A Method for Locating Blockage Points in Smart Gas Pipelines and an Internet of Things System

Abstract

The present invention provides a method for locating blockage points of intelligent gas pipelines and an Internet of Things system. This method is executed by the gas company management platform of the intelligent gas pipeline blockage point location Internet of Things system, and includes: determining monitoring points based on historical blockage points, where the monitoring points are located in the gas pipelines of the gas pipeline network; constructing a gas operation map based on the monitoring data corresponding to the monitoring points; determining the target blockage point based on the gas operation map; determining cleaning parameters based on the target blockage point, where the cleaning parameters include the points to be cleaned; generating a cleaning instruction based on the cleaning parameters, and sending the cleaning instruction to the cleaning robot to clean the gas pipeline corresponding to the points to be cleaned. Through the foregoing method and Internet of Things system, the blockage points can be accurately determined and the blockage can be cleared in a timely manner, effectively avoiding the formation of blockage points, thereby promoting the normal flow of gas and reducing pipeline risks.

Description

Technical Field

[0001] This specification relates to the field of pipeline monitoring, and particularly to a method for locating blockage points in intelligent gas pipelines and an Internet of Things system. Background Art

[0002] With the acceleration of the urbanization process, the scale and complexity of the gas pipeline network have been continuously increasing, and the detection and handling of pipeline blockage problems have become increasingly important. Gas pipeline blockage not only affects the normal supply of gas but also may pose safety hazards. Traditional detection methods such as manual inspection, hardware detection methods, and software detection methods can, to a certain extent, detect and handle blockage problems, but they have limitations such as low efficiency, high cost, and insufficient real-time performance. Therefore, it is urgent to develop more efficient and intelligent detection technologies to detect and clean pipelines in a timely manner.

[0003] [[ID=**11**]]Regarding the problem of pipeline blockage detection, CN103644457B proposes a method and device for locating pipeline blockage. The device mainly generates infrasound waves in the pipeline and uses the propagation characteristics of infrasound waves in the pipeline to detect and locate the blockage position. However, the infrasound wave sensor may be interfered by environmental noise, which may affect the accurate detection of the acoustic wave signal and thus the accuracy of blockage location.

[0004] Therefore, it is desired to provide a method for locating blockage points in intelligent gas pipelines and an Internet of Things system to better achieve efficient and accurate detection of gas pipeline blockage problems, thereby improving the safe operation level of urban gas pipeline networks. Summary of the Invention

[0005] The summary of the invention includes a method for locating blockage points in intelligent gas pipelines, characterized in that the method is executed by the gas company management platform of the Internet of Things system for locating blockage points in intelligent gas pipelines, and includes: determining monitoring points based on historical blockage points, where the monitoring points are located in the gas pipelines of the gas pipeline network, and the historical blockage points are determined based on historical blockage data corresponding to a preset historical period; constructing a gas operation map based on the monitoring data corresponding to the monitoring points, where the monitoring data includes at least one of pipeline pressure and gas flow rate; determining the target blockage point based on the gas operation map; determining cleaning parameters based on the target blockage point, where the cleaning parameters include the point to be cleaned; generating a cleaning instruction based on the cleaning parameters and sending the cleaning instruction to a cleaning robot to clean the gas pipeline corresponding to the point to be cleaned.

[0006] The invention content also includes an Internet of Things system for locating blockage points in intelligent gas pipelines, characterized in that the Internet of Things system includes a government safety supervision and management platform, a government safety supervision sensor network platform, a gas company management platform, a gas company sensor network platform, and an intelligent gas equipment object platform; the gas company management platform is configured to execute the aforementioned method for locating blockage points in intelligent gas pipelines.

[0007] The aforementioned method for locating blockage points in intelligent gas pipelines and the Internet of Things system can achieve the following beneficial effects, including but not limited to: Based on the Internet of Things system for locating blockage points in intelligent gas pipelines, an information operation closed-loop can be formed among various functional platforms, and coordinated and regular operation can be achieved under the unified management of the gas company management platform, realizing the informatization and intelligence of locating blockage points in intelligent gas pipelines; by determining monitoring points based on historical blockage points and collecting monitoring data to construct a gas operation map, the blockage points can be accurately determined and blocked cleaning can be carried out in a timely manner, effectively avoiding the formation of blockage points, thereby promoting the normal flow of gas and reducing pipeline risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will further illustrate in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0009] Figure 1 is a schematic diagram of the platform structure of an Internet of Things system for locating blockage points in intelligent gas pipelines shown in some embodiments of this specification;

[0010] Figure 2 is an exemplary flowchart of a method for locating blockage points in intelligent gas pipelines shown in some embodiments of this specification;

[0011] Figure 3 is a schematic diagram of determining blockage points shown in some embodiments of this specification;

[0012] Figure 4 is a flowchart of determining cleaning parameters shown in some embodiments of this specification;

[0013] Figure 5 is a schematic diagram of an effect determination model shown in some embodiments of this specification.

[0014] Explanation of the accompanying symbols: 100-smart gas pipeline blockage positioning Internet of Things system; 110-government safety supervision management platform; 120-government safety supervision sensor network platform; 130-government safety supervision object platform; 131-gas company management platform; 140-gas company sensor network platform; 150-smart gas equipment object platform; 310-gas operation map; 311-data completeness; 312-preset completeness conditions; 313-supplementary data; 314-update map; 320-candidate blockage section; 330-blockage detection result; 331-blockage prediction model; 332-estimated blockage point; 340-target blockage point; 510-cleaned data; 520-candidate configuration parameters; 530-blockage degree; 540-effect determination model; 550-estimated cleaning effect. DETAILED DESCRIPTION

[0015] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0017] Unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular and may include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps specifically identified, and these steps and elements do not constitute an exclusive list. A method or device may also include additional steps.

[0018] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0019] Figure 1It is a schematic diagram of the platform structure of an Internet of Things system for locating blockage points in intelligent gas pipelines shown in some embodiments of this specification. A blockage point refers to a point in a gas pipeline where there is a blockage. In some embodiments of this specification, a blockage point may also be referred to as a blocked point or a blockage location.

[0020] As Figure 1 shown, the Internet of Things system 100 for locating blockage points in intelligent gas pipelines may include a government safety supervision management platform 110, a government safety supervision sensing network platform 120, a gas company management platform 131, a gas company sensing network platform 140, and an intelligent gas equipment object platform 150.

[0021] The government safety supervision management platform 110 refers to a platform for supervising and managing gas pipelines. In some embodiments, the government safety supervision management platform 110 may be configured in a processor and / or a server, used to coordinate and collaborate the connections between various functional platforms, and gather all the information of the Internet of Things, providing perception management and control management functions for the Internet of Things operation system.

[0022] In some embodiments, the government safety supervision management platform 110 may perform data interaction with the government safety supervision sensing network platform 120.

[0023] The government safety supervision sensing network platform 120 refers to a functional platform for managing the sensing communication of the government. In some embodiments, the government safety supervision sensing network platform 120 may be configured as a communication network or a gateway, etc., capable of realizing the functions of sensing communication of perception information and sensing communication of control information.

[0024] In some embodiments, the government safety supervision sensing network platform 120 may interact with the government safety supervision management platform 110 upward and with the gas company management platform 131 downward. For example, the gas company management platform 131 may send relevant data on the location of blockage points in gas pipelines to the government safety supervision management platform 110 through the government safety supervision sensing network platform 120.

[0025] The government safety supervision object platform 130 refers to an object platform for generating perception information and executing control information.

[0026] In some embodiments, the government safety supervision object platform 130 may interact with the government safety supervision sensing network platform 120 upward and with the gas company sensing network platform 140 downward.

[0027] The gas company management platform 131 refers to a comprehensive management platform for the relevant information of the gas company. In some embodiments, the gas company management platform 131 may be configured to execute the method for locating blockage points in intelligent gas pipelines. For the detailed content of the method for locating blockage points in intelligent gas pipelines, please refer to the subsequent description in this specification.

[0028] In some embodiments, the gas company management platform 131 may further include a processor. The processor may process data and / or information obtained from other platforms. The processor may execute program instructions based on this data, information, and / or processing results to perform one or more functions described in this application.

[0029] The gas company sensing network platform 140 refers to an integrated management platform for the sensing information of the gas company. In some embodiments, the gas company sensing network platform 140 may be configured as a communication network or a gateway, etc., and can implement the functions of sensing communication of sensing information and sensing communication of control information.

[0030] In some embodiments, the gas company sensing network platform 140 may interact with the gas company management platform 131 upward and with the intelligent gas device object platform 150 downward. For example, the gas company management platform 131 may upload the monitoring data corresponding to the monitoring points obtained by the intelligent gas device object platform 150 to the government safety supervision and management platform 110 through the gas company sensing network platform 140.

[0031] The intelligent gas device object platform 150 refers to a function platform that executes cleaning instructions and obtains cleaning data. In some embodiments, the intelligent gas device object platform 150 may include at least one pipeline cleaning device and at least one pipeline monitoring device.

[0032] The pipeline cleaning device refers to a functional device for cleaning inside the gas pipeline. For example, a cleaning robot, etc. In some embodiments, the pipeline cleaning device may be used to clean the gas pipeline.

[0033] The pipeline monitoring device refers to a functional device for monitoring the blockage situation inside the gas pipeline. For example, a pressure sensor, a flow sensor, a blockage detector, etc. In some embodiments, the pipeline monitoring device may be used to monitor the blockage situation in the gas pipeline.

[0034] In some embodiments, the intelligent gas device object platform 150 may perform data interaction with the gas company management platform 131 through the gas company sensing network platform 140.

[0035] In some embodiments, the platforms in the intelligent gas pipeline blockage point positioning IoT system 100 may be divided into the intelligent gas first-level network and the intelligent gas second-level network. Among them, the intelligent gas first-level network refers to the network for the government users to supervise the operation of the gas pipeline network, and the intelligent gas second-level network is the network including the operation of the gas pipeline network. In some embodiments, the same platform in the intelligent gas pipeline blockage point positioning IoT system 100 may play different roles in the intelligent gas first-level network and the intelligent gas second-level network.

[0036] In some embodiments, the primary intelligent gas network may at least include a primary intelligent gas network management platform, a primary intelligent gas network sensor network platform, and a primary intelligent gas network object platform. Among them, the primary intelligent gas network management platform may include a government safety supervision management platform, the primary intelligent gas network sensor network platform may include a government safety supervision sensor network platform, and the primary intelligent gas network object platform may include a government safety supervision object platform. Among them, the government safety supervision object platform may be a gas company management platform.

[0037] In some embodiments, the secondary intelligent gas network may at least include a secondary intelligent gas network management platform, a secondary intelligent gas network sensor network platform, and a secondary intelligent gas network object platform. Among them, the secondary intelligent gas network management platform may include a gas company management platform, the secondary intelligent gas network sensor network platform may include a gas company sensor network platform, and the secondary intelligent gas network object platform may include a gas equipment object platform.

[0038] For more content on the functions performed by the intelligent gas pipeline blockage location IoT system 100, reference may be made to this specification Figures 2 - 5 and its related descriptions.

[0039] In some embodiments of this specification, based on the intelligent gas pipeline blockage location IoT system 100, an information operation closed-loop can be formed among the functional platforms, and coordinated and regular operation can be achieved under the unified management of the gas company management platform, realizing the informatization and intelligence of intelligent gas pipeline blockage location.

[0040] In some embodiments, when implementing the intelligent gas pipeline blockage location method, the gas company management platform may determine monitoring points based on historical blockage points; construct a gas operation map based on the monitoring data corresponding to the monitoring points; determine the target blockage point based on the gas operation map; determine cleaning parameters based on the target blockage point; generate a cleaning instruction based on the cleaning parameters, and send the cleaning instruction to the cleaning robot to clean the gas pipeline corresponding to the point to be cleaned.

[0041] Figure 2 is an exemplary flowchart of the intelligent gas pipeline blockage location method shown in some embodiments of this specification. As Figure 2 shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by the gas company management platform.

[0042] Step 210, determine monitoring points based on historical blockage points.

[0043] A monitoring point is a monitoring location where monitoring equipment is arranged. In some embodiments, the monitoring point may be located in a gas pipeline of a gas pipeline network. For example, at the bifurcation, junction, bend or special facilities (such as filtration facilities, pressure regulating facilities, etc.) of the gas pipeline.

[0044] In some embodiments, the gas company management platform may determine the monitoring point based on the historical blockage point set.

[0045] The historical blockage point set refers to the set of all historical blockage points within a preset historical time. Among them, the blockage point refers to the point in the gas pipeline where a blockage event occurs. For example, the blockage points may include main pipeline, pipeline branch, pipeline interface, etc. The historical blockage point is the point in the historical data where a blockage event occurs in the gas pipeline. In some embodiments, the blockage point and the historical blockage point may be represented by coordinates, where the coordinates may include at least one of geographical coordinates and custom coordinates.

[0046] In some embodiments, the historical blockage point set may be determined by the gas company management platform based on the historical blockage data corresponding to a preset historical period. Among them, the preset historical period may be set based on prior experience and / or actual needs.

[0047] The historical blockage data refers to all relevant information and data of blockage events that occurred in the intelligent gas pipeline blockage location positioning IoT system within a past period of time. For example, the historical blockage data may include the historical blockage points in the historical time.

[0048] In some embodiments, the historical blockage points can be obtained in various ways. For example, obtained through manual monitoring, obtained by turning on the pipeline crawling robot, etc.

[0049] In some embodiments, the duration of the preset historical period may be determined based on the accident characteristics of preset historical accidents in the gas pipeline.

[0050] The preset historical accident refers to an accident whose historical occurrence frequency meets the preset frequency condition. Among them, the preset frequency condition may include the highest historical occurrence frequency, or the historical occurrence frequency is higher than the frequency threshold, etc. The aforementioned frequency threshold can be determined according to prior experience.

[0051] The accident characteristic is an identifiable attribute used to characterize the gas pipeline accident. For example, the accident characteristic may include at least one of whether the blockage point is easy to monitor, the number of monitoring times, the number of accidents, and the cause of the accident.

[0052] The preset historical accidents and their corresponding accident characteristics can reflect the overall situation of the gas pipeline. For example, if the accident characteristics corresponding to the preset historical accident indicate that the accident is caused by part problems, it can be inferred that the gas pipeline corresponding to the preset historical accident is old. Another example is that if the accident characteristics corresponding to the preset historical accident show that the accident is easy to monitor, it can be determined that the gas pipeline corresponding to the preset historical accident is not prone to accident undetected situations.

[0053] In some embodiments, if the preset historical accident corresponding to the gas pipeline is easy to monitor, a shorter preset historical duration can be set for the gas pipeline to reduce the amount of data processing and thus improve the data processing efficiency; if the preset historical accident corresponding to the gas pipeline is not easy to monitor, a longer preset historical duration can be set for the gas pipeline to obtain sufficient data, so as to more accurately analyze the blockage situation of the gas pipeline.

[0054] In some embodiments, the gas company management platform can determine the monitoring points based on the cleaning frequency of each historical blockage point in the historical blockage point concentration. For example, the gas company management platform can calculate the cleaning frequency of each historical blockage point and include the points with a cleaning frequency greater than the cleaning threshold in the monitoring points. Among them, the cleaning threshold represents the critical value of the cleaning frequency and can be determined based on prior experience.

[0055] In some embodiments, the cleaning frequency of the historical blockage point is related to the number of cleaning times of the historical blockage point within the unit historical time. Exemplarily, the gas company management platform can calculate the cleaning frequency through formula (1):

[0056] F = N / T (1)

[0057] Where F is the cleaning frequency, T is the preset historical period, and N is the number of cleaning times.

[0058] In some embodiments, the gas company management platform can selectively enable the monitoring equipment. When the cleaning frequency of the historical blockage point is greater than the cleaning threshold, it is necessary to focus on monitoring this point. At this time, the gas company management platform can use the historical blockage points with a cleaning frequency greater than the cleaning threshold as monitoring points and turn on the monitoring equipment corresponding to the aforementioned historical blockage points.

[0059] By selectively enabling the monitoring equipment, the data quality of the monitoring data can be improved, the amount of invalid data can be reduced, and thus the operating efficiency of the system can be improved.

[0060] Step 220, construct a gas operation map based on the monitoring data corresponding to the monitoring points.

[0061] The gas operation map is a map used to reflect the actual positional relationship between the monitoring points, pipeline nodes, and pipelines.

[0062] In some embodiments, a gas operation map may include nodes and edges.

[0063] Among them, the nodes of the gas operation map can be used to represent the monitoring points and pipeline nodes in the gas pipeline network. A monitoring point refers to a point where monitoring equipment is installed, and a pipeline node refers to a key part of the pipeline system. Among them, the key part refers to the part in the gas pipeline network that has an obvious impact on the safe operation of the gas pipeline network, and may include but is not limited to the bifurcation, connection, turning, and special facility locations (such as filtration facilities, pressure regulating facilities, etc.) of the pipeline.

[0064] In some embodiments, the nodes of the gas operation map have node attributes. For example, for the node corresponding to the monitoring point, its node attributes may include monitoring data, which can be obtained through the monitoring equipment set at the monitoring point and may include at least one of pipeline pressure and gas flow rate; for another example, for the node corresponding to the pipeline node, its node attributes may include the type of the pipeline node, where the aforementioned type may include but is not limited to the bifurcation, connection, turning, and special facility locations (such as filtration facilities, pressure regulating facilities, etc.) of the pipeline, and can be specifically determined according to the actual situation of the pipeline node.

[0065] The edges of the gas operation map can be used to represent the gas pipelines connecting different nodes. In some embodiments, the edges of the gas operation map have edge attributes, and the edge attributes are used to characterize the features of the gas pipelines. Exemplarily, the edge attributes may include but are not limited to the pipeline length. The pipeline length can be obtained from the government safety supervision and management platform.

[0066] In some embodiments, the gas company management platform can construct a gas operation map based on the gas pipeline network, the key parts in the gas pipeline network, the monitoring points, and the monitoring data corresponding to the monitoring points. Exemplarily, the gas company management platform can use the monitoring points and pipeline nodes in the gas pipeline network as the nodes of the gas operation map, use the monitoring data corresponding to the monitoring points and the node types of the pipeline nodes as the node attributes; use the gas pipelines connecting the nodes as the edges of the gas operation map, and use the length of the gas pipelines between the nodes as the edge attributes to obtain the constructed gas operation map.

[0067] Step 230, determine the target blockage point based on the gas operation map.

[0068] The target blockage point refers to the point that needs to be focused on and cleaned up.

[0069] In some embodiments, the gas company management platform can determine the target blockage points in various ways based on the gas operation map. For example, the gas company management platform can determine the nodes in the gas operation map that meet the preset conditions as the target blockage points. Among them, the preset conditions can include that there are corresponding monitoring data for the nodes, and the pipeline pressure of the pipeline where the nodes are located is greater than the pressure threshold and the gas flow rate is less than the flow rate threshold. The pressure threshold and the flow rate threshold can be determined based on prior experience.

[0070] In some embodiments, the gas company management platform can also determine the candidate blockage sections based on the gas operation map, obtain the blockage detection results of the candidate blockage sections, and determine the target blockage points based on the blockage detection results. For more detailed descriptions, reference can be made to this specification Figure 3 and its related descriptions.

[0071] Step 240, determine the cleaning parameters based on the target blockage points.

[0072] In some embodiments, the cleaning parameters are the parameters that measure the cleaning performance of the cleaning robot when cleaning the pipeline.

[0073] In some embodiments, the cleaning parameters can include the points to be cleaned. The points to be cleaned refer to the positions in the gas pipeline that need to be cleaned.

[0074] In some embodiments, the cleaning parameters can also include the moving speed, cleaning intensity, cleaning tools, etc. of the cleaning robot. In some embodiments, the gas company management platform can divide the cleaning parameters into different categories according to the corresponding parameter items of the cleaning parameters. For example, the cleaning parameters can be divided into moving parameters, configuration parameters, etc. Among them, the moving parameters can include the points to be cleaned and the moving speed, and the configuration parameters can include the cleaning intensity and cleaning tools. The aforementioned moving parameters and configuration parameters can also include other types of parameters, which can be determined according to the actual situation of the cleaning robot.

[0075] For more detailed descriptions of the moving parameters, configuration parameters, moving speed, cleaning intensity, and cleaning tools, reference can be made to Figure 4 and related descriptions.

[0076] In some embodiments, the gas company management platform can determine the cleaning parameters according to the target blockage points. For example, the gas company management platform can use the target blockage points as the points to be cleaned, thereby determining the cleaning parameters.

[0077] In some embodiments, the gas company management platform can determine the moving parameters based on the points to be cleaned and the moving speed, and determine the configuration parameters based on the cleaning data.

[0078] For a detailed description of data cleaning, as well as more details on how to determine movement parameters, configuration parameters, movement speed, cleaning intensity, and cleaning tools, see Figure 4 and related descriptions.

[0079] Step 250: Generate a cleaning instruction based on the cleaning parameters and send the cleaning instruction to the cleaning robot to clean the gas pipeline where the point to be cleaned is located.

[0080] A cleaning instruction refers to a series of specific operation commands issued from the gas company management platform during pipeline cleaning operations, used to guide the cleaning robot or cleaning equipment to perform specific cleaning tasks. In some embodiments, the cleaning instruction may include cleaning parameters. In some embodiments, according to the specific content of the cleaning instruction, the cleaning instruction can be divided into a movement instruction and a configuration instruction. Among them, the movement instruction is used to control the movement of the cleaning robot; the configuration instruction is used to control the cleaning intensity of the cleaning robot and the cleaning tools it configures. For more descriptions of the cleaning instruction, see Figure 4 and related descriptions.

[0081] In some embodiments, the gas company management platform can generate a cleaning instruction based on the cleaning parameters. For example, the gas company management platform can generate a cleaning instruction containing cleaning parameters, such as cleaning the point to be cleaned in the gas pipeline, etc.

[0082] In some embodiments, the gas company management platform can transmit the cleaning instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the cleaning robot in the intelligent gas equipment object platform to clean the gas pipeline according to the cleaning parameters.

[0083] Due to reasons such as gas impurities, pipeline corrosion, and foreign objects entering, the gas pipeline will form accumulated impurities, thus forming blockage points, affecting the normal flow of gas. In some embodiments of this specification, the gas company management platform determines monitoring points through historical blockage points, collects monitoring data to construct a gas operation map, can accurately determine the blockage points, and promptly clean the blockages, effectively avoiding the formation of blockage points, thereby promoting the normal flow of gas and reducing pipeline risks.

[0084] It should be noted that the above description of process 200 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0085] Figure 3 is a schematic diagram of determining the blockage point shown in some embodiments of this specification.

[0086] AsFigure 3 As shown, in some embodiments, the gas company management platform may determine a candidate blockage section 320 based on the gas operation map 310; obtain the blockage detection result 330 of the candidate blockage section 320; and determine the target blockage point 340 based on the blockage detection result 330.

[0087] A candidate blockage section refers to a pipeline section where a blockage point may exist. In some embodiments, a candidate blockage section may include at least one pipeline section where a blockage point may exist.

[0088] A candidate blockage section can be determined in various ways. In some embodiments, the gas company management platform may determine a candidate blockage section based on the gas operation map. For example, the gas company management platform may determine the pipeline where the node satisfying the preset conditions is located, and the upstream and downstream gas pipelines of the node as the candidate blockage interval. Among them, the preset conditions may include that there is corresponding monitoring data in the stage, and the pipeline pressure of the gas pipeline where the node is located is greater than the pressure threshold, and the gas flow rate is less than the flow rate threshold.

[0089] In some embodiments, the gas company management platform may also evaluate the data completeness 311 of the gas operation map 310. In response to the data completeness 311 not meeting the preset completeness condition 312: determine a data request, and send the data request to the government safety supervision management platform to obtain supplementary data 313; update the gas operation map based on the acquisition situation of the supplementary data to obtain an updated map 314; and determine the candidate blockage section 320 based on the updated map 314.

[0090] In some cases, due to data permissions, untimely data collection, or other possible reasons, the government safety supervision management platform cannot send all the data required to construct the gas operation map to the gas company management platform in a timely manner. At this time, the gas company management platform may first construct the gas operation map based on the obtained data, and judge whether it is necessary to further obtain data from the government safety supervision management platform by evaluating the data completeness.

[0091] Data completeness is a numerical value representing the perfection degree of the attribute data corresponding to the nodes in the gas operation map. The higher the data completeness, the more perfect the attribute data corresponding to the nodes in the gas operation map.

[0092] In some embodiments, data completeness may include the completeness of the detection data corresponding to the monitoring points in the gas operation map. Exemplarily, the gas company management platform may evaluate the data completeness of the gas operation map based on the number of nodes with monitoring data in the gas operation map and the total number of nodes representing the monitoring points in the gas operation map. The more the number of nodes with monitoring data, the higher the data completeness. Exemplarily, the data completeness is determined based on the percentage of the number of nodes with monitoring data in the total number of nodes representing the monitoring points.

[0093] In some embodiments, the preset completeness condition may be that the data completeness is not less than the completeness threshold.

[0094] The completeness threshold is a value representing the lowest standard of data completeness. In some embodiments, the completeness threshold may be set based on prior experience and / or actual requirements.

[0095] In some embodiments, the completeness threshold is positively correlated with the area of the region where the gas pipeline network is located. For example, the larger the floor area of the region where the gas pipeline network is located, the higher the completeness threshold. In some embodiments, the area of the region where the gas pipeline network is located may be obtained by the gas company management platform from the government safety supervision management platform through the government safety supervision sensor network platform.

[0096] Since the larger the regional area, the wider the scope of the pipelines in the region, by setting a completeness threshold that is positively correlated with the regional area, the comprehensiveness of the data can be improved, thereby more accurately judging the blockage condition of the gas pipeline network.

[0097] In some embodiments, in response to the data completeness of the gas operation map not meeting the preset completeness condition, the gas company management platform may determine a data request and send the data request to the government safety supervision management platform through the government safety supervision sensor network platform.

[0098] The data request is information for applying for supplementary data.

[0099] The supplementary data is data for supplementing the missing node attributes in the gas operation map. In some embodiments, the supplementary data can be used to improve the data completeness of the existing monitoring data.

[0100] In some embodiments, the gas company management platform may send the data request to the government safety supervision management platform through the government safety supervision sensor network platform to request the acquisition of supplementary data.

[0101] In some embodiments, the gas company management platform may update the gas operation map based on the acquisition situation of the supplementary data to obtain an updated map.

[0102] An updated map refers to a new gas operation map obtained by improving the missing node attributes in the gas operation map. Among them, the missing node attributes refer to the missing monitoring data corresponding to the nodes of the monitoring points in the gas operation map.

[0103] In some embodiments, in response to obtaining supplementary data, the gas company management platform can update the gas operation map based on the supplementary data to obtain the updated gas operation map. For example, the gas company management platform can supplement the missing node attributes in the gas operation map based on the supplementary data. In response to the data completeness of the gas operation map after data supplementation meeting the preset completeness condition, the gas company management platform can determine the gas operation map after data supplementation as the updated map.

[0104] In some embodiments, in response to the data completeness of the gas operation map after data supplementation still not meeting the preset completeness condition, the gas company management platform can process the gas operation map after data supplementation through a preset method to determine the estimated value of the missing monitoring data, and further improve the gas operation map based on the estimated value to obtain the updated map. Among them, the aforementioned preset method can be the interpolation method. For a detailed description of the interpolation method, please refer to the relevant description below.

[0105] In some embodiments, in response to not obtaining supplementary data, the gas company management platform can determine the estimated monitoring data based on the gas operation map; update the gas operation map based on the estimated monitoring data to obtain the updated gas operation map.

[0106] The estimated monitoring data is the estimated value of the missing monitoring data in the gas operation map. For example, in the nodes of the gas operation map, the estimated monitoring data can include one of the monitoring nodes and pipeline nodes.

[0107] In some embodiments, the gas company management platform can determine the estimated monitoring data based on the gas operation map through a preset method. Among them, the preset algorithm can be the interpolation method.

[0108] In some embodiments, determining the estimated monitoring data by the interpolation method can include the following: The gas company management platform can obtain the actual position coordinates of the nodes in the gas operation map, determine multiple discrete points based on the nodes with monitoring data, and perform interpolation calculation based on the multiple discrete points to obtain the estimated monitoring data corresponding to the nodes without monitoring data.

[0109] The types of interpolation methods can include multiple types. For example, it includes at least one of the Lagrange algorithm and Newton interpolation, and can also be other algorithms that can implement interpolation calculation.

[0110] In some embodiments, in response to obtaining the clogging data of at least one clogging point, the gas company management platform may update the gas operation map.

[0111] The clogging data is data characterizing the composition of the clogging material, which may include the composition information of at least one component causing the clogging and the proportion of each component.

[0112] In some embodiments, the clogging point may be a node in the gas operation map or a point not belonging to the gas operation map. In response to the aforementioned clogging point being a node in the gas operation map, the gas company management platform may add the corresponding clogging data as a new node attribute to the gas operation map; in response to the aforementioned clogging point being a point not belonging to the gas operation map, the gas company management platform may add the clogging point as a supplementary node to the gas operation map, determine the connection relationship between the supplementary node and other nodes based on the gas pipeline where the clogging point is located, and then determine the new edges between the supplementary node and the original nodes in the gas operation map. Among them, the node attribute corresponding to the supplementary node is the clogging data corresponding to the clogging point, and the edge feature corresponding to the new edge is the length of the gas pipeline corresponding to the new edge.

[0113] Based on the clogging points and their corresponding clogging data in the gas pipeline, expanding the nodes and node attributes in the gas operation map can obtain a gas operation map with richer data, so as to obtain a wider and more accurate determination result of the candidate clogging interval.

[0114] In some embodiments, in response to the data completeness of the gas operation map meeting the preset completeness condition, the gas company management platform may determine the candidate clogging section based on the gas operation map; in response to the data completeness of the gas operation map not meeting the preset completeness condition, the gas company management platform may determine the candidate clogging section based on the updated map.

[0115] In some embodiments, the gas company management platform may determine the candidate clogging section based on the node attributes of each node in the gas operation map or the updated map. For example, the gas company management platform may determine the target nodes as the nodes with monitoring data, and the pipeline pressure greater than the pressure threshold and the gas flow rate less than the flow rate threshold, and determine the target nodes and their upstream and downstream pipelines as the candidate clogging sections.

[0116] Since the gas operation map before the update is incomplete, the gas company management platform judges whether the data of the gas operation map before the update is perfect by evaluating the data completeness. In some embodiments of this specification, the gas operation map with imperfect data is supplemented with data through the gas operation map to obtain a more perfect updated map; determining the candidate clogging section in the gas pipeline based on the updated map can obtain a wider and more accurate result.

[0117] In some embodiments, the gas company management platform may obtain the blockage detection results of the candidate blockage sections.

[0118] The blockage detection results are data sequences used to reflect the blockage conditions in the candidate blockage sections. In some embodiments, the blockage detection results may include the acoustic wave reflection duration sequences at at least one position in the candidate blockage intervals.

[0119] In some embodiments, the gas company management platform may control an acoustic wave transmitting device outside the candidate blockage section to emit an acoustic wave signal, and use a receiving device to capture and analyze the characteristic changes such as reflection, scattering, or attenuation of the acoustic wave when propagating inside the pipeline, so as to obtain the blockage detection results.

[0120] In some embodiments, the gas company management platform may determine the target blockage points based on the blockage detection results. In some embodiments, for each blockage detection result, the gas company management platform may calculate the gradient of adjacent acoustic wave reflection durations in its acoustic wave reflection duration sequence, and take the position corresponding to the point with the largest gradient change as the target blockage point of the candidate blockage section.

[0121] In some embodiments, the target blockage points may further include estimated blockage points where blockages may occur in the future.

[0122] In some embodiments, the gas company management platform may determine the estimated blockage points 332 within a future period of time based on the blockage detection results 330 through the blockage prediction model 331.

[0123] The blockage prediction model refers to a model used to determine the estimated blockage points and their corresponding blockage degrees within a future period of time. In some embodiments, the blockage prediction model may be a machine learning model. For example, a Deep Neural Networks (DNN) model, etc.

[0124] In some embodiments, the input of the blockage prediction model may include the blockage detection results, and the output of the blockage prediction model may include the estimated blockage points in the gas pipeline network within a future period of time. Among them, the future period of time can be set according to actual needs. For a detailed description of the blockage detection results, please refer to the relevant description in this specification Figure 2 in the relevant description.

[0125] The estimated blockage points refer to at least one point in the gas pipeline network where blockages may occur within a future period of time.

[0126] The blockage degree refers to the proportion of the space occupied by the blockage in the gas pipeline. In some embodiments, the blockage degree can be represented by a numerical value, and the larger the numerical value, the higher the blockage degree.

[0127] In some embodiments, the blockage prediction model can be obtained by training an initial prediction model based on a large number of first training samples with first labels. The gas company management platform can input multiple first training samples with first labels into the initial prediction model, construct a loss function through the first labels and the output results of the initial prediction model, and iteratively update the initial prediction model based on the loss function. When the end condition is met, the training is completed, and a trained blockage prediction model is obtained. Among them, the preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0128] In some embodiments, the first training sample can be the sample detection result of the sample blockage section at the historical first time in the historical data. The sample detection result refers to the blockage detection result in the historical data.

[0129] In some embodiments, the first label can be the historical blockage point detected in the sample blockage section at the historical second time in the historical data. Among them, the historical second time is later than the historical first time.

[0130] In some embodiments of this specification, through the blockage prediction model, the pattern of blockage occurrence can be learned from historical data, thereby improving the accuracy of predicting future blockage events, facilitating the advance regulation of possible blockage points, and reducing the possibility of accidents.

[0131] In some embodiments of this specification, the gas company management platform uses a gas operation map to preliminarily evaluate the sections that may be blocked. By further detecting the internal conditions of the sections that may be blocked, more accurate results can be obtained, optimizing the maintenance work.

[0132] Figure 4 It is a flowchart for determining cleaning parameters according to some embodiments of this specification. As Figure 4 shown, process 400 includes the following steps 410-step 450. In some embodiments, process 400 can be executed by the gas company management platform.

[0133] In some embodiments, the processor can determine movement parameters based on the point to be cleaned and the movement speed, and control the cleaning robot to clean the point to be cleaned based on the movement parameters; in response to the cleaning robot reaching the point to be cleaned, control the cleaning robot to perform preliminary cleaning according to the preset configuration; in response to obtaining the cleaning data fed back by the cleaning robot, determine the configuration parameters based on the cleaning data; generate a configuration update instruction based on the configuration parameters, and send the configuration update instruction to the cleaning robot.

[0134] In some embodiments, controlling a cleaning robot to clean a point to be cleaned based on movement parameters may include: sending the movement parameters to a government safety supervision and management platform; in response to obtaining a confirmation cleaning instruction from the government safety supervision and management platform, generating a movement instruction based on the movement parameters and sending it to the cleaning robot to control the cleaning robot to clean the point to be cleaned.

[0135] Step 410, determining the movement parameters of the cleaning robot based on the point to be cleaned and the movement speed, and sending the movement parameters to the government safety supervision and management platform.

[0136] The movement parameters are parameters used to characterize the movement characteristics of the cleaning robot. In some embodiments, the movement parameters may include the point to be cleaned and the movement speed. For a detailed description of the point to be cleaned and its determination, reference can be made to Figure 2 the relevant descriptions in

[0137] The movement speed refers to the movement speed of the cleaning robot in the gas pipeline network during the cleaning operation.

[0138] In some embodiments, the gas company management platform may determine the movement speed of the cleaning robot based on prior experience or actual requirements.

[0139] In some embodiments, the movement speed may be related to the distribution sparsity of the points to be cleaned in the area where the point to be cleaned is located. The greater the distribution sparsity, the slower the movement speed of the cleaning robot in this area.

[0140] The distribution sparsity refers to the degree of sparsity of the distribution of the points to be cleaned. In some embodiments, the distribution sparsity can be represented by a numerical value. The larger the numerical value, the sparser the distribution of the points to be cleaned, and the smaller the numerical value, the denser the distribution of the points to be cleaned.

[0141] In some embodiments, the gas company management platform may determine the distribution sparsity based on the point distance between the points to be cleaned. For example, the gas company management platform may count the point distances between every two points to be cleaned among multiple points to be cleaned, and calculate the mean value based on all the point distances corresponding to the multiple points to be cleaned. The calculated mean value is the distribution sparsity.

[0142] In some embodiments of this specification, evaluating the movement speed based on the distribution sparsity is beneficial to improving the cleaning efficiency. The greater the distribution sparsity, the more dispersed the points to be cleaned are, and the higher the probability of blockage between the points to be cleaned. Appropriately reducing the movement speed of the cleaning robot based on this larger distribution sparsity can enable the cleaning robot to clean more carefully between two points to be cleaned.

[0143] In some embodiments, the processor of the gas company management platform may determine the movement parameters of the cleaning robot based on the points to be cleaned and the moving speed. For example, the gas company management platform may directly use the determined points to be cleaned and the moving speed as the movement parameters.

[0144] In some embodiments, the gas company management platform may send the movement parameters to the intelligent gas device object platform through the gas company sensing network platform. For more descriptions of the gas company management platform and the intelligent gas device object platform, reference can be made to Figure 1 the relevant descriptions.

[0145] Step 420: In response to obtaining a confirmation cleaning instruction from the government safety supervision and management platform, generate a movement instruction based on the movement parameters, and send the movement instruction to the cleaning robot to control the cleaning robot to clean the points to be cleaned.

[0146] The confirmation cleaning instruction refers to an instruction for confirming the cleaning of the points to be cleaned.

[0147] In some embodiments, the gas company management platform may obtain the confirmation cleaning instruction from the government safety supervision and management platform through the government safety supervision sensing network platform. For more descriptions of the government safety supervision and management platform and the government safety supervision sensing network platform, reference can be made to Figure 1 the relevant descriptions.

[0148] The movement instruction refers to an instruction for controlling the movement of the cleaning robot.

[0149] In some embodiments, in response to obtaining a confirmation cleaning instruction from the government safety supervision and management platform, the gas company management platform may generate a movement instruction based on the movement parameters. For example, the processor generates a movement instruction based on the movement parameters through path planning technology. The movement instruction may include at least one point to be cleaned that the cleaning robot needs to reach, the sequence of cleaning at least one point to be cleaned, and the moving speed from the current point to be cleaned to the next point to be cleaned, etc.

[0150] In some embodiments, the gas company management platform may send the movement instruction to the intelligent gas device object platform through the gas company sensing network platform to control the cleaning robot in the intelligent gas device object platform to reach the points to be cleaned based on the foregoing movement instruction, so as to clean the blocked positions in the gas pipeline network. For more descriptions of the intelligent gas device object platform, reference can be made to Figure 1 the relevant descriptions.

[0151] Step 430: In response to the cleaning robot reaching the points to be cleaned, control the cleaning robot to perform a preliminary cleaning according to the preset configuration.

[0152] The preset configuration refers to the cleaning configuration of the cleaning robot set in advance. In some embodiments, the preset configuration may include at least one of a preset cleaning intensity and a preset cleaning tool. The preset cleaning intensity refers to the intensity of cleaning set in advance, and the preset cleaning tool refers to the tool for cleaning set in advance.

[0153] In some embodiments, the intensity of cleaning can be represented by a value from 1 to 10, and the larger the value, the higher the preset cleaning intensity. In some embodiments, the preset cleaning tool may include one or more of, but is not limited to, a brush, a scraper, a high-pressure nozzle, etc.

[0154] In some embodiments, the preset configuration can be set based on prior experience.

[0155] The preliminary cleaning refers to the first round of cleaning of the cleaning robot for the point to be cleaned.

[0156] In some embodiments, the gas company management platform can send the preset configuration to the intelligent gas equipment object platform through the gas company sensing network platform, so as to control the cleaning robot in the intelligent gas equipment object platform to perform preliminary cleaning on the blocked positions in the gas pipeline network according to the preset configuration.

[0157] Step 440, in response to obtaining the cleaning data fed back by the cleaning robot, determine the configuration parameters based on the cleaning data.

[0158] The cleaning data refers to the data about the blockage situation fed back by the cleaning robot after the first round of cleaning of the point to be cleaned. In some embodiments, the cleaning data may include the component distribution of the blockage at the point to be cleaned.

[0159] In some embodiments, the gas company management platform can analyze the components of the cleaned blockage to determine the cleaning data. The blockage refers to the object blocking the gas pipeline.

[0160] The configuration parameter is a parameter characterizing the configuration of the cleaning robot. In some embodiments, the configuration parameter may include at least one of the cleaning intensity and the cleaning tool. The cleaning intensity can be represented by a value, and the larger the value, the greater the cleaning intensity. The types of cleaning tools are similar to the preset cleaning tools, and reference can be made to the relevant descriptions above.

[0161] In some embodiments, in response to obtaining the cleaning data fed back by the cleaning robot, the gas company management platform can determine the configuration parameters based on the cleaning data in various ways.

[0162] Exemplarily, the gas company management platform can determine the configuration parameters of the cleaning robot by matching in the reference configuration database based on the cleaning data.

[0163] In some embodiments, the gas company management platform may determine candidate reference data based on the historical data of the gas pipeline network, and screen the candidate reference data according to preset screening criteria to determine the reference data. Among them, the candidate reference data may include at least one piece of data characterizing the historical cleaning situation, and the reference data includes at least one piece of data characterizing the historical cleaning situation after screening. Each piece of data may include the historical blockage degree after historical cleaning, historical cleaning data, historical configuration parameters, and the historical blockage degree after cleaning.

[0164] The preset screening criteria may be that the change rate of the blockage degree is greater than the change rate threshold. Among them, the change rate of the blockage degree may be determined based on the ratio of the change value of the blockage degree of the point to be cleaned to the initial blockage degree before cleaning. The change value of the blockage degree is determined based on the difference between the initial blockage degree before cleaning and the historical blockage degree after cleaning. The change rate threshold may be determined based on prior experience.

[0165] In some embodiments, the gas company management platform may construct at least one vector to be clustered based on the historical cleaning data and its corresponding historical configuration parameters in the reference data, cluster the at least one vector to be clustered, and obtain a preset number of cluster centers. The gas company management platform may determine the historical cleaning data corresponding to the cluster center as the reference cleaning data, and determine the historical configuration parameters corresponding to the cluster center as the reference configuration parameters. Among them, the preset number may be set based on prior experience and / or actual requirements.

[0166] In some embodiments, the gas company management platform may construct a reference configuration database based on the reference cleaning data and the reference configuration parameters, and perform matching in the reference configuration database based on the cleaning data and the feedback of the cleaner to obtain the reference cleaning data with the highest similarity to the cleaning data, and determine the reference configuration parameters corresponding to the reference cleaning data as the configuration parameters corresponding to the cleaning data.

[0167] In some embodiments, the gas company management platform may also obtain candidate configuration parameters based on the historical cleaning data; determine the estimated cleaning effect of the candidate configuration parameters based on the cleaning data; and determine the candidate configuration parameters whose estimated cleaning effect meets the preset cleaning target as the configuration parameters.

[0168] Historical data refers to the relevant data when the cleaning robot performs historical cleaning operations. In some embodiments, the historical data may include at least one piece of data characterizing the historical cleaning situation. Each piece of data may include the historical blockage degree after historical cleaning, historical cleaning data, historical configuration parameters, and the historical blockage degree after cleaning.

[0169] Candidate configuration parameters refer to alternative configuration parameters.

[0170] In some embodiments, the gas company management platform may screen historical data according to a preset screening criterion to obtain at least one piece of alternative data, and determine the historical cleaning data with the number of occurrences greater than the frequency threshold in the alternative data as candidate configuration parameters. For a detailed description of the preset screening criterion, refer to the foregoing description.

[0171] The estimated cleaning effect refers to the estimated effect after cleaning. In some embodiments, the estimated cleaning effect may be represented by a value from 1 to 10, and the larger the value, the better the estimated cleaning effect.

[0172] In some embodiments, the gas company management platform may determine the estimated cleaning effect corresponding to the candidate configuration parameters in various ways.

[0173] Exemplarily, the processor may determine the estimated cleaning effect by querying a cleaning effect evaluation table based on the cleaning data and the candidate configuration parameters. The cleaning effect evaluation table includes at least one piece of reference evaluation data, and each piece of reference evaluation data includes reference cleaning data, reference configuration parameters, and their corresponding reference cleaning effects.

[0174] In some embodiments, the cleaning effect evaluation table may be constructed based on reference data. Exemplarily, the gas company management platform may construct a cleaning vector based on the historical cleaning data, historical configuration parameters, and clogging degree change rate in the reference data, and perform clustering on the cleaning vector to form a preset number of clustering centers. The historical cleaning data and historical configuration parameters corresponding to the clustering centers are determined as the reference cleaning data and reference configuration parameters, and the clogging degree change rate corresponding to the clustering centers is determined as the corresponding reference cleaning effect. For the acquisition of reference data, refer to the foregoing related description.

[0175] In some embodiments, the gas company management platform may query in the cleaning effect evaluation table based on the cleaning parameters and the candidate configuration parameters to determine the reference cleaning data and reference configuration parameters that are the closest, and determine the reference cleaning effect corresponding to the reference cleaning data and reference configuration parameters as the estimated cleaning effect corresponding to the candidate configuration parameters.

[0176] In some embodiments, the gas company management platform may also determine the estimated cleaning effect of the candidate configuration parameters based on the cleaning data through an effect determination model. For a detailed description of the effect determination model, refer to this specification Figure 5 and its related descriptions.

[0177] In some embodiments, the gas company management platform may determine the configuration parameters based on the estimated cleaning effect of the candidate configuration parameters. For example, the gas company management platform may determine the candidate configuration parameters whose estimated cleaning effect meets the preset cleaning target as the configuration parameters.

[0178] The preset cleaning target refers to the preset expected cleaning effect. In some embodiments, the preset cleaning target can be represented by a change rate threshold. When the change in the degree of blockage is greater than the change rate threshold, it can be considered that the preset cleaning target is achieved. This change rate threshold represents the minimum expected value of the change rate of the blockage degree of the gas pipeline and / or the object to be cleaned. In some embodiments, the preset cleaning target can be set based on prior experience and / or actual requirements.

[0179] In some embodiments, the gas company management platform determines the candidate configuration parameters whose estimated cleaning effect is not less than the change rate threshold as the configuration parameters.

[0180] In some embodiments of this specification, based on historical cleaning data, candidate configuration parameters are determined, the cleaning effect is determined based on the cleaning parameters and the candidate configuration parameters, and then the configuration parameters are further determined, which is beneficial to selecting the configuration parameters with the best effect, and then better ensuring the cleaning effect of the cleaning robot.

[0181] Step 450, generate a configuration update instruction based on the configuration parameters, and send the configuration update instruction to the cleaning robot.

[0182] The configuration update instruction refers to the instruction for updating the configuration of the cleaning robot.

[0183] In some embodiments, the processor can generate a corresponding configuration update instruction based on the configuration parameters to control the cleaning robot to update the relevant configuration.

[0184] In some embodiments, the processor can send a configuration update instruction based on the intelligent gas device object platform. For more descriptions about the intelligent gas device object platform, reference can be made to Figure 1 Related descriptions.

[0185] In some embodiments of this specification, based on the cleaning point and the moving speed, the moving parameters are determined, a moving instruction is generated based on the moving parameters to control the cleaning robot to reach the point to be cleaned, and the cleaning robot is controlled to perform preliminary cleaning according to the preset configuration. Based on the cleaning data fed back by the cleaning robot, the configuration parameters are determined, and then the configuration update instruction is further determined, which is beneficial to ensuring the compliance and safety of the cleaning process, beneficial to determining the appropriate configuration update instruction for pipeline cleaning, ensuring the use of the best cleaning tools and cleaning loudness, reducing the unnecessary cleaning times of the cleaning robot, and thus avoiding damage to the gas pipeline while maintaining energy conservation and high efficiency.

[0186] It should be noted that the above descriptions of Process 200 and Process 400 are only for illustration and explanation, and do not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to Process 200 and Process 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0187] Figure 5 It is a schematic diagram of a model determined according to the effects shown in some embodiments of this specification.

[0188] In some embodiments, the gas company management platform can determine the estimated cleaning effect of candidate configuration parameters through an effect determination model based on the cleaned data.

[0189] For more information about the cleaned data, candidate configuration parameters, and estimated cleaning effect, refer to Figure 2 and Figure 4 the relevant descriptions.

[0190] The effect determination model refers to a model used to determine the estimated cleaning effect. In some embodiments, the effect determination model can be a machine learning model. For example, a deep neural network (DNN), a graph neural network (GNN), etc.

[0191] In some embodiments, as Figure 5 shown, the input of the effect determination model 540 can include the cleaned data 510 and the candidate configuration parameters 520, and the output can be the estimated cleaning effect 550.

[0192] In some embodiments, the effect determination model can be obtained through training in various ways. For example, the effect determination model can be obtained by training an initial determination model with multiple groups of training samples with training labels. A group of training samples for training the effect determination model can include sample cleaned data and sample configuration parameters, and the training label corresponding to the training sample is the historical cleaning effect when cleaning is performed based on the sample cleaned data and sample configuration parameters.

[0193] In some embodiments, the training samples and their corresponding training labels can be obtained based on historical data. For example, obtain the historical cleaned data and historical configuration parameters corresponding to at least one cleaning operation in the historical data, and determine the historical cleaning effect based on the difference between the degree of blockage before cleaning and the degree of blockage after cleaning. The gas company management platform can determine the historical cleaned data and historical configuration parameters as the sample cleaned data and sample configuration parameters, and determine the historical cleaning effect as the training label. For the description of the degree of blockage, refer to the relevant descriptions above.

[0194] In some embodiments, the gas company management platform can perform multiple rounds of iterative training on the initial effect determination model based on multiple groups of training samples with training labels. The training process is similar to the training process of the blockage prediction model, and reference can be made to Figure 3 the relevant descriptions in.

[0195] In some embodiments, as Figure 5As shown, the input of the effect determination model 540 may further include the estimated blockage points 332 and the corresponding blockage degrees 530 within a future period of time.

[0196] For more information about the estimated blockage points and blockage degrees, reference can be made to Figure 3 the relevant descriptions in

[0197] In some embodiments, the training samples for training the effect determination model may further include sample blockage points and sample blockage degrees, and the sample blockage points and sample blockage degrees can be obtained based on historical data.

[0198] In some embodiments, the gas company management platform may train the effect determination model based on training samples including sample blockage points, sample blockage degrees, sample cleaning data, and sample candidate configuration parameters. For the training method of the effect determination model, reference can be made to the relevant descriptions above.

[0199] In some embodiments of this specification, taking the blockage points and the large blockage degree as the input of the effect determination model can consider the generation rate of the blockage in the gas pipeline, thereby improving the accuracy of the effect determination model in determining the cleaning effect, providing more accurate reference data for subsequent cleaning operations, and making the cleaning more effective.

[0200] In some embodiments, the gas company management platform may split the sample data set according to a preset ratio to obtain a training set, a validation set, and a test set; use the training set, the validation set, and the test set to train the initial effect determination model to obtain the effect determination model.

[0201] The preset ratio refers to the ratio of the training set, the validation set, and the test set to the sample data set preset in advance. For example, the ratio of the training set, the validation set, and the test set to the sample data set can be 8:1:1.

[0202] In some embodiments, the preset ratio can be preset by the gas company management platform based on default settings or prior experience.

[0203] The sample data set is historical data that can be used as training samples. In some embodiments, the sample data set may include the historical cleaning data and historical configuration parameters corresponding to the gas pipeline within at least one historical time period.

[0204] In some embodiments, the sample data set can be extracted by the gas company management platform from the storage device based on default settings or prior experience.

[0205] In some embodiments, the processor splits the sample data set according to the preset ratio to obtain a training set, a validation set, and a test set.

[0206] The splitting method may include sampling statistics, which may include but are not limited to random sampling, stratified sampling, etc. In some embodiments, the gas company management platform may also split the sample data set in other ways.

[0207] The training set refers to the data set used to train the effect determination model.

[0208] The test set refers to the data set used to evaluate the performance of the effect determination model.

[0209] The validation set refers to the data set used to select the best effect determination model.

[0210] There is no data intersection among the training set, validation set, and test set obtained by splitting, that is, there is no duplicate data between any two of the training set, validation set, and test set.

[0211] In some embodiments, the gas company management platform may train the initial effect determination model based on the training set, validation set, and test set to obtain the effect determination model. The training process includes multiple stages of training. Among them, one stage of training includes: inputting the training set into the initial effect determination model, constructing a loss function based on the training labels and the output of the initial effect determination model, and updating the parameters of the initial effect determination model through multiple rounds of iteration based on the loss function; during the aforementioned training process, based on a preset validation frequency, the trained initial effect determination model is validated through the validation set, and the initial learning rate or the learning rate during the training process of the initial effect determination model after this round of training is adjusted based on the validation result. Multiple strategies can be used to adjust the learning rate, such as one or more of the learning rate decay strategy, learning rate warm-up, cyclic learning rate, and using an adaptive learning rate adjustment algorithm, etc.

[0212] Among them, the validation frequency refers to the frequency of validating the trained initial effect determination model. For example, the model is validated every n rounds of training; in response to the validation condition being met, an intermediate model is obtained, and the intermediate model is tested through the test set to evaluate the performance of the intermediate model obtained from this stage of training. Among them, the intermediate model refers to the effect determination model obtained through stage training, and the performance of the intermediate model can refer to the accuracy rate of the output of the intermediate model. The validation condition refers to the condition for validating the current training result, which may include one or more of the number of iterations reaching a threshold, the loss function converging, and the value of the loss function being less than a preset threshold. Multiple stages of training are performed, and the effect determination model with the best performance is used as the trained effect determination model.

[0213] The learning rate is a parameter that controls the step size during the model training process when updating the model parameters. It determines the amplitude of updating the parameters along the direction of the fastest descent of the loss function during the training of the initial effect determination model through gradient descent (or other optimization algorithms).

[0214] In some embodiments, different initial learning rates can be adopted when training the model using different sample data sets, and the initial learning rate of each sample data set is related to the sample statistical differences of the sample data set. In some embodiments, the gas company management platform can determine the initial learning rate of the effect determination model based on the sample statistical differences. For example, the greater the sample statistical differences, the smaller the initial learning rate.

[0215] Generally, the greater the sample statistical differences, the higher the uncertainty of the results of pipeline cleaning, and the greater the influence of potential factors. Therefore, for such samples, a smaller learning rate should be determined to better discover the hidden laws in the samples.

[0216] The sample statistical differences refer to the degree of differences among samples in the sample data set. In some embodiments, the greater the sample statistical differences, the greater the diversity of the samples.

[0217] In some embodiments, the gas company management platform can calculate the sample statistical differences in various ways. For example, the gas company management platform can quantify the cleaning data, configuration parameters, and cleaning effects of each sample data in the sample data set into numerical values, correspond each sample data to a numerical vector, calculate the vector distance (such as cosine distance) between every two numerical vectors in the sample data set, and calculate the variance of the obtained multiple vector distances. The greater the variance, the greater the sample statistical differences.

[0218] In some embodiments of this specification, training the effect determination model based on the training set, test set, and validation set is beneficial to improving the robustness of the effect determination model, preventing the effect determination model from overfitting. When the sample statistical differences are too large, appropriately increasing the learning rate enables the model to be fully learned, which is beneficial to accurately obtaining the cleaning effect.

[0219] In some embodiments of this specification, through the effect determination model, based on the cleaning data and configuration parameters, the cleaning effect is determined, and the learning ability of the machine learning model can be used to accurately evaluate the cleaning effect, thereby improving the efficiency of gas pipeline blockage cleaning.

[0220] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0221] In the meantime, this specification uses specific terms to describe the embodiments of this specification. For example, "an embodiment", "one embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be combined appropriately.

[0222] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification are not used to limit the order of the processes and methods in this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are only for illustrative purposes. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0223] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the individual embodiments disclosed above.

[0224] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history documents that are inconsistent with or conflict with the content of this specification, as well as the documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0225] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly presented and described in this specification.

Claims

1. A method for locating blockage points in intelligent gas pipelines, characterized in that, The method is executed by the gas company management platform of the intelligent gas pipeline blockage location Internet of Things system, and includes: Determine monitoring points based on the historical blockage point set, where the monitoring points are located in the gas pipelines of the gas pipeline network; Construct a gas operation map based on the monitoring data corresponding to the monitoring points; Determine the target blockage position based on the gas operation map; Take the target blockage position as the position to be cleaned, determine the movement parameters of the cleaning robot based on the position to be cleaned and the movement speed, and control the cleaning robot to clean the position to be cleaned based on the movement parameters; the movement speed is related to the distribution sparsity of the position to be cleaned in the area where the position to be cleaned is located; In response to the cleaning robot reaching the position to be cleaned, control the cleaning robot to perform preliminary cleaning according to the preset configuration; In response to obtaining the cleaning data fed back by the cleaning robot: Obtain candidate configuration parameters based on historical data; the candidate configuration parameters refer to alternative configuration parameters, and the configuration parameters include at least one of the cleaning intensity and the cleaning tool; Based on the cleaning data, determine the estimated cleaning effect of the candidate configuration parameters through an effect determination model; the effect determination model is a machine learning model; Determine the candidate configuration parameters whose estimated cleaning effect meets the preset cleaning target as the configuration parameters; Generate a configuration update instruction based on the configuration parameters and send the configuration update instruction to the cleaning robot.

2. The method according to claim 1, characterized in that, The determining the target blockage position based on the gas operation map includes: Determine candidate blockage sections based on the gas operation map; Obtain the blockage detection results of the candidate blockage sections; Determine the target blockage position based on the blockage detection results.

3. The method according to claim 2, wherein The determining the candidate blockage sections based on the gas operation map includes: Evaluate the data completeness of the gas operation map. In response to the data completeness not meeting the preset completeness condition: Determine a data request and send the data request to the government safety supervision management platform to obtain supplementary data; Update the gas operation map based on the acquisition situation of the supplementary data to obtain an updated map; Determine the candidate blockage sections based on the updated map.

4. An Internet of Things system for locating blockage points in intelligent gas pipelines, characterized in that, The Internet of Things system includes a government safety supervision management platform, a government safety supervision sensor network platform, a gas company management platform, a gas company sensor network platform, and an intelligent gas equipment object platform; The gas company management platform is configured to execute the intelligent gas pipeline blockage location method as described in claim 1.

5. The Internet of Things system according to claim 4, wherein The gas company management platform is further configured to: Determine candidate blockage sections based on the gas operation map; Obtain the blockage detection results of the candidate blockage sections; Determine the target blockage position based on the blockage detection results.

6. The Internet of Things system according to claim 5, characterized in that, The gas company management platform is further configured to: Evaluate the data completeness of the gas operation map. In response to the data completeness not meeting the preset completeness condition: Determine a data request and send the data request to the government safety supervision management platform to obtain supplementary data; Update the gas operation map based on the acquisition of the supplementary data to obtain an updated map; Determine the candidate blockage section based on the updated map.

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