Pipeline drying treatment method and internet of things system based on intelligent gas safety supervision

Abstract

The application provides a pipeline drying treatment method and an Internet of Things system based on intelligent gas safety supervision, which comprises a gas company management platform, a government gas supervision management platform, a gas company object platform and the like, and the method comprises the following steps: acquiring drying medium information of at least one gas pipeline outlet; determining an initial drying value of the gas pipeline based on the drying medium information; in response to the initial drying value being lower than a drying threshold value, taking the gas pipeline as a target gas pipeline, determining advanced drying parameters based on the initial drying value of the target gas pipeline; generating a first control instruction based on the advanced drying parameters and sending the first control instruction to the gas company object platform, and performing advanced drying on the target gas pipeline; evaluating the confidence of the initial drying value based on detection data during the advanced drying; generating a threshold value adjustment instruction based on the confidence of the initial drying value, and updating the drying threshold value; and generating a performance adjustment instruction based on the confidence of the initial drying value. The application can improve the cleaning quality and effect of the gas pipeline.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline drying technology, and in particular to a pipeline drying treatment method and Internet of Things system based on intelligent gas safety monitoring. Background Technology

[0002] After gas pipelines are put into use, impurities accumulate inside. To ensure normal gas supply, regular or irregular cleaning and drying of the pipelines is necessary. Ordinary gas pipeline cleaning devices are complex to operate and inefficient, making it difficult to quickly clean and effectively dry the inside of the pipelines. Therefore, simplifying the operation and quickly and efficiently cleaning and drying the inside of gas pipelines is an urgent problem to be solved.

[0003] To address the issue of complex operation of gas pipeline cleaning devices, CN112692011A proposes a gas pipeline cleaning and maintenance device. This device features an optimized overall structural design, enabling rapid cleaning of internal pipe debris and simplifying operation. However, it still fails to resolve issues such as water accumulation in low-lying areas and persistent gaseous water adhering to the pipe walls after cleaning. This necessitates evaluation and further treatment of the pipe drying effect.

[0004] Therefore, a pipeline drying method and Internet of Things system based on intelligent gas safety supervision are provided to solve the problem of natural gas quality being affected by incomplete drying inside gas pipelines, thereby improving the cleaning quality and effect of gas pipelines. Summary of the Invention

[0005] The invention includes a pipeline drying method based on intelligent gas safety monitoring. The method is executed by an IoT system for pipeline drying based on intelligent gas safety monitoring. The method includes: acquiring drying medium information for at least one gas pipeline outlet through the gas company's management platform; determining an initial drying value for the gas pipeline based on the drying medium information; in response to the initial drying value of at least one gas pipeline being lower than a drying threshold, designating the at least one gas pipeline as a target gas pipeline, and determining advanced drying parameters based on the initial drying value of the target gas pipeline; generating a first control command based on the advanced drying parameters, and sending the first control command... The data is sent to the gas company's target platform, which, based on the first control command, performs advanced drying on the target gas pipeline; acquires the detection data of the target gas pipeline during advanced drying and sends it to the government gas regulatory management platform; through the government gas regulatory management platform: assesses the confidence level of the initial drying value based on the detection data during advanced drying; generates a threshold adjustment command based on the confidence level of the initial drying value and sends the threshold adjustment command to the gas company's management platform so that the gas company's management platform updates the drying threshold; and generates a performance adjustment command based on the confidence level of the initial drying value.

[0006] The invention includes an IoT system for pipeline drying based on intelligent gas safety supervision, comprising a government gas supervision and management platform, a government gas supervision sensor network platform, a government-supervised object platform, a gas company sensor network platform, and a gas company object platform. The government-supervised object platform includes a gas company management platform. The gas company management platform is configured to: acquire drying medium information at at least one gas pipeline outlet; determine an initial drying value for the gas pipeline based on the drying medium information; in response to the initial drying value of at least one gas pipeline being lower than a drying threshold, designate the at least one gas pipeline as a target gas pipeline, determine advanced drying parameters based on the initial drying value of the target gas pipeline, and generate... A first control command is generated and sent to the gas company's object platform. Based on the first control command, the gas company's object platform performs advanced drying on the target gas pipeline. The platform acquires detection data of the target gas pipeline during advanced drying and sends it to the government gas regulatory management platform. The government gas regulatory management platform is configured to: assess the confidence level of the initial drying value based on the detection data during advanced drying; generate a threshold adjustment command based on the confidence level of the initial drying value and send the threshold adjustment command to the gas company's management platform to update the drying threshold; and generate a performance adjustment command based on the confidence level of the initial drying value.

[0007] The present invention includes, but is not limited to, the following beneficial effects: (1) By analyzing the initial drying value, the gas pipeline that is not fully dried can be evaluated, and different advanced drying parameters can be provided according to different initial drying values ​​to further advance the drying of the pipeline that is not fully dried, thereby improving the overall drying effect of the gas pipeline; (2) By adjusting the drying threshold according to the confidence level of the initial drying value, the gas pipeline that is not fully dried can be further screened as the target gas pipeline for further advanced drying. The lower the confidence level of the gas company, the more likely it is to have incomplete final drying, which will affect the quality and safety of gas use. The processor needs to process the detection data obtained by the crawling robot at a higher frequency to improve the accuracy of data processing and the timeliness of judgment; (3) Based on the actual detection data of the crawling robot in the target pipeline, the correction coefficient is determined to correct the initial drying value of the candidate gas pipeline, which can further screen out the target pipeline that meets the conditions and avoid the omission of the target pipeline; (4) The drying position with a natural drying time of less than the preset time threshold is removed from the advanced drying parameters, so that the crawling robot does not need to stay in the pipeline segment that has been naturally dried, thereby improving efficiency and safety, while saving resources and costs. Attached Figure Description

[0008] The present invention will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is a schematic diagram of the platform structure of an IoT system for pipeline drying based on intelligent gas safety monitoring, as shown in some embodiments of this specification.

[0010] Figure 2 This is an exemplary flowchart of a pipeline drying treatment method based on intelligent gas safety monitoring, as shown in some embodiments of this specification.

[0011] Figure 3 This is an exemplary schematic diagram illustrating the determination of sub-drying values ​​according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart illustrating the adjustment of advanced drying parameters according to some embodiments of this specification;

[0013] Figure 5 This is an exemplary schematic diagram of a natural drying model according to some embodiments of this specification. Detailed Implementation

[0014] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of the present invention. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0015] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0016] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0017] This invention uses flowcharts to illustrate the operations performed by the system according to embodiments of the invention. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0018] After gas pipelines are put into use, they need to be cleaned regularly to prevent the accumulation of large amounts of impurities inside. CN112692011A, through its rationally designed device structure, focuses solely on achieving the goal of quickly cleaning debris inside gas pipelines and simplifying operation, but fails to address the issues of evaluating the drying effect and treating incompletely dried areas. Therefore, some embodiments of this invention preliminarily determine the distribution of drying conditions based on cleaning parameters, drying parameters, pipeline characteristics, and ambient temperature, and combine this with internal monitoring data to determine the true degree of dryness. During this process, based on the distribution of drying conditions, a first drying command is generated to control the robot to reach the corresponding position. Then, combined with the true degree of dryness, advanced drying parameters are determined, and the robot is controlled to use these advanced drying parameters to treat incompletely dried pipeline areas. This allows for a better assessment of the drying condition inside the gas pipeline and further treatment of incompletely dried areas, thereby ensuring drying efficiency and improving the cleaning quality and effect of the gas pipeline.

[0019] Figure 1 This is a schematic diagram of the platform structure of an IoT system for pipeline drying based on intelligent gas safety supervision, as shown in some embodiments of this specification.

[0020] In some embodiments, such as Figure 1 As shown, the Internet of Things system 100 for pipeline drying based on smart gas safety supervision may include a government gas supervision and management platform 110, a government gas supervision sensor network platform 120, a government supervision object platform 130, a gas company sensor network platform 140, and a gas company object platform 150 connected in sequence.

[0021] The government gas regulatory management platform 110 is a platform used to process data related to government gas regulation. In some embodiments, the government gas regulatory management platform 110 can be configured as a processor. Government gas regulation-related data may include confidence levels of initial dryness values, threshold adjustment instructions, performance adjustment instructions, etc.

[0022] The government gas regulatory sensor network platform 120 is a platform used by the government for sensor communication. In some embodiments, the government gas regulatory sensor network platform 120 can be configured as a gateway device or communication device, etc., to realize data interaction functions.

[0023] In some embodiments, the government gas regulatory sensor network platform 120 can communicate with the government gas regulatory management platform 110 and the gas company management platform 131 of the government-regulated object platform 130. For example, the government gas regulatory sensor network platform 120 can acquire detection data and initial dryness values ​​sent by the gas company management platform 131 and upload them to the government gas regulatory management platform 110. As another example, the government gas regulatory sensor network platform 120 can acquire threshold adjustment instructions and performance adjustment instructions generated by the government gas regulatory management platform 110 and send them to the gas company management platform 131.

[0024] The Government Regulatory Target Platform 130 is a direct source platform for government users to obtain regulatory information.

[0025] In some embodiments, the government regulatory object platform 130 may include a gas company management platform 131.

[0026] The gas company management platform 131 is a platform for managing and analyzing data related to the gas company. In some embodiments, the gas company management platform 131 may include a data center and a testing database. The data center is used to store and manage data related to the drying treatment of gas pipelines. In some embodiments, the data center may be configured as a storage device. The testing database is a database of testing data for gas pipelines. In some embodiments, the testing database may be part of the data center or a separate storage device. More information about testing data can be found in [link to relevant documentation]. Figure 2 And related explanations.

[0027] In some embodiments, the gas company management platform 131 is configured to acquire drying medium information of at least one gas pipeline outlet; determine an initial drying value of the gas pipeline based on the drying medium information; in response to the initial drying value of at least one gas pipeline being lower than a drying threshold, designate at least one gas pipeline as a target gas pipeline and determine advanced drying parameters based on the initial drying value of the target gas pipeline; generate a first control command based on the advanced drying parameters and send the first control command to the gas company object platform 150, the gas company object platform 150 performing advanced drying on the target gas pipeline based on the first control command; acquire detection data of the target gas pipeline during advanced drying and send it to the government gas regulatory management platform 110.

[0028] In some embodiments, the drying medium information also includes the drying medium flow rate, and the gas company management platform 131 can be further configured to determine the moisture discharge data in the gas pipeline based on the drying medium information; and to determine the initial drying value based on the moisture discharge data.

[0029] In some embodiments, the gas pipeline includes at least one pipeline segment, and the initial drying value includes a sub-drying value of at least one pipeline segment. The gas company management platform 131 can be further configured to: segment the gas pipeline based on the gas pipeline information and determine the location information of at least one pipeline segment; and determine the sub-drying value of at least one pipeline segment based on the location information of at least one pipeline segment and moisture discharge data.

[0030] In some embodiments, the gas company management platform is further configured to: determine the location of residual moisture in the gas pipeline based on gas pipeline information; segment the gas pipeline based on the location of residual moisture, and determine the location information of at least one pipeline segment. More information on the location of residual moisture can be found at [link to relevant documentation]. Figure 2 And related explanations.

[0031] In some embodiments, the gas company management platform 131 is further configured to: construct a moisture discharge map based on the location information of at least one pipeline segment and moisture discharge data. The moisture discharge map includes nodes and edges. The nodes are pipeline segments, and the node features include the location information of the pipeline segment, gas pipeline information, drying medium information, and the location of residual moisture. The direction of the edges is the flow direction of the drying medium, and the edge features include the diameter at the junction of the pipeline segments. Based on the moisture discharge map, a sub-drying value for at least one pipeline segment is determined through a drying assessment model, where the drying assessment model is a graph neural network model.

[0032] In some embodiments, the gas company management platform 131 is further configured to: determine the actual dryness value of the target gas pipeline based on the detection data of the target gas pipeline; determine a correction value based on the actual dryness value and the initial dryness value; and correct the initial dryness value of the candidate gas pipeline based on the correction value.

[0033] In some embodiments, the gas company management platform 131 is further configured to: determine similar gas pipelines to the target gas pipeline based on gas pipeline information; and correct the initial dryness value of the similar gas pipelines based on a correction value.

[0034] In some embodiments, the gas company management platform 131 is further configured to: determine the moisture content in the target gas pipeline based on the initial drying value and the target gas pipeline information; determine the advanced drying parameters based on the moisture content; acquire the first detection data of the first position before advanced drying; evaluate the first drying value of the target gas pipeline at the first position based on the first detection data; generate a parameter adjustment instruction based on the first drying value; and adjust the advanced drying parameters of the second position based on the parameter adjustment instruction.

[0035] In some embodiments, the target gas pipeline includes multiple pipeline segments, and the gas company management platform can be further configured to: estimate the airflow drying effect of multiple pipeline segments at a second location based on a first drying value; determine the natural drying time of the target gas pipeline at the second location based on the airflow drying effect and the second drying value, where the second drying value refers to the drying value of the pipeline segment at the second location; and delete drying locations whose natural drying time is less than a preset time threshold from the advanced drying parameters.

[0036] In some embodiments, the gas company management platform can be further configured to: determine the natural drying duration based on an airflow drying effect sequence, a second location information sequence, and a second drying value sequence, using a natural drying model, where the natural drying model is a machine learning model. More information about natural drying models can be found in [link to relevant documentation]. Figure 5 And related explanations.

[0037] The gas company's sensor network platform 140 is a functional platform used by the gas company to monitor and transmit data related to the drying treatment of gas pipelines. In some embodiments, the gas company's sensor network platform 140 can be configured as a gateway device or communication device, etc., to realize data interaction functions.

[0038] In some embodiments, the gas company sensor network platform 140 can communicate with the gas company management platform 131 and the gas company object platform 150 of the government-regulated object platform 130. For example, the gas company sensor network platform 140 can obtain drying medium information uploaded by the gas company object platform 150 and send it to the gas company management platform 131 of the government-regulated object platform 130. As another example, the gas company sensor network platform 140 can obtain a first control command issued by the gas company management platform 131 of the government-regulated object platform 130 and send it to the gas company object platform 150.

[0039] The gas company's object platform 150 is a functional platform for generating sensing information and executing control information.

[0040] In some embodiments, the gas company object platform can be configured as a drying device, a blower, a testing device, and a crawling robot, etc.

[0041] Drying equipment refers to equipment used for the preliminary drying of gas pipelines. For example, drying equipment may include at least one of the following: pipeline pigs, air compressor units and heating equipment, nitrogen purging equipment, etc.

[0042] Gas drying equipment refers to equipment used for further drying of gas pipelines. It may include a fan and heating wires. The fan blows hot air through the heating wires, thereby further drying the gas pipelines.

[0043] The detection equipment is used to monitor relevant operating parameters of gas pipelines. The detection equipment can be configured with humidity sensors, temperature sensors, flow rate sensors, etc.

[0044] A crawling robot is an autonomous device capable of moving within a gas pipeline. In some embodiments, the crawling robot may be equipped with a drying device and a detection device. The crawling robot can move within the gas pipeline, using the drying device to perform advanced drying on different parts of the gas pipeline, and using the detection device to acquire detection data within the gas pipeline.

[0045] For further explanation of the above content, please refer to Figures 2-5 And related explanations.

[0046] Through the IoT system 100 for pipeline drying based on smart gas safety supervision, a closed loop of information operation can be formed between the gas company's object platform 150 and the government's gas supervision and management platform 110. Under the unified management of the government's object platform 130, it can be coordinated and operated in a regular manner, realizing the informatization and intelligentization of gas pipeline drying.

[0047] Figure 2This is an exemplary flowchart of a pipeline drying treatment method based on intelligent gas safety monitoring, as shown in some embodiments of this specification. Flow 200 is an exemplary flowchart of a pipeline drying treatment method based on intelligent gas safety monitoring, as follows: Figure 2 As shown, process 200 includes the following steps 210-280.

[0048] In some embodiments, steps 210-250 may be performed by the gas company management platform 131.

[0049] Step 210: Obtain the drying medium information of at least one gas pipeline outlet.

[0050] The gas pipeline is a gas pipeline that has undergone preliminary drying. Preliminary drying can be achieved using drying equipment and pre-defined methods. The drying equipment can include at least one of the following: a pipeline pig, an air compressor unit, heating equipment, or a nitrogen purging unit. The pre-defined methods can be at least one of the following: desiccant drying, dry air drying, or nitrogen drying.

[0051] A gas pipeline outlet refers to the outlet of the drying medium within the gas pipeline. The drying medium is the medium that provides the drying environment or creates the drying conditions. Different drying methods use different drying media, such as desiccants (e.g., methanol desiccants) used in desiccant drying, nitrogen used in nitrogen drying, and dry air used in dry air drying.

[0052] Gas pipeline drying is typically an inter-station drying process along long-distance pipelines, where the drying medium enters from the inlet of one gas station and flows out from the gas pipelines of other gas stations. In some embodiments, the gas pipeline outlet may include one or more outlets.

[0053] Drying medium information is information relating to the drying medium. In some embodiments, drying medium information may include the humidity of the drying medium at the gas pipeline outlet at the end of the initial drying process.

[0054] In some embodiments, the gas company management platform 131 can obtain drying medium information uploaded by the gas company object platform 150 through the gas company sensor network platform 140. The gas company object platform 150 can obtain drying medium information through sensors arranged at at least one gas pipeline outlet. For example, the humidity of the drying medium can be obtained through humidity sensors arranged at at least one gas pipeline outlet.

[0055] Step 220: Determine the initial drying value of the gas pipeline based on the drying medium information.

[0056] The initial dryness value is a numerical value used to characterize the degree of dryness of a gas pipeline after preliminary drying. The higher the initial dryness value, the drier the gas pipeline and the better the preliminary drying effect.

[0057] In some embodiments, the gas company management platform 131 can use the humidity of the drying medium at the end of the initial drying process as the theoretical humidity within the gas pipeline to calculate the initial drying value of the gas pipeline. The initial drying value is negatively correlated with the theoretical humidity.

[0058] For example, the gas company management platform 131 can calculate the initial dryness value using formula (1). Formula (1) is shown below:

[0059] Initial dryness value = 1 / theoretical humidity (1)

[0060] Theoretical humidity refers to the theoretical humidity value inside the gas pipeline after initial drying.

[0061] In some embodiments, the gas company management platform 131 can determine the moisture discharge data in the gas pipeline based on the drying medium information; and determine the initial drying value based on the moisture discharge data.

[0062] Moisture discharge data refers to data related to the moisture discharged from the gas outlet during the initial drying process of a gas pipeline. In some embodiments, moisture discharge data includes moisture discharge rate, moisture discharge volume, etc. Moisture discharge rate refers to the rate at which moisture is discharged from the gas outlet during the initial drying process. Moisture discharge volume refers to the amount of moisture contained in the drying medium discharged from the gas outlet during the initial drying process.

[0063] In some embodiments, the gas company management platform 131 can obtain the flow rate and humidity of the drying medium at multiple consecutive moments during the initial drying process; based on the flow rate and humidity of the drying medium between two adjacent moments, the sub-moisture discharge amount between the two adjacent moments is calculated, and the total moisture discharge amount of the gas pipeline is obtained by accumulating multiple consecutive sub-moisture discharge amounts; the sub-moisture discharge rate can be calculated based on the sub-moisture discharge amount and the time length between two moments.

[0064] In some embodiments, the gas company management platform 131 can perform curve fitting on multiple sub-moisture discharge amounts and sub-moisture discharge rates during the preliminary drying process to determine the moisture discharge variation curve; integrate the moisture discharge data variation curve to obtain the total moisture content in the gas pipeline; take the difference between the total moisture content in the gas pipeline and the moisture discharge amount as the remaining moisture content in the gas pipeline; and determine the initial drying value based on the remaining moisture content in the gas pipeline and the gas pipeline information. The moisture discharge variation curve reflects the changes in sub-moisture discharge amounts and sub-moisture discharge rates at multiple time periods during the preliminary drying process, where multiple time periods are composed of multiple adjacent moments during the preliminary drying process.

[0065] The gas company management platform 131 can calculate the volume of the gas pipeline based on the diameter and length of the gas pipeline in the gas pipeline information, and use the ratio of the remaining water content to the volume as the theoretical humidity to calculate the initial dryness value based on formula (1). For formula (1), please refer to the above text, and for gas pipeline information, please refer to the following text.

[0066] The initial drying value is determined by measuring the moisture discharge data. The moisture discharge data can be calculated based on multiple detection data, and the total moisture content before the initial drying of the gas pipeline can be obtained by fitting the data. The remaining moisture content can then be calculated. Based on the remaining moisture content, the initial drying value can be made more accurate, reducing the error caused by directly measuring the humidity of the drying medium.

[0067] In some embodiments, the gas pipeline includes at least one pipeline segment, and the initial dryness value includes a sub-dryness value of at least one pipeline segment.

[0068] Pipeline segmentation refers to dividing the same gas pipeline into at least one segment, each segment being a pipeline segment. Different pipeline segments correspond to different sub-drying values.

[0069] In some embodiments, the gas company management platform 131 can segment the gas pipeline based on gas pipeline information to determine the location information of at least one pipeline segment; and determine the sub-drying value of at least one pipeline segment based on the location information of at least one pipeline segment and moisture discharge data.

[0070] Gas pipeline information refers to information related to the size and distribution of gas pipelines. For example, the length, diameter, material, service life, branches, joints, and bends of the gas pipeline. In some embodiments, the gas company management platform 131 can obtain gas pipeline information uploaded by the government gas regulatory management platform 110 through the government gas regulatory sensor network platform 120. Government users can upload gas pipeline information to the government gas regulatory management platform 110 through their user terminals.

[0071] In some embodiments, the gas company management platform 131 can segment the gas pipeline based on gas pipeline information using various methods. For example, the gas company management platform 131 can segment different branch pipelines into different pipeline segments. Another example is that the gas company management platform 131 can segment gas pipelines of different materials into different pipeline segments. Yet another example is that the gas company management platform 131 can segment gas pipelines of different diameters into different pipeline segments.

[0072] In some embodiments, the gas company management platform 131 can determine the location of residual moisture in the gas pipeline based on gas pipeline information; and divide the gas pipeline into segments based on the location of residual moisture to determine the location information of at least one pipeline segment.

[0073] Moisture residue locations refer to places in gas pipelines where moisture is likely to remain, such as recesses, joints, branches, and bends in the pipeline.

[0074] In some embodiments, the location of residual moisture can be determined based on prior experience. For example, locations where water accumulation exists in historical data can be set as the location of residual moisture based on prior experience.

[0075] In some embodiments, the gas company management platform 131 can use the location of residual moisture as the dividing point of the pipeline segmentation, and divide the gas pipeline into multiple pipeline segments according to the dividing point.

[0076] The presence of residual water can affect the drying value of nearby pipes. Segmenting the pipes according to the location of residual water can yield more refined and accurate drying values, providing accurate data references for determining advanced drying parameters later.

[0077] Pipeline segment location information is information used to indicate the position of a pipeline segment within a gas pipeline. The location information of a pipeline segment can be represented by its distance from the gas pipeline inlet and / or outlet.

[0078] In some embodiments, the gas company management platform 131 can construct feature vectors based on moisture discharge data and pipeline segment location information, retrieve the reference dryness value corresponding to the reference vector with the highest similarity as the sub-dryness value of different gas pipeline segments based on the feature vectors in the vector database.

[0079] The vector database includes multiple reference vectors and their corresponding reference drying values. The vector database can be constructed based on actual drying values ​​and their corresponding pipeline segment location information and moisture discharge data. For information on actual drying values, please refer to [link to relevant documentation]. Figure 2 The following is a related explanation.

[0080] In some embodiments, the feature vector also includes the location of residual moisture. See above for details on the location of residual moisture.

[0081] Gas pipelines between gas plants are not single pipelines; they may branch and vary in shape. During the initial drying process, the drying conditions of each pipeline section may differ, resulting in varying levels of residual moisture. Furthermore, the desiccant absorbs a large amount of moisture in the early stages of entering the pipeline, leading to saturation and reduced absorption capacity in later stages. This results in lower drying values ​​for pipeline sections closer to the pipeline outlet. Therefore, determining the sub-drying values ​​for each pipeline section allows for more accurate determination of the desired drying value.

[0082] In some embodiments, the gas company management platform 131 can determine the actual dryness value of the target gas pipeline based on the detection data of the target gas pipeline; determine a correction value based on the actual dryness value and the initial dryness value; and correct the initial dryness value of the candidate gas pipeline based on the correction value.

[0083] Target gas pipelines refer to gas pipelines that require advanced drying. Further details about target gas pipelines can be found below. For initial drying values, please refer to the relevant explanations above.

[0084] Candidate gas pipelines refer to all gas pipelines other than the target gas pipeline. The initial dryness value of candidate gas pipelines is greater than or equal to the dryness threshold.

[0085] Actual dryness value is used to characterize the actual dryness level inside the target gas pipeline.

[0086] In some embodiments, the gas company management platform 131 can obtain the actual moisture content detected by the crawling robot in different pipeline segments, determine the sub-drying value corresponding to different pipeline segments based on the actual moisture content using formula (1), and use the statistical value of the sub-drying value of different pipeline segments as the actual drying value. Formula (1) can be found above. The statistical value can be the mean.

[0087] The correction value refers to the corrected initial drying value.

[0088] In some embodiments, the gas company management platform 131 may determine at least one correction coefficient corresponding to at least one target gas pipeline based on the actual dryness value and the initial dryness value of at least one target gas pipeline; multiply the statistical value of the correction coefficient by the initial dryness value of the candidate gas pipeline as the correction value; and adjust the initial dryness value of the candidate gas pipeline to the correction value. The statistical value of the correction coefficient may be the average value of the correction coefficients.

[0089] The correction factor is positively correlated with the actual dryness value of the target gas pipeline and negatively correlated with the initial dryness value of the target gas pipeline. For example, the gas company management platform 131 can determine the correction factor using formula (2), which is shown below:

[0090] Correction factor = Actual drying value / Initial drying value (2)

[0091] In some embodiments, the gas company management platform 131 can re-evaluate candidate gas pipelines based on the corrected initial drying value, and select candidate gas pipelines whose corrected initial drying value is lower than the drying threshold as target gas pipelines for further drying.

[0092] The initial dryness value is calculated based on the humidity of the drying medium at the gas pipeline outlet. This is an estimate and may deviate from the actual humidity inside the pipeline. A correction coefficient is determined based on the actual detection data from the crawling robot inside the target pipeline. This correction adjusts the initial dryness value of candidate gas pipelines, allowing for further screening of target pipelines that meet the criteria and preventing the omission of any target pipelines.

[0093] In some embodiments, the gas company management platform 131 can determine similar gas pipelines to the target gas pipeline based on gas pipeline information; and correct the initial dryness value of the similar gas pipelines based on correction values. For information on gas pipeline information, please refer to the relevant description above.

[0094] Similar gas pipelines refer to candidate gas pipelines that are similar to the target gas pipeline. In some embodiments, the gas company management platform 131 can construct a first feature vector based on gas pipeline information, calculate the similarity between the first feature vector of the target gas pipeline and the first feature vector of the candidate gas pipelines, and designate candidate gas pipelines with similarity greater than a similarity threshold as similar gas pipelines to the target gas pipeline. Similarity is negatively correlated with vector distance, which can be Euclidean distance, cosine distance, etc. For more information on candidate gas pipelines, please refer to the relevant explanations above.

[0095] In some embodiments, the gas company management platform 131 may obtain the correction coefficient of the target gas pipeline corresponding to the similar gas pipeline; use the product of the correction coefficient and the initial dryness value of the similar gas pipeline as the correction value; and adjust the initial dryness value of the similar gas pipeline to the correction value.

[0096] Because gas pipelines are long, comparing entire sections of gas pipelines may result in significant differences between different pipelines, potentially leading to a limited number of similar gas pipelines that meet the selection criteria.

[0097] In some embodiments, for a specific segment of a target gas pipeline, the gas company management platform 131 can construct a second feature vector based on the gas pipeline information, segment location information, and moisture residue location information of that segment. It then searches the segment vector database for reference feature vectors with a similarity greater than a similarity threshold, calculates the similarity between the second feature vectors of different segments of the target gas pipeline and the second feature vectors of segments of candidate gas pipelines, and identifies segments with a similarity greater than the similarity threshold as similar pipeline segments. The vector database includes multiple gas pipeline segments of all candidate gas pipelines and their corresponding reference feature vectors. The reference feature vectors are constructed based on the gas pipeline information, location information, and moisture residue location of the segments of the candidate gas pipelines.

[0098] In some embodiments, the gas company management platform 131 can calculate the correction coefficient of the target gas pipeline segment based on the actual dryness value and sub-dryness value of the pipeline segment using formula (2), and use the product of the correction coefficient and the sub-dryness value of the similar gas pipeline segment as the correction value of the similar pipeline segment, and adjust the sub-dryness value of the similar pipeline segment to the correction value. For details on actual dryness value and sub-dryness value, please refer to the relevant description above.

[0099] In some embodiments, if similar pipeline segments correspond to pipeline segments of multiple target gas pipelines, a weighted sum of the correction values ​​of the pipeline segments of the multiple target gas pipelines is calculated, and this weighted sum is used as the final correction value of the pipeline segment. The weight can be determined based on the similarity between the reference feature vector of the pipeline segment and the second feature vector of the pipeline segments of the multiple target gas pipelines, and the weight is positively correlated with the similarity.

[0100] By only modifying similar gas pipelines, the accuracy of the modification operation can be ensured, and ineffective operations can be avoided.

[0101] Step 230: In response to the fact that the initial dryness value of at least one gas pipeline is lower than the dryness threshold, at least one gas pipeline is designated as the target gas pipeline, and advanced drying parameters are determined based on the initial dryness value of the target gas pipeline.

[0102] The drying threshold refers to the critical value for the initial dryness of a gas pipeline. The drying threshold can be set based on experience or requirements.

[0103] For information about the target gas pipeline, please refer to the relevant explanation above.

[0104] Advanced drying refers to further drying of gas pipelines. Water accumulation in low-lying areas and residual liquid water adhering to the pipe walls are difficult to completely remove through initial drying and require further advanced drying. Advanced drying can be performed using a crawling robot that enters the gas pipeline. The drying equipment on the crawling robot uses a fan and heating wires to blow hot air to dry areas with high humidity, such as low-lying areas, within the pipeline. More information about crawling robots and drying equipment can be found in [link to relevant documentation]. Figure 1 .

[0105] Advanced drying parameters refer to the parameters relevant to the advanced drying of the target gas pipeline. These include, for example, the drying location, the blowing speed and duration of the blower on the drying equipment, and the heating power of the heating wires on the drying equipment. The drying location refers to the position of the crawling robot during the advanced drying of the target gas pipeline. For a given target gas pipeline, which may be long and have multiple depressions, the crawling robot can stop at multiple locations along the pipeline to ensure effective advanced drying.

[0106] In some embodiments, the gas company management platform 131 can uniformly set up multiple drying locations based on the length of the gas pipeline; the spacing between adjacent drying locations can be determined based on an initial drying value, with smaller spacing as the initial drying value is smaller.

[0107] In some embodiments, the gas company management platform 131 can set the heating power of the heating wire on the drying device based on the difference between the initial drying value and the drying threshold of the target gas pipeline; the larger the difference, the greater the heating power.

[0108] In some embodiments, the gas company management platform 131 can set the drying speed and drying time of the blower on the drying equipment based on the distance between the drying location and the gas pipeline outlet. The farther the distance, the higher the wind speed and the longer the drying time, so that the moisture can be blown out of the gas pipeline.

[0109] Step 240: Generate a first control command based on the advanced drying parameters and send the first control command to the gas company's object platform. The gas company's object platform performs advanced drying on the target gas pipeline based on the first control command.

[0110] The first control command is an operational command that controls the drying equipment of the crawling robot to perform advanced drying. In some embodiments, the gas company object platform 150 can control the drying equipment on the crawling robot to perform advanced drying on the target gas pipeline based on the first control command. For more information on crawling robots and drying equipment, please refer to [link to relevant documentation]. Figure 1 Related explanations.

[0111] Step 250: Obtain the detection data of the target gas pipeline during advanced drying and send it to the government's gas regulatory management platform.

[0112] The detection data refers to the actual moisture data detected by the crawling robot inside the gas pipeline. In some embodiments, the detection data includes the air humidity and water accumulation status inside the gas pipeline, with the water accumulation status including the water area and water depth.

[0113] In some embodiments, the gas company management platform 131 can obtain detection data uploaded by the gas company object platform 150 through the gas company sensor network platform 140. The gas company object platform 150 can obtain the air humidity inside the gas pipeline through a humidity sensor configured on a crawling robot; and obtain the water accumulation status inside the gas pipeline through an acoustic detector.

[0114] In some embodiments, steps 260-280 may be performed by the government gas regulatory management platform 110.

[0115] Step 260: Based on the detection data during advanced drying, assess the confidence level of the initial drying value.

[0116] The confidence level of the initial drying value is used to characterize the reliability of the initial drying value.

[0117] In some embodiments, the government gas regulatory management platform 110 can determine the theoretical moisture content in the gas pipeline based on theoretical humidity and gas pipeline size; determine the actual moisture content based on detection data; and determine the confidence level of the initial dryness value based on the difference between the theoretical moisture content and the actual moisture content.

[0118] The dimensions of a gas pipeline can be the internal volume of the gas pipeline. The dimensions can be calculated based on the length and diameter of the gas pipeline from the gas pipeline information.

[0119] Theoretical moisture content refers to the theoretical amount of water remaining in the gas pipeline after initial drying. In some embodiments, the government gas regulatory management platform 110 may use the product of theoretical humidity and gas pipeline size as the theoretical moisture content.

[0120] Actual moisture content refers to the actual amount of moisture detected inside the gas pipeline after initial drying. Actual moisture content includes the moisture content in the air and the amount of water accumulated. In some embodiments, actual moisture content is positively correlated with air humidity, gas pipeline size, water accumulation area, and water accumulation depth. The government gas regulatory management platform 110 can calculate the actual moisture content using formula (3).

[0121] Actual water content = air humidity × gas pipeline size + water density × water accumulation area × water accumulation depth (3)

[0122] The confidence level of the initial dryness value is positively correlated with the absolute value of the difference between the theoretical moisture content and the actual moisture content. For example, the government gas regulatory management platform 110 can calculate the confidence level of the initial dryness value using formula (4).

[0123] Confidence level of initial dryness value = 1 - |AB| / B (4)

[0124] Where A is the initial drying value and B is the actual drying value.

[0125] Step 270: Based on the confidence level of the initial dryness value, generate a threshold adjustment instruction and send the threshold adjustment instruction to the gas company management platform so that the gas company management platform can update the dryness threshold.

[0126] Threshold adjustment command refers to the operation command for adjusting the drying threshold.

[0127] In some embodiments, the government gas regulatory management platform 110 may generate a threshold adjustment instruction in response to the confidence level of the initial dryness value being less than a preset threshold.

[0128] In some embodiments, the adjusted drying threshold is positively correlated with the confidence level of the initial drying value; the lower the confidence level of the drying value, the lower the adjusted drying threshold.

[0129] In some embodiments, the gas company management platform 131 can, based on steps 210-240, determine the candidate gas pipelines, further filter out new target gas pipelines with initial drying values ​​lower than the adjusted drying threshold, and perform advanced drying on the new target gas pipelines. This ensures that as many incompletely dried gas pipelines as possible are selected as target gas pipelines for advanced drying, avoiding omissions. For information on candidate gas pipelines, please refer to the relevant descriptions above.

[0130] Step 280: Generate performance adjustment instructions based on the confidence level of the initial drying value.

[0131] The performance adjustment command is an instruction message that adjusts the frequency at which the processor of the government gas regulatory management platform 110 acquires and processes data related to different gas companies. Gas company-related data refers to data related to the drying treatment of the gas company's gas pipelines, such as information on the drying medium for the gas pipelines, gas pipeline information, detection data, threshold adjustment commands, etc.

[0132] In some embodiments, the government gas regulatory management platform 110 can generate performance adjustment instructions based on the reciprocal of the confidence level of the initial dryness values ​​of different gas companies, and adjust the performance allocation strategy accordingly. The performance allocation strategy refers to the frequency at which the processor of the government gas regulatory management platform 110 processes data related to different gas companies.

[0133] The government gas regulatory management platform 110 can use the ratio of the average of the inverses of the confidence levels of the initial dryness values ​​of multiple gas pipelines from different gas companies as the ratio of the processor's processing frequency for different gas companies, generate performance adjustment instructions, and adjust the performance allocation strategy.

[0134] By analyzing initial dryness values, gas pipelines that have not been thoroughly dried can be assessed. Based on different initial dryness values, different advanced drying parameters can be provided to further dry these pipelines, thereby improving the overall drying effect. Adjusting the drying threshold based on the confidence level of the initial dryness value allows for the further screening of incompletely dried gas pipelines as target pipelines for advanced drying. Gas companies with lower confidence levels are more prone to incomplete final drying, which can affect gas quality and safety. Therefore, processors need to process the detection data acquired by the crawling robot at a higher frequency to improve the accuracy of data processing and the timeliness of judgment.

[0135] Figure 3This is an exemplary schematic diagram illustrating the determination of sub-drying values ​​according to some embodiments of this specification.

[0136] In some embodiments, such as Figure 3 As shown, the gas company management platform 131 can construct a moisture discharge map 320 based on at least one pipeline segment 311 and moisture discharge data 312; based on the moisture discharge map 320, it can determine the sub-drying value 340 of at least one pipeline segment through a drying assessment model 330.

[0137] A moisture discharge map is used to represent the discharge of moisture across different sections of the same gas pipeline. One moisture discharge map corresponds to one gas pipeline. In some embodiments, the moisture discharge map may include nodes, node features, edges, and edge features.

[0138] The nodes in the moisture discharge map represent pipeline segments. The number of pipeline segments is equal to the number of nodes. For example, such as... Figure 3 As shown, the nodes can include node A, node B, node C, and node D, which represent the four pipe sections of the gas pipeline, respectively.

[0139] The node features of the moisture discharge map reflect the characteristic information of pipeline segments. In some embodiments, node features may include the location information of the pipeline segment, gas pipeline information, drying medium information, and the location of residual moisture. More information on the location information of pipeline segments, gas pipeline information, drying medium information, and the location of residual moisture can be found in [link to relevant documentation]. Figure 3 And related explanations.

[0140] The edges of the moisture discharge diagram are used to indicate the connection relationship between two adjacent pipe segments. The direction of the edge can be the flow direction of the drying medium.

[0141] Edge features are used to describe the connection characteristics between adjacent pipe segments. Edge features can include the diameter at the junction of pipe segments.

[0142] In some embodiments, the gas company management platform 131 can construct a moisture discharge map based on at least one pipeline segment and moisture discharge data. For example, the gas company management platform 131 can determine at least one node and edge based on the location information of at least one pipeline segment and gas pipeline information; use the location information of at least one pipeline segment, gas pipeline information, drying medium information, and moisture residue location as node features in the moisture discharge map; and use the moisture discharge direction in the moisture discharge data as the edge direction. Based on the location information in the node features, a preset table is used to determine the diameter at the junction of the pipeline segments, which is then used as the edge feature. The preset table includes pipeline diameters at different locations and can be constructed based on gas pipeline information. For information on gas pipeline information, please refer to [link to relevant documentation]. Figure 1 Related explanations.

[0143] The drying assessment model 330 is a model used to determine the sub-drying value 340 of at least one pipeline segment. The input to the drying assessment model 330 can be a moisture discharge map 320, and the output can be the sub-drying value 340 of at least one pipeline segment. In some embodiments, the drying assessment model 330 can be a graph neural network (GNN) model.

[0144] In some embodiments, the dryness assessment model can be trained using a large number of first training samples and first labels corresponding to the first training samples.

[0145] In some embodiments, the first training sample includes a historical moisture discharge map of sample pipelines constructed based on historical data from multiple gas companies. The first label is the actual dryness value corresponding to each pipeline segment in the first training sample. Here, the actual dryness value refers to the actual degree of dryness within the sample pipeline; more information on the actual dryness value and its determination method can be found in [link to relevant documentation]. Figure 2 And related explanations.

[0146] In some embodiments, the dryness assessment model can be trained through the following steps:

[0147] Step 1: Obtain the training dataset. The training dataset includes multiple first training samples and a first label corresponding to each first training sample. The training dataset is a collection of training data from multiple gas companies.

[0148] Step 2 involves performing multiple iterations. At least one iteration includes: selecting one or more training samples from the training dataset; inputting these training samples into the drying evaluation model to obtain the model's predicted outputs corresponding to the training samples; substituting the model's predicted outputs and the labels of these training samples into a predefined loss function formula to calculate the value of the loss function; and updating the model parameters in the drying evaluation model in reverse, for example, using gradient descent.

[0149] Step 3: When the iteration termination condition is met, the iteration ends, and the trained dry evaluation model is obtained. The iteration termination condition can be loss function convergence, the number of iterations reaching a threshold, etc.

[0150] Moisture discharge maps can reveal the characteristics and connections of each pipeline segment. Based on the moisture discharge maps, the sub-drying values ​​of pipeline segments can be determined through a drying assessment model, which helps to improve the accuracy and efficiency of predicting sub-drying values.

[0151] In some embodiments, for a gas company, the gas company management platform 131 may obtain detection data from the gas company management platform in response to the initial dryness value having a confidence level lower than a confidence level threshold; based on the detection data, determine a supplementary training dataset for the dryness assessment model; and train the dryness assessment model based on the supplementary training dataset to obtain the dryness assessment model for the gas company.

[0152] In some embodiments, the gas company management platform 131 may retrieve detection data from its detection database in response to the gas company's initial dryness confidence level being lower than a confidence threshold. For details regarding the initial dryness confidence level and the confidence threshold, please refer to [link to relevant documentation]. Figure 2 And related explanations.

[0153] The supplementary training dataset refers to an expanded training dataset for a single gas company, added to the initial model training dataset. The supplementary training dataset includes multiple second training samples and a second label for each second training sample. Specifically, for a gas company, the corresponding second training samples include multiple historical moisture discharge maps constructed from historical data obtained from that gas company, based on multiple pipeline segments and moisture discharge data. The second label can be the actual dryness value for each pipeline segment corresponding to the second training sample.

[0154] The drying assessment model of a gas company refers to a specific drying assessment model for a particular gas company. When the gas company management platform 131 obtains data from the gas company, it can determine the sub-drying value of at least one pipeline segment based on the moisture discharge map and the gas company's drying assessment model.

[0155] In some embodiments, the gas company management platform 131 can train a gas company dryness assessment model based on multiple second training samples and multiple second labels in a supplementary training dataset. For details on the specific training process, please refer to the relevant descriptions above.

[0156] Since the data volume from a single gas company is insufficient, a general dryness assessment model is initially trained using data from multiple gas companies. If the confidence level of the initial dryness value from a particular gas company is insufficient, supplementary training datasets can be obtained for that gas company. This allows for further training on the existing dryness assessment model to obtain a model better suited to that company's specific circumstances. Consequently, the sub-dryness values ​​determined by the dryness assessment model become more accurate.

[0157] Figure 4 This is an exemplary flowchart illustrating the adjustment of advanced drying parameters according to some embodiments of this specification. Figure 4 As shown, process 400 includes steps 410-450. Steps 410-450 can be executed through the gas company management platform 131.

[0158] Step 410: Based on the initial dryness value and the target gas pipeline information, determine the moisture content in the target gas pipeline. For information on the initial dryness value, please refer to... Figure 2 And related explanations.

[0159] Target gas pipeline information refers to relevant information about the target gas pipeline. For example, target gas pipeline information may include the pipeline's length, diameter, material, service life, branches, joints, bends, etc.

[0160] The water content in the target gas pipeline refers to the moisture content of multiple pipe sections of the target gas pipeline, which is used to determine the required drying air velocity of the blower and the heating power of the heating wire, etc.

[0161] In some embodiments, the gas company management platform 131 can calculate the pipe volume of the target gas pipeline based on the diameter and length in the target gas pipeline information, and calculate the water content in the target gas pipeline based on the theoretical humidity corresponding to the pipe volume and the initial dryness value.

[0162] In some embodiments, the gas company management platform 131 can also calculate the water content of different pipeline segments of the target gas pipeline.

[0163] Step 420: Determine the advanced drying parameters based on the moisture content. For an explanation of the advanced drying parameters, please refer to [link to relevant documentation]. Figure 1 .

[0164] In some embodiments, the gas company management platform 131 can determine the advanced drying parameters by: setting different pipeline segment starting points as the starting positions of the advanced drying; setting the heating power of the heating wire in the blowing equipment according to the moisture content, with higher heating power for higher moisture content; and setting the drying air velocity and drying time of the blower in the blowing equipment according to the moisture content and the position of the pipeline segment, with higher drying air velocity and longer drying time for higher moisture content and the farther the pipeline segment is from the pipeline outlet. Here, the pipeline segment starting point refers to the starting position in the direction of the drying medium flow.

[0165] Step 430: Obtain the first detection data of the first position before advanced drying, and evaluate the first drying value of the target gas pipeline at the first position based on the first detection data.

[0166] The first position refers to the real-time location of the crawling robot within a specific segment of the target gas pipeline, before that segment has undergone drying treatment. The first position can be represented by the location information of the pipeline segment in which the crawling robot is located. For information on the location information of pipeline segments, please refer to [link to relevant documentation]. Figure 2 Related instructions: On the crawling robot's path, the pipe sections preceding the first position have already undergone advanced drying.

[0167] The first detection data refers to the actual moisture data obtained by the crawling robot at the first location inside the gas pipeline. For example, the first detection data may include the air humidity and water accumulation status of the pipeline section at the first location.

[0168] In some embodiments, the first detection data can be obtained by a humidity sensor or an acoustic detector configured on the crawling robot.

[0169] The first drying value refers to the sub-drying value of the pipeline segment at the first location. The first drying value may change due to the progressive drying process of the upstream pipeline segment. For example, if the airflow temperature is higher when the upstream pipeline segment is drying, it may carry away the moisture at the first location, thus increasing the first drying value. The upstream pipeline segment can be upstream (i.e., the starting point) in the flow sequence of the drying medium.

[0170] In some embodiments, the gas company management platform 131 can use the air humidity in the first detection data as the theoretical humidity and calculate the first dryness value using formula (1). For formula (1), please refer to [link to formula 1]. Figure 2 And related explanations.

[0171] Step 440: Generate parameter adjustment instructions based on the first drying value.

[0172] Parameter adjustment instructions refer to instructions that adjust advanced drying parameters. For example, parameter adjustment instructions may include decreasing and / or increasing the drying airflow rate and heating power.

[0173] In some embodiments, the gas company management platform 131 can determine the first dryness value at different locations based on the first detection data, compare the first dryness value with the initial dryness value, and thus determine the adjustment instruction. For example, when the first dryness value is higher than the initial dryness value, the drying air speed and heating power are reduced; when the first dryness value is lower than the initial dryness value, the drying air speed and heating power are increased.

[0174] Step 450: Adjust the advanced drying parameters of the second position based on the parameter adjustment command.

[0175] The second position refers to the section of the gas pipeline following the crawler's current location in the direction the crawler is traveling along the target gas pipeline. The second position is located after the first position. In other words, the pipeline section located at the second position has not yet undergone advanced drying.

[0176] In some embodiments, the gas company management platform 131 can send parameter adjustment instructions to the gas company object platform 150, and the gas company object platform 150 can adjust the advanced drying parameters of the second position based on the parameter adjustment instructions.

[0177] Based on the first detection data, the first drying value at the first position is determined, the drying effect of the advanced drying parameters is evaluated, and the advanced drying parameters at the second position are adjusted accordingly. The impact of the advanced drying of the upstream pipeline on the downstream pipeline is taken into account. The advanced drying parameters of the downstream pipeline to be further dried are adjusted in a timely manner according to the impact effect, so that the subsequent pipeline can show a better drying effect.

[0178] In some embodiments, the gas company management platform 131 can determine the natural drying time of the target gas pipeline at the second position based on the airflow drying effect and the second drying value; and delete the drying positions with a natural drying time less than a preset time threshold from the advanced drying parameters.

[0179] The airflow drying effect refers to the drying effect of the airflow generated by advanced drying on downstream pipeline sections. For example, it can determine how much moisture the airflow generated by advanced drying can remove from a downstream gas pipeline. The downstream pipeline section can be downstream of the flow sequence of the drying medium (i.e., the outlet).

[0180] In some embodiments, the gas company management platform 131 can determine the cumulative impact value of one or more pipeline segments on downstream pipeline segments during advanced drying based on the difference between the first drying value and the initial drying value of multiple upstream pipeline segments, and use the cumulative impact value as the airflow drying effect.

[0181] The cumulative effect refers to the cumulative impact of all advanced drying operations in the upstream pipeline on the initial drying value of each pipeline segment. For example, if there are pipeline segments 1, 2, 3, 4, and 5 from upstream to downstream, the cumulative effect of pipeline segment 2 is that its initial drying value is affected by the advanced drying of pipeline segment 1; the cumulative effect of pipeline segment 4 is that its initial drying value is affected by the cumulative impact of the advanced drying of pipeline segments 1, 2, and 3.

[0182] The cumulative impact value refers to the difference between the real-time drying value after the cumulative impact and the drying threshold. For information on the drying threshold, please refer to [link to relevant documentation]. Figure 2 Related explanations.

[0183] The second drying value refers to the sub-drying value of the pipeline segment at the second location. The sub-drying value refers to the initial drying value of the pipeline segment; see details below. Figure 2 Related explanations.

[0184] Natural drying time reflects the time required for the dryness value of a downstream pipeline segment to reach the drying threshold solely due to the airflow generated by the advanced drying of the upstream pipeline segment. Natural drying time can be the time from when the crawling robot enters the target gas pipeline until the dryness value of the downstream pipeline segment reaches the drying threshold due to the influence of the advanced drying of the upstream pipeline.

[0185] In some embodiments, the natural drying time can be determined by various methods. For example, the gas company management platform 131 can determine the natural drying time for the second location by looking up a preset table based on the airflow drying effect and the second drying value. The preset table includes a mapping relationship between the airflow drying effect, the second drying value, and the natural drying time. The preset table can be based on a historical data framework.

[0186] In some embodiments, the gas company management platform 131 can determine the natural drying time based on the airflow drying effect, the location information of multiple pipeline segments at the second location, and the second drying value, using a natural drying model. See details below. Figure 5 .

[0187] The preset time threshold is a critical value for the natural drying time. In some embodiments, the preset time threshold can be the time it takes for the crawling robot to travel from entering the target gas pipeline to reaching that pipeline segment. In some embodiments, the gas company management platform 131 can remove drying locations with a natural drying time less than the preset time threshold from the advanced drying parameters. That is, the crawling robot will not dry pipeline segments with a natural drying time less than the preset time threshold.

[0188] If the natural drying time is less than the preset time threshold, it means that the pipe segment has already completed natural drying before the crawling robot reaches it. If the drying positions with natural drying time less than the preset time threshold are removed from the advanced drying parameters, the crawling robot does not need to stay on the pipe segment that has already completed natural drying, thereby improving efficiency and safety, while saving resources and costs.

[0189] Figure 5 This is an exemplary schematic diagram of a natural drying model according to some embodiments of this specification.

[0190] In some embodiments, such as Figure 5 As shown, the government gas regulatory management platform 110 can determine the natural drying time 530 based on the airflow drying effect sequence 511, the second location information sequence 512, and the second drying value sequence 513 through the natural drying model 520.

[0191] The airflow drying effect sequence 511 refers to the sequence consisting of the airflow drying effects corresponding to multiple pipe segments at the second position. The second position information sequence 512 refers to the sequence consisting of the position information of multiple pipe segments at the second position. For information on the position information of the pipe segments, please refer to... Figure 2 Related explanation. The second drying value sequence 513 refers to the sequence composed of sub-drying values ​​of multiple pipe segments at the second position.

[0192] Natural drying model 520 is a model used to predict the natural drying time of a target gas pipeline at a second location. In some embodiments, the natural drying model is a machine learning model, such as a deep neural network (DNN) model. For information on airflow drying effect, second location, second drying value, and natural drying time, please refer to [link to relevant documentation]. Figure 4 Related instructions; for information on the location of pipeline sections, please refer to [link / reference]. Figure 2 Related explanations.

[0193] In some embodiments, the natural drying model can be trained using a large number of third training samples and corresponding third labels. In some embodiments, multiple third training samples with third labels can be input into the initial natural drying model. A loss function is constructed using the third labels and the results of the initial natural drying model. Based on the loss function, the parameters of the initial natural drying model are iteratively updated using gradient descent or other methods. Model training is complete when preset conditions are met, resulting in a trained natural drying model. These preset conditions may include loss function convergence, the number of iterations reaching a threshold, etc.

[0194] Each training sample in the third training sample may include a sample airflow drying effect sequence, a sample second position information sequence, and a sample second drying value sequence. The third training sample can be obtained from historical data, specifically the historical airflow drying effect sequence, historical second position information sequence, and historical second drying value sequence corresponding to the second position when the crawling robot is located in different pipe segments of the target gas pipeline. The third label corresponding to the third training sample is the sample natural drying time corresponding to each training sample. In some embodiments, the third label may be the actual natural drying time corresponding to a pipe segment whose real-time drying value at the second position of the third training sample is just greater than the drying threshold, as subsequently measured.

[0195] The actual natural drying time reflects the actual time required for a pipeline segment to complete natural drying due to the influence of the upstream pipeline's advanced drying process. The actual natural drying time can be the duration from the first moment to the second moment. The first moment refers to the time from when the crawling robot enters the target gas pipeline, and the second moment refers to the moment when the real-time drying value detected by the crawling robot in the pipeline segment just exceeds the drying threshold. The second moment may be the time when the crawling robot is in the process of performing advanced drying on that pipeline segment.

[0196] Predicting the natural drying time using a natural drying model can improve the accuracy of predicting the natural drying time of downstream pipelines and increase data processing speed.

[0197] In some embodiments, the training dataset for the natural drying model includes training samples from various collection environments, and the distribution parameters of the training samples from various collection environments are determined based on historical data.

[0198] The data acquisition environment refers to the ambient temperature and humidity during the collection of training samples. The data acquisition environment can be obtained based on temperature and humidity sensors placed in the environment.

[0199] The distribution parameters of the training samples reflect the distribution of the training samples during collection. In some embodiments, the distribution parameters of the training samples include the coverage area of ​​the collection environment during collection and the proportion of training samples collected in different collection environment segments.

[0200] The environmental acquisition range can be set according to needs or experience. An environmental acquisition range can be represented as [(T1, T2), (W1, W2)], where (T1, T2) represents the temperature range and (W1, W2) represents the humidity range. For example, if a temperature gradient is set at 5°C intervals and a humidity gradient is set at 5% intervals, the environmental data collection gradient can include: [(10°C, 15°C), (50%, 55%)], [(10°C, 15°C), (55%, 60%)], [(10°C, 15°C), (65%, 70%)]...[(15°C, 20°C), (50%, 55%)], [(15°C, 20°C), (55%, 60%)], [(15°C, 20°C), (65%, 70%)]...[(20°C, 25°C), (50%, 55%)], [(20°C, 25°C), (55%, 60%)], [(20°C, 25°C), (65%, 70%)]...

[0201] In some embodiments, the gas company management platform 131 may use the area covered by historical environmental data collected during the drying process in the historical drying records as the environmental coverage area.

[0202] In some embodiments, the environmental gradient can be set based on the distribution density of historical environmental data in historical drying records. For environmental data ranges with low distribution density, a larger environmental gradient can be set. For example, conventionally, a temperature step is set every 5°C. However, since temperatures between 0°C and 20°C are relatively rare in historical data collection, the span of this environmental data range can be directly set to a temperature step every 10°C.

[0203] In some embodiments, the proportion of training sample data collected in different acquisition environment stages can be set proportionally.

[0204] By collecting training samples from various environments, we can ensure the diversity of training data and improve the model's universality in different environments.

[0205] The basic concepts have been described above. It is clear that the detailed disclosure above is merely illustrative and does not constitute a limitation of the present invention. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to the present invention by those skilled in the art. Such modifications, improvements, and corrections are suggested in this invention and therefore remain within the spirit and scope of the exemplary embodiments of the present invention.

[0206] Furthermore, specific terms are used to describe embodiments of the invention. For example, "some embodiments" refers to a particular feature, structure, or characteristic related to at least one embodiment of the invention. Therefore, it should be emphasized and noted that "some embodiments" mentioned twice or more in different locations in this invention do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the invention can be appropriately combined.

[0207] Furthermore, the order of processing elements and sequences, the use of numbers and letters, or other names described in this invention are not intended to limit the order of the processes and methods of this invention. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only. For example, while the system components described above can be implemented by hardware devices, they can also be implemented solely by software solutions, such as installing the described system on existing servers or mobile devices.

[0208] Similarly, it should be noted that, in order to simplify the description of this invention and thus aid in the understanding of one or more embodiments, the foregoing description of the embodiments of this invention sometimes combines multiple features into a single embodiment, drawing, or description thereof. In fact, the embodiments have fewer features than all the features of the single embodiment disclosed above.

[0209] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in this invention are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit preservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this invention are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0210] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this invention, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this invention, as well as documents that limit the broadest scope of this invention (currently or subsequently appended to this invention). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or terminology used in the appended materials of this invention and the content described herein, the descriptions, definitions, and / or terminology used in this invention shall prevail.

[0211] Finally, it should be understood that the embodiments described in this invention are merely illustrative of the principles of the invention. Other modifications may also fall within the scope of this invention. Therefore, alternative configurations of the embodiments of this invention are considered as examples and not limitations, and are regarded as consistent with the teachings of this invention. Accordingly, the embodiments of this invention are not limited to those explicitly described and illustrated herein.

Claims

1. A pipeline drying treatment method based on intelligent gas safety monitoring, characterized in that, The method is executed by a pipeline drying treatment IoT system based on smart gas safety monitoring, and the method includes: Through the gas company's management platform: Obtain drying medium information for at least one gas pipeline outlet, wherein the drying medium information includes the drying medium flow rate; Based on the drying medium information, the initial drying value of the gas pipeline is determined, including: Based on the drying medium information, determine the moisture discharge data in the gas pipeline; Based on the moisture discharge data, the initial drying value is determined; In response to the initial drying value of at least one gas pipeline being lower than a drying threshold, the at least one gas pipeline is designated as a target gas pipeline, and advanced drying parameters are determined based on the initial drying value of the target gas pipeline. A first control command is generated based on the advanced drying parameters, and the first control command is sent to the gas company's object platform. The gas company's object platform performs advanced drying on the target gas pipeline based on the first control command. Acquire the detection data of the target gas pipeline during advanced drying and send it to the government's gas regulatory management platform; Through the aforementioned government gas regulatory management platform: Based on the detection data during the advanced drying process, the confidence level of the initial drying value is evaluated, including: The theoretical water content in the gas pipeline is determined based on theoretical humidity and gas pipeline dimensions. The actual moisture content is determined based on the detection data; The confidence level of the initial drying value is determined based on the difference between the theoretical moisture content and the actual moisture content. Based on the confidence level of the initial dryness value, a threshold adjustment instruction is generated and sent to the gas company management platform so that the gas company management platform updates the dryness threshold. Based on the confidence level of the initial dryness value, a performance adjustment instruction is generated, wherein the performance adjustment instruction is an instruction information for adjusting the frequency at which the processor of the government gas regulatory management platform acquires and processes relevant data from different gas companies.

2. The method as described in claim 1, characterized in that, The gas pipeline includes at least one pipeline segment, the initial dryness value includes sub-dryness values ​​of the at least one pipeline segment, and the method further includes: Based on the gas pipeline information, the gas pipeline is segmented to determine the location information of at least one pipeline segment; Based on the location information of the at least one pipeline segment and the moisture discharge data, the sub-drying value of the at least one pipeline segment is determined.

3. The method as described in claim 1, characterized in that, The method further includes: Based on the detection data of the target gas pipeline, the actual dryness value of the target gas pipeline is determined; Based on the actual drying value and the initial drying value, a correction value is determined; The initial dryness value of the candidate gas pipeline is corrected based on the correction value.

4. The method as described in claim 1, characterized in that, The determination of advanced drying parameters based on the initial drying value of the target gas pipeline includes: Based on the initial dryness value and the target gas pipeline information, the moisture content in the target gas pipeline is determined; Based on the moisture content, the advanced drying parameters are determined; Obtain first detection data at the first location before advanced drying, and evaluate the first drying value of the target gas pipeline at the first location based on the first detection data; Based on the first dryness value, generate parameter adjustment instructions; The advanced drying parameters at the second position are adjusted based on the parameter adjustment command.

5. An IoT system for pipeline drying based on intelligent gas safety monitoring, characterized in that, It includes a government gas regulatory management platform, a government gas regulatory sensor network platform, a government regulatory object platform, a gas company sensor network platform, and a gas company object platform, wherein the government regulatory object platform includes a gas company management platform; The gas company management platform is configured as follows: Obtain drying medium information for at least one gas pipeline outlet, wherein the drying medium information includes the drying medium flow rate; Based on the drying medium information, the initial drying value of the gas pipeline is determined, including: Based on the drying medium information, determine the moisture discharge data in the gas pipeline; Based on the moisture discharge data, the initial drying value is determined; In response to the initial drying value of at least one gas pipeline being lower than a drying threshold, the at least one gas pipeline is designated as a target gas pipeline, and advanced drying parameters are determined based on the initial drying value of the target gas pipeline. A first control command is generated based on the advanced drying parameters, and the first control command is sent to the gas company's object platform. The gas company's object platform performs advanced drying on the target gas pipeline based on the first control command. Acquire the detection data of the target gas pipeline during advanced drying and send it to the government gas regulatory management platform; The government gas regulatory management platform is configured as follows: Based on the detection data during the advanced drying process, the confidence level of the initial drying value is evaluated, including: The theoretical water content in the gas pipeline is determined based on theoretical humidity and gas pipeline dimensions. The actual moisture content is determined based on the detection data; The confidence level of the initial drying value is determined based on the difference between the theoretical moisture content and the actual moisture content. Based on the confidence level of the initial dryness value, a threshold adjustment instruction is generated and sent to the gas company management platform so that the gas company management platform updates the dryness threshold. Based on the confidence level of the initial dryness value, a performance adjustment instruction is generated, wherein the performance adjustment instruction is an instruction information for adjusting the frequency at which the processor of the government gas regulatory management platform acquires and processes relevant data from different gas companies.

6. The system as described in claim 5, characterized in that, The gas pipeline includes at least one pipeline segment, the initial dryness value includes sub-dryness values ​​of the at least one pipeline segment, and the gas company management platform is further configured to: Based on the gas pipeline information, the gas pipeline is segmented to determine the location information of at least one pipeline segment; Based on the location information of the at least one pipeline segment and the moisture discharge data, the sub-drying value of the at least one pipeline segment is determined.

7. The system as described in claim 5, characterized in that, The gas company management platform is further configured as follows: Based on the detection data of the target gas pipeline, the actual dryness value of the target gas pipeline is determined; Based on the actual drying value and the initial drying value, a correction value is determined; The initial dryness value of the candidate gas pipeline is corrected based on the correction value.

8. The system as described in claim 5, characterized in that, The gas company management platform is further configured as follows: Based on the initial dryness value and the target gas pipeline information, the moisture content in the target gas pipeline is determined; Based on the moisture content, the advanced drying parameters are determined; Obtain first detection data at the first location before advanced drying, and evaluate the first drying value of the target gas pipeline at the first location based on the first detection data; Based on the first dryness value, generate parameter adjustment instructions; The advanced drying parameters at the second position are adjusted based on the parameter adjustment command.

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