handle the pipeline to be monitored.

[0095] The adjustment unit is configured to collect the corrosion area from the processing unit and the real-time flow velocity of the pipeline to be monitored, establish a feature set based on the corrosion area and the real-time flow velocity, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to correct the adjustment coefficient of the collection time interval based on the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval.

[0096] Specifically, the processing unit can calculate the corrosion area according to the change of level data, and adjust the collection time interval through the analysis of corrosion rate to more effectively respond to the change of pipeline status. The adjustment unit can use historical data for cluster analysis to determine whether the collection time interval needs to be corrected, thereby realizing a dynamic feedback mechanism, so that the collection time interval can better adapt to the current corrosion situation and ensure the timeliness and accuracy of data collection.

[0097] In one possible implementation, since the real-time flow rate of the pipeline to be monitored has a great influence on the rate of corrosion, a feature set can be established based on the corrosion area and the real-time flow rate. Afterwards, the historical operating data can be used to perform cluster analysis with the feature set to group the data according to the similarity between the samples, for example, to help identify real-time flow rates with similar corrosion rates. If the adjustment coefficient of the historical collection time interval in the similar set is significantly different from the current setting, then the adjustment coefficient of the current collection time interval should be considered to be corrected. For example, if historical data shows that more frequent data collection is required during periods of high corrosion risk, and the current collection frequency is low, then the collection frequency should be increased to ensure that corrosion changes are captured in a timely manner.

[0098] It is understandable that by integrating real-time data acquisition with a dynamic feedback mechanism, comprehensive and continuous monitoring of pipeline status is achieved. Rapid identification of corrosion risks is achieved, and the monitoring frequency is intelligently adjusted according to the actual operating conditions of the pipeline, which improves the flexibility and accuracy of monitoring. By deeply analyzing the corrosion area and real-time flow rate, the system can take effective measures before potential risks occur, reducing safety hazards and economic losses.

[0099] In some embodiments of the present application, the collection unit determines that the collection time interval satisfies the following formula according to the operation data of the pipeline to be monitored:

[0100]

[0101] Wherein, Tc represents the collection time interval, D represents the pipe diameter, ρ represents the density of the fluid in the pipe, Ac represents the cross-sectional area of ​​the pipe, Fs represents the sensitivity of the Hall sensor, β represents the temperature influence coefficient, β takes a value of 0.01-0.05, and Tavg represents the average temperature of the fluid in the pipe to be monitored.

[0102] Specifically, the pipe diameter and cross-sectional area directly affect the flow rate, which interacts with the physical properties of the fluid (such as density) to affect changes in the corrosion rate. The sensitivity of the Hall sensor determines its ability to capture changes, while the temperature influence coefficient takes into account the dynamic changes of environmental factors on the corrosion process. By combining these factors, a reasonable collection time interval is determined, so that the data collection frequency can be reasonably adjusted to adapt to the actual state of the pipeline under different working conditions. For example, different fluids have different corrosive abilities, so the collection time intervals corresponding to different fluids are also different. For another example, different temperatures correspond to different corrosion rates, so the collection time intervals corresponding to different temperatures are also different.

[0103] It is understandable that by incorporating multiple parameters into the calculation of the acquisition time interval, dynamic adaptive monitoring of the pipeline status is achieved. Compared with the traditional fixed time interval acquisition method, it can more effectively respond to changes in the fluid and environmental conditions in the pipeline, ensuring that data changes are captured in time at the early stage of corrosion. It not only improves the flexibility and accuracy of monitoring, but also reduces potential safety hazards.

[0104] In some embodiments of the present application, the judgment unit judges whether the pipeline is corroded according to the comparison result, including: when any level data in the second data set changes, the judgment unit judges that the pipeline to be monitored is corroded. When the level data in the second data set are all the same as those in the first data set, the judgment unit judges that the pipeline to be monitored is not corroded.

[0105] In some embodiments of the present application, the processing unit determines the corrosion area according to the level change, including: the processing unit determines the corrosion area according to the number of Hall sensors corresponding to the change position of the level data, and the corrosion area is proportional to the number of Hall sensors.

[0106] Specifically, when the sensor array determines the corrosion area based on the level change, the Hall sensor corresponding to the changed level data can be determined by comparing the first data set with the second data set, and the location of the Hall sensor is the location where the level data changes, that is, the location where the corrosion occurs. Afterwards, the corrosion area can be determined based on the location where the corrosion occurs and the number of these Hall sensors, and it can be determined which location has corroded. In this way, when the corrosion area is large, the staff can be prompted to deal with the corrosion at that location in a timely manner, thereby avoiding safety hazards.

[0107] In some embodiments of the present application, the processing unit determines an adjustment coefficient of the collection time interval based on the corrosion rate, and adjusts the collection time interval based on the adjustment coefficient, including: the processing unit determines the corrosion rate based on the corrosion area and the collection time interval, determines the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval to which the corrosion rate belongs, and adjusts the collection time interval based on the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval.

[0108] In a possible implementation, when the corrosion rate is less than or equal to a first preset corrosion rate, the processing unit determines that the corrosion rate belongs to a first corrosion rate interval, determines a first preset adjustment coefficient corresponding to the first preset corrosion rate interval, and adjusts the collection time interval based on the first preset adjustment coefficient;

[0109] When the corrosion rate is greater than the first preset corrosion rate and less than or equal to the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to the second corrosion rate interval, determines a second preset adjustment coefficient corresponding to the second corrosion rate interval, and adjusts the collection time interval based on the second preset adjustment coefficient;

[0110] When the corrosion rate is greater than the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to a third corrosion rate interval, determines a third preset adjustment coefficient corresponding to the third corrosion rate interval, and adjusts the collection time interval based on the third preset adjustment coefficient;

[0111] The first corrosion rate is less than the second corrosion rate, the first preset adjustment coefficient is greater than the second preset adjustment coefficient, the second preset adjustment coefficient is greater than the third preset adjustment coefficient, and the third preset adjustment coefficient is greater than 0.

[0112] Optionally, the first preset corrosion rate, the second preset corrosion rate, the first preset adjustment coefficient, the second preset adjustment coefficient, and the third preset adjustment coefficient are all preset in advance. In addition, these values ​​can be determined based on historical operating data of the pipeline to be monitored, so that the preset data can better meet the operating conditions of the pipeline.

[0113] Specifically, the processing unit can compare the corrosion rate with the first preset corrosion rate and the second preset corrosion rate respectively, determine the adjustment coefficient of the collection time interval (e.g., the first preset adjustment coefficient, the second preset adjustment coefficient, and the third preset adjustment coefficient) according to the comparison result, and adjust the collection time interval. For example, when the corrosion rate is fast, the current collection time interval may be too long, which may cause safety hazards. Therefore, the adjustment coefficient of the collection time interval corresponding to the current corrosion rate can be determined, and the collection time interval can be adjusted to shorten the collection time interval, increase the collection frequency, and reduce the risk of accidents. It can be understood that the judgment unit judges whether the pipeline is corroded by comparing the level data in the first data set and the second data set. If any level data in the second data set changes, it is determined that the pipeline is corroded; otherwise, it is considered that the pipeline is not corroded. The processing unit determines the corrosion area according to the number of Hall sensors corresponding to the level change, and the two are proportional. That is, the more sensors with data changes, the larger the corrosion area. The processing unit compares the actual corrosion rate with multiple sets of preset corrosion rates to determine the corresponding adjustment coefficient of the collection time interval. According to different corrosion rate ranges, the adjustment coefficient of the collection time interval is a different preset value to ensure timely response to corrosion progress.

[0114] It is understandable that by comparing the level data in real time, it is possible to quickly respond to changes in the pipeline status. The data-based judgment method reduces manual intervention and improves the accuracy and real-time nature of the judgment. The corrosion area is proportional to the number of sensors, ensuring that the actual corrosion situation of the pipeline can be more comprehensively reflected. By setting different adjustment coefficients for corrosion rate and acquisition time interval, it can adapt to the actual situation of the pipeline and ensure effective monitoring under different corrosion conditions.

[0115] In some embodiments of the present application, the adjustment unit performs cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, including:

[0116] S1: The adjustment unit initializes K centroids in the historical running data, assigns feature sets to the nearest centroids, and forms K clusters.

[0117] S2: The adjustment unit recalculates the centroid of each cluster.

[0118] S3: The adjustment unit repeats S1 and S2 until the center of mass no longer changes or the preset number of repetitions is completed.

[0119] S4: The adjustment unit constructs the remaining historical running data in the cluster where the feature set is located into a similar set.

[0120] The centroid calculation formula is as follows:

[0121]

[0122] Among them, Zk represents the centroid of the kth cluster, |Ck| represents the number of data in the kth cluster, and Ti represents the feature vector of the i-th data.

[0123] Specifically, the historical operation data includes the historical feature set collected during historical monitoring, and the adjustment coefficient of the historical collection time interval corresponding to the historical feature set. The historical data is a database that is gradually accumulated as the intelligent pipeline wall thickness monitoring system is used, and is used to quickly judge and correct the rationality of the time interval adjustment coefficient. Among them, K-means clustering is an unsupervised learning method that can identify the natural structure between data by dividing the data. The continuous adjustment of the centroid ensures the accuracy of the clustering, and the allocation of data points allows similar features to be concentrated for further analysis. The feature vector of each piece of data is multidimensional data that describes its operating status. By calculating the centroid, typical features of similar operating states can be extracted, thereby providing strong support for pipeline monitoring.

[0124] It is understandable that through cluster analysis, the adjustment unit can effectively identify similar states of pipelines in historical operation data and form accurate similar sets. This allows the system to not only dynamically adapt to current monitoring needs, but also make more accurate risk assessments and decisions based on historical data. Through in-depth analysis of pipeline operation status, more efficient resource allocation and safety management can be achieved.

[0125] In some embodiments of the present application, the adjustment unit determines whether to adjust the adjustment coefficient of the collection time interval based on the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval, including: the adjustment unit extracts the adjustment coefficients of the historical collection time intervals in the similar set to form a historical adjustment group.

[0126] When there is data in the historical adjustment group that is greater than the adjustment coefficient of the collection time interval and there is data that is less than the adjustment coefficient of the collection time interval at the same time, the adjustment unit determines to adjust the adjustment coefficient of the collection time interval. When the data in the historical adjustment group are all less than or equal to the adjustment coefficient of the collection time interval or are all greater than or equal to the adjustment coefficient of the collection time interval, the adjustment unit determines not to adjust the adjustment coefficient of the collection time interval.

[0127] Specifically, when there is an adjustment coefficient in the historical adjustment group that is both greater than and less than the current collection time interval, it indicates that there are different corrosion rates or environmental changes in the historical data. In this case, it may be necessary to re-evaluate the current collection frequency to ensure that rapidly changing situations can be captured while avoiding waste of resources due to overly frequent collection. When all the data in the historical adjustment group are less than or equal to, or all are greater than or equal to the adjustment coefficient of the current collection time interval, it indicates that the historical collection frequency is relatively stable, and the corrosion rate or other influencing factors do not fluctuate much within a certain range. At this point, it can be considered that the current collection frequency is reasonable and does not need to be corrected.

[0128] It can be understood that the adjustment unit extracts the historical time interval adjustment coefficients from the similar set to form a data set called "historical adjustment group". These data represent the historical adjustment situations under similar conditions. When there are data greater than and less than the current time interval adjustment coefficient in the historical adjustment group, the adjustment unit will determine that the current time interval adjustment coefficient needs to be corrected. This judgment indicates that there have been different adjustment experiences in history, indicating that re-evaluation may be required at present. If all data in the historical adjustment group are less than or equal to the current time interval adjustment coefficient, or are greater than or equal to the coefficient, the adjustment unit will determine not to make corrections. This means that the current settings may still be applicable and no changes are required. By analyzing historical data, the adjustment unit can make more reasonable decisions based on past operating conditions. The composition and conditional judgment of the historical adjustment group provides a data-driven decision-making basis for the system, which helps to reduce the interference of human subjective factors. By observing the distribution of data in the historical adjustment group, the suitability of the current time interval adjustment coefficient can be judged, thereby realizing a more flexible monitoring strategy.

[0129] It is understandable that by introducing comparative analysis of historical time interval adjustment coefficients, the adjustment unit can achieve more intelligent adaptive adjustment, which not only improves the flexibility of monitoring, but also ensures the effectiveness of adjustment measures, so that in the face of complex and changing pipeline operation environments, it can respond to potential risks more quickly and accurately, and ensure the safety and stable operation of the pipeline.

[0130] In some embodiments of the present application, when the adjustment unit determines to modify the adjustment coefficient of the acquisition time interval, the adjustment unit constructs the data of the adjustment coefficient greater than the acquisition time interval in the historical adjustment group as a first correction set. The adjustment unit constructs the data of the adjustment coefficient less than the acquisition interval in the historical adjustment group as a second correction set.

[0131] The adjustment coefficient of the acquisition time interval is corrected according to the first correction set and the second correction set.

[0132] The adjustment unit corrects the adjustment coefficient of the collection time interval according to the first correction set and the second correction set to satisfy the following formula:

[0133]

[0134] Among them, Kz is the adjustment coefficient of the corrected collection time interval, m is the number of adjustment coefficients of the historical collection time intervals in the first correction set, Ki is the adjustment coefficient of the i-th historical collection time interval in the first correction set, K0 is the adjustment coefficient of the collection time interval, n is the number of adjustment coefficients of the historical collection time intervals in the second correction set, and Kj is the adjustment coefficient of the j-th historical collection time interval in the second correction set.

[0135] Specifically, when there are adjustment coefficients greater than and less than the current collection time interval in the historical adjustment group, it means that the corrosion rate or environmental conditions have fluctuated greatly in the past. Therefore, it is necessary to adjust the adjustment coefficient in time so that the collection frequency can be applied to the current changing scene and improve the adaptability and flexibility of the system. The adjustment unit needs to correct the adjustment coefficient of the collection time interval. This dynamic adjustment strategy helps to optimize the monitoring effect and reduce risks.

[0136] It is understandable that the adjustment unit constructs all data in the historical adjustment group that are greater than the current time interval adjustment coefficient into a first correction set. These data indicate adjustment experience under similar conditions. Similarly, the adjustment unit constructs all data in the historical adjustment group that are less than the current time interval adjustment coefficient into a second correction set, reflecting adjustment data that may be more suitable for current conditions. Correction is performed according to the data in the first correction set and the second correction set. By calculating based on these two sets of data, a new time interval adjustment coefficient is obtained, thereby optimizing the monitoring strategy. By calculating the new coefficient, it can quickly adapt to the changing pipeline state to ensure that the data collection frequency is consistent with actual needs. Based on the learning of historical data, an adaptive monitoring mechanism is formed. By dividing the historical adjustment data into two categories, the adjustment unit can make targeted corrections to ensure that the time interval adjustment coefficient is more in line with the current operating state of the pipeline. It improves the scientificity and flexibility of monitoring, and can quickly respond to changes in the internal and external environment of the pipeline, thereby effectively reducing potential safety risks.

[0137] In the above embodiments, by comprehensively using the sensor array and the monitoring module, real-time and continuous monitoring of the pipeline status is achieved. The level data of the Hall sensor is automatically collected and compared to promptly determine whether the pipeline has a corrosion risk, which reduces the dependence on manual detection, and reduces labor costs and human errors. At the same time, the collection time interval is dynamically adjusted based on the pipeline operation data, making the monitoring process more flexible and efficient, and able to respond quickly to corrosion conditions. By analyzing the corrosion area and real-time flow rate, the monitoring strategy is further optimized to ensure the safety and reliability of the pipeline, effectively reducing potential safety hazards and economic losses. This application improves the efficiency and accuracy of pipeline monitoring, and improves its adaptability and intelligence level.

[0138] The basic hardware structure of the monitoring module 102 includes Figure 2 The components of the intelligent pipeline wall thickness monitoring device shown in FIG. Figure 2 Taking the intelligent pipeline wall thickness monitoring device shown as an example, the hardware structure of the monitoring module 102 is introduced.

[0139] like Figure 2, which is a hardware structure diagram of an intelligent pipeline wall thickness monitoring device provided in an embodiment of the present application. The intelligent pipeline wall thickness monitoring device includes a processor 21, a memory 22, a communication interface 23, and a bus 24. The processor 21, the memory 22, and the communication interface 23 can be connected through a bus 24.

[0140] The processor 21 is the control center of the intelligent pipeline wall thickness monitoring device, which can be a processor or a general term for multiple processing elements. For example, the processor 21 can be a general central processing unit (CPU) or other general processors. Among them, the general processor can be a microprocessor or any conventional processor.

[0141] As an embodiment, the processor 21 may include one or more CPUs, for example Figure 2 CPU 0 and CPU 1 are shown in .

[0142] The memory 22 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0143] In a possible implementation, the memory 22 may exist independently of the processor 21, and the memory 22 may be connected to the processor 21 via a bus 24 to store instructions or program codes. When the processor 21 calls and executes the instructions or program codes stored in the memory 22, the intelligent pipeline wall thickness monitoring method provided in the following embodiments of the present application can be implemented.

[0144] In the embodiment of the present application, for the monitoring module 102, the software programs stored in the memory 22 are different, so the functions implemented by the monitoring module 102 are different. The functions performed by each device will be described in conjunction with the following flowchart.

[0145] In another possible implementation, the memory 22 may also be integrated with the processor 21 .

[0146] The communication interface 23 is used to connect the intelligent pipeline wall thickness monitoring device to other devices through a communication network, and the communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc. The communication interface 23 can include a receiving unit for receiving data and a sending unit for sending data.

[0147] The bus 24 may be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0148] It should be pointed out that Figure 2 The structure shown in the figure does not constitute a limitation on the intelligent pipeline wall thickness monitoring device. Figure 2 In addition to the components shown, the intelligent pipeline wall thickness monitoring device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0149] The intelligent pipeline wall thickness monitoring method provided in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0150] The intelligent pipeline wall thickness monitoring method provided in the embodiment of the present application is applied to Figure 1 The monitoring module 102 in the intelligent pipeline wall thickness monitoring system shown in FIG. Figure 3 As shown, the intelligent pipeline wall thickness monitoring method includes:

[0151] S301: collecting level data of each Hall sensor in all sensor arrays to obtain a first data set; after a collection time interval, collecting level data of each Hall sensor again to obtain a second data set.

[0152] Wherein, the collection time interval is determined according to the operation data of the pipeline to be monitored;

[0153] S302: Compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and determine whether the pipeline is corroded according to the comparison result.

[0154] S303: When it is determined that the pipeline is corroded, the corrosion area is determined according to the level change, and the adjustment coefficient of the collection time interval is determined according to the corrosion rate, and the collection time interval is adjusted based on the adjustment coefficient.

[0155] S304: Collect the corrosion area and the real-time flow velocity of the pipeline to be monitored, establish a feature set based on the corrosion area and the real-time flow velocity, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to adjust the adjustment coefficient of the collection time interval based on the relationship between the historical time interval adjustment coefficient and the collection time interval adjustment coefficient in the similar set.

[0156] Specifically, the level data of different time periods can be obtained by surrounding the sensor array set on the pipeline to be monitored, and the presence of corrosion can be determined based on the comparison of the level data of different time periods. The monitoring module can also determine the corrosion area based on the level change obtained after comparing the level data, and adjust the collection time interval according to the corrosion rate. For example, when the corrosion is slow, the collection frequency can be reduced to reduce the energy consumption of the equipment, and when the corrosion rate is fast, the collection frequency can be increased, so as to better monitor the safety of the pipeline. In addition, the monitoring module can also determine whether to adjust the adjustment coefficient of the collection time interval based on the real-time flow rate and the corrosion area, so that the set collection time interval is more in line with the current pipeline corrosion situation, which can better meet the needs of users.

[0157] It is understandable that through the comprehensive use of sensor arrays and monitoring modules, real-time and continuous monitoring of pipeline status is achieved. Automatically collect and compare the level data of the Hall sensor to promptly determine whether the pipeline is at risk of corrosion, reduce dependence on manual detection, and reduce labor costs and human errors. At the same time, dynamically adjust the collection time interval based on pipeline operation data, making the monitoring process more flexible and efficient, and able to respond quickly to corrosion conditions. Through the analysis of the corrosion area and real-time flow rate, the monitoring strategy is further optimized to ensure the safety and reliability of the pipeline, effectively reducing potential safety hazards and economic losses. This application improves the efficiency and accuracy of pipeline monitoring, and improves its adaptability and intelligence level.

[0158] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0159] The embodiment of the present application can divide the functional modules of the intelligent pipeline wall thickness monitoring device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0160] like Figure 4 , which is a schematic diagram of the structure of an intelligent pipeline wall thickness monitoring device provided in an embodiment of the present application. Figure 4 The intelligent pipeline wall thickness monitoring device shown includes: a collection unit 401, a judgment unit 402, a processing unit 403 and an adjustment unit 404;

[0161] The acquisition unit 401 is used to acquire the level data of each Hall sensor in all sensor arrays to obtain a first data set; after an acquisition time interval, the level data of each Hall sensor is acquired again to obtain a second data set. The acquisition time interval is determined according to the operation data of the pipeline to be monitored;

[0162] The judgment unit 402 is used to compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and judge whether the pipeline is corroded according to the comparison result.

[0163] The processing unit 403 is used to determine the corrosion area according to the level change when it is determined that the pipeline is corroded, determine the adjustment coefficient of the collection time interval according to the corrosion rate, and adjust the collection time interval based on the adjustment coefficient.

[0164] The adjustment unit 404 is used to collect the corrosion area and the real-time flow velocity of the pipeline to be monitored, establish a feature set based on the corrosion area and the real-time flow velocity, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to correct the adjustment coefficient of the collection time interval based on the relationship between the historical time interval adjustment coefficient and the collection time interval adjustment coefficient in the similar set.

[0165] Optionally, the collection unit 401 is specifically configured to:

[0166] According to the operation data of the pipeline to be monitored, the collection time interval is determined to meet the following formula:

[0167]

[0168] Wherein, Tc represents the collection time interval, D represents the pipe diameter, ρ represents the density of the fluid in the pipe, Ac represents the cross-sectional area of ​​the pipe, Fs represents the sensitivity of the Hall sensor, β represents the temperature influence coefficient, β takes a value of 0.01-0.05, and Tavg represents the average temperature of the fluid in the pipe to be monitored.

[0169] Optionally, the determining unit 402 is specifically configured to:

[0170] When any level data in the second data set changes, the judgment unit determines that corrosion exists in the pipeline to be monitored;

[0171] When the level data in the second data set are all the same as those in the first data set, the judgment unit determines that there is no corrosion in the pipeline to be monitored.

[0172] Optionally, the processing unit 403 is specifically configured to:

[0173] The corrosion area is determined according to the number of Hall sensors corresponding to the change position of the level data, and the corrosion area is proportional to the number of Hall sensors.

[0174] Optionally, the processing unit 403 is specifically configured to:

[0175] The processing unit determines the corrosion rate according to the corrosion area and the collection time interval, determines the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval to which the corrosion rate belongs, and adjusts the collection time interval based on the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval.

[0176] Optionally, the processing unit 403 is specifically configured to:

[0177] When the corrosion rate is less than or equal to the first preset corrosion rate, the processing unit determines that the corrosion rate belongs to the first corrosion rate interval, determines a first preset adjustment coefficient corresponding to the first preset corrosion rate interval, and adjusts the collection time interval based on the first preset adjustment coefficient;

[0178] When the corrosion rate is greater than the first preset corrosion rate and less than or equal to the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to the second corrosion rate interval, determines a second preset adjustment coefficient corresponding to the second corrosion rate interval, and adjusts the collection time interval based on the second preset adjustment coefficient;

[0179] When the corrosion rate is greater than the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to a third corrosion rate interval, determines a third preset adjustment coefficient corresponding to the third corrosion rate interval, and adjusts the collection time interval based on the third preset adjustment coefficient;

[0180] The first corrosion rate is less than the second corrosion rate, the first preset adjustment coefficient is greater than the second preset adjustment coefficient, the second preset adjustment coefficient is greater than the third preset adjustment coefficient, and the third preset adjustment coefficient is greater than 0.

[0181] Optionally, the adjusting unit 404 is specifically configured to:

[0182] S1: The adjustment unit initializes K centroids in the historical running data, assigns feature sets to the nearest centroids, and forms K clusters;

[0183] S2: The adjustment unit recalculates the centroid of each cluster;

[0184] S3: The adjustment unit repeats S1 and S2 until the center of mass no longer changes or the preset number of repetitions is completed;

[0185] S4: The adjustment unit constructs the remaining historical operation data in the cluster where the feature set is located into a similar set;

[0186] The centroid calculation formula is as follows:

[0187]

[0188] Among them, Zk represents the centroid of the kth cluster, |Ck| represents the number of data in the kth cluster, and Ti represents the feature vector of the i-th data.

[0189] Optionally, the adjusting unit 404 is specifically configured to:

[0190] The adjustment unit extracts the adjustment coefficients of the historical collection time intervals in the similar set to form a historical adjustment group;

[0191] When there is data in the historical adjustment group that is greater than the adjustment coefficient of the collection time interval and there is data that is less than the adjustment coefficient of the collection time interval at the same time, the adjustment unit determines to modify the adjustment coefficient of the collection time interval;

[0192] When the data in the historical adjustment group are all smaller than or equal to the adjustment coefficient of the collection time interval or are all larger than or equal to the adjustment coefficient of the collection time interval, the adjustment unit determines not to adjust the adjustment coefficient of the collection time interval.

[0193] Optionally, the adjusting unit 404 is specifically configured to:

[0194] The adjustment unit constructs the data of the adjustment coefficient greater than the interval between collections in the historical adjustment group into a first correction set;

[0195] The adjustment unit constructs the data of the adjustment coefficient in the historical adjustment group that is smaller than the collection time interval into a second correction set;

[0196] The adjustment unit adjusts the adjustment coefficient of the collection time interval according to the first correction set and the second correction set.

[0197] Optionally, the adjusting unit 404 is specifically configured to:

[0198] The adjustment coefficient of the acquisition time interval is corrected according to the first correction set and the second correction set to satisfy the following formula:

[0199]

[0200] Among them, Kz is the adjustment coefficient of the corrected collection time interval, m is the number of adjustment coefficients of the historical collection time intervals in the first correction set, Ki is the adjustment coefficient of the i-th historical collection time interval in the first correction set, K0 is the adjustment coefficient of the collection time interval, n is the number of adjustment coefficients of the historical collection time intervals in the second correction set, and Kj is the adjustment coefficient of the j-th historical collection time interval in the second correction set.

[0201] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0202] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0203] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent pipeline wall thickness monitoring system, characterized in that: include: A sensor array and a monitoring module, wherein the monitoring module is connected to the sensor array; There are a plurality of sensor arrays, each of which is surrounded and arranged on the pipeline to be monitored, and each of which includes a plurality of Hall sensors; The monitoring module includes a collection unit, a judgment unit, a processing unit and an adjustment unit; The acquisition unit is configured to acquire level data of each Hall sensor in all the sensor arrays to obtain a first data set; after an acquisition time interval, the level data of each Hall sensor is acquired again to obtain a second data set; the acquisition time interval is determined by the acquisition unit according to the operation data of the pipeline to be monitored; The judgment unit is configured to compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor array in the first data set, and judge whether the pipeline to be monitored has corrosion according to the comparison result; The processing unit is configured to determine the corrosion area according to the level change, determine the adjustment coefficient of the collection time interval according to the corrosion rate, and adjust the collection time interval according to the adjustment coefficient when the judgment unit determines that the pipeline to be monitored has corrosion; the level change is the level data changed in the first data set and the second data set; The adjustment unit is configured to collect the corrosion area and the real-time flow rate of the pipeline to be monitored from the processing unit, establish a feature set based on the corrosion area and the real-time flow rate, perform cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, and determine whether to adjust the adjustment coefficient of the collection time interval based on the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval.

2. The intelligent pipeline wall thickness monitoring system according to claim 1 is characterized in that: The collection unit determines that the collection time interval satisfies the following formula according to the operation data of the pipeline to be monitored: Wherein, Tc represents the collection time interval, D represents the pipe diameter, ρ represents the density of the fluid in the pipe, Ac represents the cross-sectional area of ​​the pipe, Fs represents the sensitivity of the Hall sensor, β represents the temperature influence coefficient, β takes a value of 0.01-0.05, and Tavg represents the average temperature of the fluid in the pipe to be monitored.

3. The intelligent pipeline wall thickness monitoring system according to claim 1 is characterized in that: The judging unit judges whether the pipeline is corroded according to the comparison result, including: When any level data in the second data set changes, the judgment unit determines that corrosion exists in the pipeline to be monitored; When the level data in the second data set are all the same as those in the first data set, the judgment unit determines that there is no corrosion in the pipeline to be monitored.

4. The intelligent pipeline wall thickness monitoring system according to claim 1 is characterized in that: The processing unit determines the corrosion area according to the level change, including: The processing unit determines the corrosion area according to the number of Hall sensors corresponding to the change position of the level data, and the corrosion area is proportional to the number of the Hall sensors.

5. The intelligent pipeline wall thickness monitoring system according to claim 4 is characterized in that: The processing unit determines the adjustment coefficient of the collection time interval according to the corrosion rate, and adjusts the collection time interval according to the adjustment coefficient, including: The processing unit determines the corrosion rate according to the corrosion area and the collection time interval, determines an adjustment coefficient of the collection time interval corresponding to the corrosion rate interval to which the corrosion rate belongs, and adjusts the collection time interval based on the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval.

6. The intelligent pipeline wall thickness monitoring system according to claim 5 is characterized in that: The processing unit determines an adjustment coefficient of a collection time interval corresponding to a corrosion rate interval to which the corrosion rate belongs, and adjusts the collection time interval based on the adjustment coefficient of the collection time interval corresponding to the corrosion rate interval, including: When the corrosion rate is less than or equal to a first preset corrosion rate, the processing unit determines that the corrosion rate belongs to a first corrosion rate interval, determines a first preset adjustment coefficient corresponding to the first preset corrosion rate interval, and adjusts the collection time interval based on the first preset adjustment coefficient; When the corrosion rate is greater than the first preset corrosion rate and less than or equal to the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to a second corrosion rate interval, determines a second preset adjustment coefficient corresponding to the second corrosion rate interval, and adjusts the collection time interval based on the second preset adjustment coefficient; When the corrosion rate is greater than the second preset corrosion rate, the processing unit determines that the corrosion rate belongs to a third corrosion rate interval, determines a third preset adjustment coefficient corresponding to the third corrosion rate interval, and adjusts the collection time interval based on the third preset adjustment coefficient; Among them, the first corrosion rate is less than the second corrosion rate, the first preset adjustment coefficient is greater than the second preset adjustment coefficient, the second preset adjustment coefficient is greater than the third preset adjustment coefficient, and the third preset adjustment coefficient is greater than 0.

7. The intelligent pipeline wall thickness monitoring system according to claim 1 is characterized in that: The adjusting unit performs cluster analysis on the feature set and the historical operation data of the pipeline to be monitored to determine a similar set, including: S1: The adjustment unit initializes K centroids in the historical operation data, and assigns the feature set to the nearest centroid to form K clusters; S2: the adjustment unit recalculates the centroid of each cluster; S3: the adjustment unit repeats S1 and S2 until the center of mass no longer changes or a preset number of times are completed; S4: the adjustment unit constructs the remaining historical operation data in the cluster where the feature set is located into a similar set; The centroid calculation formula is as follows: Among them, Zk represents the centroid of the kth cluster, |Ck| represents the number of data in the kth cluster, and Ti represents the feature vector of the i-th data.

8. The intelligent pipeline wall thickness monitoring system according to claim 7 is characterized in that: The adjusting unit determines whether to adjust the adjustment coefficient of the collection time interval according to the relationship between the adjustment coefficient of the historical collection time interval in the similar set and the adjustment coefficient of the collection time interval, including: The adjustment unit extracts the adjustment coefficients of the historical collection time intervals in the similar set to form a historical adjustment group; When there is data in the history adjustment group that is greater than the adjustment coefficient of the collection time interval and there is data that is less than the adjustment coefficient of the collection time interval, the adjustment unit determines to modify the adjustment coefficient of the collection time interval; When the data in the historical adjustment group are all smaller than or equal to the adjustment coefficient of the collection time interval or are all larger than or equal to the adjustment coefficient of the collection time interval, the adjustment unit determines not to adjust the adjustment coefficient of the collection time interval.

9. The intelligent pipeline wall thickness monitoring system according to claim 8, characterized in that: When the adjustment unit determines to adjust the adjustment coefficient of the acquisition time interval, The adjustment unit constructs data in the historical adjustment group that is greater than the adjustment coefficient of the inter-collection interval into a first correction set; The adjustment unit constructs data of the adjustment coefficient in the historical adjustment group that is smaller than the acquisition time interval into a second correction set; The adjustment unit adjusts the adjustment coefficient of the acquisition time interval according to the first correction set and the second correction set.

10. The intelligent pipeline wall thickness monitoring system according to claim 7, characterized in that: The adjustment unit modifies the adjustment coefficient of the collection time interval according to the first correction set and the second correction set to satisfy the following formula: Among them, Kz is the adjustment coefficient of the corrected collection time interval, m is the number of adjustment coefficients of the historical collection time intervals in the first correction set, Ki is the adjustment coefficient of the i-th historical collection time interval in the first correction set, K0 is the adjustment coefficient of the collection time interval, n is the number of adjustment coefficients of the historical collection time intervals in the second correction set, and Kj is the adjustment coefficient of the j-th historical collection time interval in the second correction set.

11. An intelligent pipeline wall thickness monitoring method, applied to the intelligent pipeline wall thickness monitoring system according to any one of claims 1 to 10, characterized in that: include: Collecting level data of each Hall sensor in all sensor arrays to obtain a first data set; After a collection time interval, the level data of each Hall sensor is collected again to obtain a second data set; the collection time interval is determined according to the operation data of the pipeline to be monitored; Compare the level data corresponding to each sensor array in the second data set with the level data corresponding to each sensor queue in the first data set, and determine whether there is corrosion in the pipeline according to the comparison result; When it is determined that the pipeline is corroded, the corrosion area is determined according to the level change, the adjustment coefficient of the collection time interval is determined according to the corrosion rate, and the collection time interval is adjusted based on the adjustment coefficient; The corrosion area and the real-time flow velocity of the pipeline to be monitored are collected, a feature set is established according to the corrosion area and the real-time flow velocity, the feature set and the historical operation data of the pipeline to be monitored are clustered to determine a similar set, and according to the relationship between the historical time interval adjustment coefficient and the adjustment coefficient of the collection time interval in the similar set, it is determined whether to correct the adjustment coefficient of the collection time interval.

12. An intelligent pipeline wall thickness monitoring device, characterized in that: include: A processor and a memory; wherein the memory is used to store one or more programs, and the one or more programs include computer-executable instructions. When the device is running, the processor executes the computer-executable instructions stored in the memory to enable the device to execute the system described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is capable of executing the system according to any one of claims 1 to 10.

14. A computer program product, characterized in that The computer program product comprises: a computer program or instructions, and when the computer program or instructions are run on a computer, the computer is caused to execute the system according to any one of claims 1 to 10.

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