Pipeline attachment monitoring method and system based on intelligent gas internet of things

Abstract

The invention provides a pipeline attachment monitoring method and system based on an intelligent gas internet of things, and relates to the technical field of internet of things, and the method comprises the steps: controlling a sensor on a gas pipeline or a gas valve to collect basic sensing data, and uploading the basic sensing data to a gas company management platform through a gas equipment object platform; determining an attachment risk area of the gas pipeline; determining a to-be-detected pipeline area, controlling the inspection robot to inspect the to-be-detected pipeline area, and collecting an inspection result uploaded by the inspection robot; generating pipeline cleaning data of the gas pipeline and sending the pipeline cleaning data to a government safety supervision management platform; and generating a pipe cleaning instruction based on the pipe cleaning confirmation data, and performing pipe cleaning operation on part of the gas pipelines according to the instruction of the pipe cleaning instruction. According to the method, the actual attachment influence relation between different gas pipeline areas can be determined, and the accuracy degree of pipeline attachment monitoring can be improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology, and in particular to a pipeline attachment monitoring method and system based on a smart gas Internet of Things. Background Art

[0002] Due to changes in the composition of the gas source and the influence of factors such as wear and corrosion of the pipeline itself, as the service life of the gas pipeline continues to increase, the gas often carries tiny particles such as solid impurities during transportation. These particles, including dust and other impurities, not only affect the quality of the gas, but also pose a potential threat to the safe operation of the pipeline system. Therefore, it is necessary to estimate and evaluate the type and amount of impurities in the gas pipeline to determine whether the gas pipeline needs to be cleaned. The efficiency and safety of gas transportation are closely related to the cleanliness of the gas pipeline. Regular cleaning of the gas pipeline can not only ensure the stability of the gas transmission process, but also extend the life of the gas pipeline.

[0003] In order to improve the ability to identify impurities in gas pipelines, CN107727541B discloses an in-pipeline aerosol monitoring device, method and pipeline system. The system detects the concentration and particle size distribution of aerosols in gas pipelines by configuring an aerosol particle size spectrometer. However, the system does not study the potential relationship between impurities and gas pipelines in different areas. For example, it does not involve identifying the impact of impurities in upstream gas pipelines on downstream gas pipelines.

[0004] Therefore, it is necessary to provide a pipeline attachment monitoring method based on a smart gas Internet of Things, which can not only monitor the impurity distribution of each gas pipeline based on the distribution of the gas pipeline, but also optimize the cleaning strategy of the gas pipeline based on the impurity distribution. Summary of the invention

[0005] In order to solve the problems of low accuracy in monitoring different gas pipeline attachments in the gas pipeline network and difficulty in identifying the potential relationship between impurities and gas pipelines in different areas, this specification provides a pipeline attachment monitoring method and system based on a smart gas Internet of Things.

[0006] One or more embodiments of the present specification provide a pipeline attachment monitoring method based on a smart gas Internet of Things, which is executed by a gas company management platform of a smart gas pipeline attachment monitoring system, including: issuing a data collection instruction, controlling sensors on gas pipelines or gas valves to collect basic perception data, and transmitting the data to a gas equipment object platform for aggregation and storage, and uploading the basic perception data to the gas company management platform through a gas company sensor network platform according to preset reporting rules through the gas equipment object platform; determining the attachment risk area of ​​the gas pipeline according to the basic perception data; determining the pipeline area to be inspected according to the attachment risk area, generating a pipeline detection instruction and sending it to a gas pipeline network maintenance object platform; the gas pipeline network maintenance object platform controls an inspection robot to inspect the pipeline area to be inspected based on the pipeline detection instruction, and collects the inspection results uploaded by the inspection robot; receives the inspection results collected by the gas pipeline network maintenance object platform, generates gas pipeline cleaning data and sends it to a government safety supervision and management platform; and receives cleaning confirmation data returned by the government safety supervision and management platform, generates a cleaning instruction based on the cleaning confirmation data, and performs cleaning operations on some gas pipelines according to the cleaning instruction.

[0007] One or more embodiments of the present specification provide a pipeline attachment monitoring system based on a smart gas Internet of Things, the system comprising one or more of a gas company management platform, a gas equipment object platform, a gas network maintenance object platform, and a government safety supervision management platform; the gas company management platform is configured to: issue data collection instructions, control sensors on gas pipelines or gas valves to collect basic perception data, and transmit it to the gas equipment object platform, and receive basic perception data uploaded by the gas equipment object platform; determine the attachment risk area of ​​the gas pipeline based on the basic perception data; determine the pipeline area to be inspected based on the attachment risk area, generate a pipeline inspection instruction and send it to the gas network maintenance object platform; receive the inspection results collected by the gas network maintenance object platform, generate gas pipeline cleaning data and send it to the government safety supervision management platform; Full supervision and management platform; receives cleaning confirmation data returned by the government safety supervision and management platform, generates cleaning instructions based on the cleaning confirmation data, and performs cleaning operations on some gas pipelines according to the cleaning instructions; the gas equipment object platform is configured to: receive basic perception data transmitted by sensors, summarize and store them, and upload the basic perception data to the gas company management platform according to preset reporting rules; the gas pipeline network maintenance object platform is configured to: receive pipeline inspection instructions sent by the gas company management platform, control the inspection robot to inspect the pipeline area to be inspected, and collect the inspection results uploaded by the inspection robot, and send the inspection results to the gas company management platform; the government safety supervision and management platform is configured to: receive cleaning data sent by the gas company management platform, generate and send cleaning confirmation data to the gas company management platform.

[0008] The beneficial effects brought about by the above invention include but are not limited to: (1) By determining the attachment impact degree, the attachment impact relationship between different gas pipeline areas that conforms to reality can be determined, thereby providing reliable data support for the subsequent classification of attachment impact levels and determination of attachment impact areas. (2) By using the attachment analysis model based on the pipeline network simulation map to predict the attachment impact degree of each gas pipeline, the attachment risk level of different gas pipelines can be more accurately evaluated, which can improve the accuracy of pipeline attachment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a platform structure diagram of a pipeline attachment monitoring system based on a smart gas Internet of Things according to some embodiments of this specification; Figure 2 is an exemplary flow chart of a pipeline attachment monitoring method based on a smart gas Internet of Things according to some embodiments of this specification; Figure 3 is an exemplary flow chart for determining an attachment risk area according to some embodiments of this specification; Figure 4 is an exemplary schematic diagram of determining the risk level of attachments according to some embodiments of this specification; Figure 5 is an exemplary schematic diagram of a prediction model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0010] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0011] In view of the different attachments distributed in the gas pipeline, the different contents of different attachments, and the physical properties of the different gas pipelines themselves, the fan can better clean the gas pipeline by adopting reasonable cleaning strategies.

[0012] In view of this, in some embodiments of the present specification, it is hoped to provide a pipeline attachment monitoring method based on a smart gas Internet of Things, which can more intelligently plan the inspection sequence of gas pipelines based on the location distribution relationship of gas management, and more reasonably plan the cleaning strategy for gas pipelines that need to be cleaned.

[0013] Figure 1 is a platform structure diagram of a pipeline attachment monitoring system based on a smart gas Internet of Things according to some embodiments of this specification. Figure 1 As shown, the pipeline attachment monitoring system based on the smart gas Internet of Things may include a government safety supervision management platform 110, a government safety supervision sensor network platform 120, a gas company management platform 130, a gas company sensor network platform 140, a gas pipeline network maintenance object platform 150 and a gas equipment object platform 160.

[0014] The government safety supervision management platform 110 is a platform for providing government information and services. In some embodiments, the government safety supervision management platform 110 can be configured to receive the pigging data sent by the gas company management platform 130, and generate and send the pigging confirmation data to the gas company management platform 130.

[0015] The government safety supervision sensor network platform 120 is used to connect the government safety supervision management platform 110 and the gas company management platform 130 for information transmission.

[0016] The gas company management platform 130 refers to a platform for processing and storing data related to the pipeline attachment monitoring system 100 based on the smart gas Internet of Things. In some embodiments, the gas company management platform 130 can exchange information with the gas equipment object platform 160, the gas pipeline maintenance object platform 150 and the government safety supervision management platform 110 through the gas company sensor network platform 140. For example, the gas company management platform 130 can generate cleaning data of the gas pipeline and send it to the government safety supervision management platform 110. For more information about the gas company management platform 130, please refer to the relevant description below.

[0017] In some embodiments, the gas company management platform 130 may include a processor and a storage unit.

[0018] The gas company sensor network platform 140 refers to a platform for managing communications. In some embodiments, the gas company sensor network platform 140 can realize the functions of sensing information sensor communication and controlling information sensor communication. In some embodiments, the gas company sensor network platform 140 can be used to interact with other modules in the pipeline attachment monitoring system 100 based on the smart gas Internet of Things. For example, the gas company management platform 130 can send the cleaning data to the gas company management platform 130 through the gas company sensor network platform 140. For another example, the gas company management platform 130 can send the pipeline detection instruction to the gas pipeline network maintenance object platform 150 through the gas company sensor network platform 140.

[0019] The gas pipeline network maintenance object platform 150 refers to a platform for controlling auxiliary facilities, pipeline maintenance equipment and vehicles related to gas pipeline network maintenance. In some embodiments, the gas pipeline network maintenance object platform 150 may include a PLC controller, a data transmission device and a storage unit, and communicate with pipeline maintenance equipment (e.g., inspection robots, pipeline locators, pipeline cleaning machines, combustible gas detectors, etc.).

[0020] In some embodiments, the gas pipeline network maintenance object platform 150 may be configured to receive pipeline inspection instructions sent by the gas company management platform 130, control the inspection robot to inspect the pipeline area to be inspected, and collect the inspection results uploaded by the inspection robot, and send the inspection results to the gas company management platform 130. In some embodiments, the gas pipeline network maintenance object platform 150 may include a processor and a storage unit. For more information about the gas pipeline network maintenance object platform 150, please refer to the relevant description below.

[0021] In some embodiments, the gas equipment object platform 160 may be configured to communicate with the gas pipe network maintenance object platform 150 through the gas company sensor network platform 140. In some embodiments, the gas equipment object platform 160 may be configured to receive basic sensing data transmitted by the sensor, summarize and store it, and upload the basic sensing data to the gas company management platform 130 according to the preset reporting rules. For more information about the basic sensing data, please refer to the relevant description below.

[0022] In some embodiments, the gas equipment object platform 160 may include a PLC controller, a data transmission device and a storage unit, and is communicatively connected to the sensor, and may receive, aggregate and store the basic sensing data collected by the sensor.

[0023] In some embodiments, the pipeline attachment monitoring system 100 based on the smart gas Internet of Things may further include a processor ( Figure 1Not shown). For example, the gas company management platform 130 and / or the government safety supervision management platform 110 may include a processor. In some embodiments, the processor may process information and / or data related to the pipeline attachment monitoring system 100 based on the smart gas Internet of Things to perform one or more functions described in this specification. In some embodiments, the processor may include one or more engines (e.g., a single-chip processing engine or a multi-chip processing engine). As an example only, the processor may include a central processing unit (CPU), a graphics processor (GPU), a digital signal processor (DSP), etc. or any combination of the above. In some embodiments, the processor may interact with multiple platforms (e.g., the government safety supervision management platform 110, the gas company management platform 130, the gas pipeline network maintenance object platform 150, the gas equipment object platform 160, etc.) included in the pipeline attachment monitoring system 100 based on the smart gas Internet of Things.

[0024] In some embodiments, the pipeline attachment monitoring system 100 based on the smart gas Internet of Things may further include a storage unit ( Figure 1 (not shown). For example, one or more of the government safety supervision management platform 110, the gas company management platform 130, the gas pipeline network maintenance object platform 150, and the gas equipment object platform 160 may include a storage unit. In some embodiments, the storage unit may store information and / or data related to the pipeline attachment monitoring system 100 based on the smart gas Internet of Things. For example, the gas pipeline network maintenance object platform 150 may store the inspection results obtained by the inspection robot through the storage unit.

[0025] In some embodiments of the present specification, the pipeline attachment monitoring system 100 based on the smart gas Internet of Things can form an information operation closed loop between various functional platforms, coordinate and operate regularly, and realize the informatization and intelligence of the monitoring of attachments on the gas pipeline.

[0026] Figure 2 This is an exemplary flow chart of a pipeline attachment monitoring method based on a smart gas Internet of Things according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by a gas company management platform.

[0027] Step 210, issue a data collection instruction to control the sensors on the gas pipeline or gas valve to collect basic perception data, and transmit it to the gas equipment object platform for aggregation and storage. The basic perception data is uploaded to the gas company management platform through the gas equipment object platform according to the preset reporting rules.

[0028] Data collection instructions refer to instructions sent by the gas company management platform to the gas equipment object platform for data collection, aggregation, and storage.

[0029] In some embodiments, the gas equipment object platform can collect basic perception data through sensors on gas pipelines or gas valves based on data collection instructions.

[0030] Gas pipeline refers to the pipeline facilities used to transport gas. Gas pipelines can be divided into different types according to their importance. For example, trunk pipelines, secondary pipelines, branch pipelines, etc.

[0031] Gas valve refers to the valve installed on the gas pipeline. Gas valve can be used to adjust the gas flow rate, gas pressure, etc. in the gas pipeline.

[0032] Sensors refer to components used to collect gas-related parameters, such as pressure sensors, temperature sensors, and flow rate sensors.

[0033] Basic sensing data refers to gas-related parameters collected by sensors. For example, basic sensing data may include gas temperature, gas flow rate, gas pressure, etc.

[0034] The preset reporting rules may be data processing procedures predetermined by the gas company management platform and executed by the gas equipment object platform. For example, the preset reporting rules may include regular or irregular reporting. For example, regular reporting may be reporting every 45 days, and irregular reporting may be reporting immediately when abnormal gas parameters exist. Abnormal gas parameters may refer to abnormal gas temperature, abnormal gas flow rate, or abnormal gas pressure, etc.

[0035] In some embodiments, the preset reporting rules can be determined based on historical experience or the risk level of the attachments. For example, when reporting once every 14 days, the risk level of the attachments determined by the gas company management platform based on the basic perception data analysis is relatively low, and the data reporting time can be appropriately extended. For example, if the risk level of attachments in different gas pipeline areas is lower than the preset risk threshold, the reporting time is doubled.

[0036] It is understandable that when the risk level of deposits is relatively low, it means that the data analysis interval is relatively short and the accumulation of deposits in the pipeline is relatively slow.

[0037] For more information on the risk levels of deposits, see Figure 3 and related descriptions.

[0038] In some embodiments, the gas equipment object platform can acquire and aggregate basic perception data by communicating with different sensors, and store the basic perception data in a storage device.

[0039] For more information about the gas company management platform and gas equipment object platform, see Figure 1 and related descriptions.

[0040] Step 220: Determine the risk area of ​​the gas pipeline attachment based on the basic sensing data.

[0041] The attachment risk area refers to the gas pipeline area where there may be pipeline attachments. Pipeline attachments refer to the residues accumulated on the inner wall of the gas pipeline during the gas transportation process. For example, the residues may include hydrates, oil stains, sand, etc.

[0042] The gas pipeline area refers to an area where there is a need to monitor pipeline attachments. In some embodiments, the gas pipeline area can be a section of the pipeline or a partial area of ​​a section of the pipeline. In some embodiments, if there are multiple sensors on a section of the pipeline, the part of the gas pipeline between every two adjacent sensors can be used as the gas pipeline area.

[0043] In some embodiments, the gas company management platform can determine the attachment risk area based on the basic sensing data through a variety of methods. For example, the gas company management platform can generate a difference between the basic sensing data and a preset gas delivery parameter, and if the difference is greater than a preset delivery difference threshold, the location of the sensor is determined as an attachment risk area.

[0044] The preset gas transmission parameters refer to the preset gas pipeline operating parameters. The preset gas transmission parameters can be the gas pipeline operating parameters determined during the construction or planning of the gas pipeline to ensure the normal transmission of downstream gas. For example, the preset gas transmission parameters may include a preset gas transmission rate range, a preset gas pressure range, a preset gas temperature range, etc.

[0045] In some embodiments, the preset transmission difference threshold may be determined by the gas company management platform based on historical inspection results. For example, if there are 10 historical inspection results that find pipeline attachments, and 9 of them are when the difference between the basic sensing data of the pipeline and the preset gas transmission parameters is greater than a value (e.g., 5%), then this value is set as the preset transmission difference threshold.

[0046] For more information about historical check results, see Figure 2 Relevant description of step 230.

[0047] Step 230, according to the attachment risk area, determine the pipeline area to be inspected, generate a pipeline inspection instruction and send it to the gas pipeline network maintenance object platform; the gas pipeline network maintenance object platform controls the inspection robot to inspect the pipeline area to be inspected based on the pipeline inspection instruction, and collects the inspection results uploaded by the inspection robot.

[0048] The pipeline area to be inspected refers to a gas pipeline area that needs to be inspected on site. For example, the pipeline area to be inspected can be a gas pipeline area that is inspected on site by a detection device or an inspection robot.

[0049] In some embodiments, the gas company management platform can determine the pipeline area to be inspected by various methods according to the attachment risk area. For example, the gas company management platform can obtain the time interval between the current time and the last cleaning time of a gas pipeline area. If the time interval is greater than a preset interval, the gas pipeline is determined to be a pipeline area to be inspected. The preset interval can be a preset value input by the user into the gas company management platform.

[0050] In some embodiments, the gas company management platform can determine the pipeline area to be detected based on the difference between the basic sensing data and the preset gas transmission parameters. For example, if the difference between the basic sensing data and the preset gas transmission parameters of a gas pipeline area reaches 2 times the preset transmission difference threshold (for example, the preset transmission difference threshold is 5%, and the actual difference reaches 10% or more), it will be determined as the pipeline area to be detected.

[0051] The pipeline detection instruction refers to the pipeline detection related instruction transmitted between the platform and the device in the smart gas pipeline attachment monitoring system. In some embodiments, the gas pipeline network maintenance object platform can send the pipeline detection instruction to the inspection robot.

[0052] Inspection robots refer to robots equipped with inspection equipment. Inspection robots can be used to inspect whether there are pipeline attachments and the content of attachments (such as attachment thickness, etc.) on gas pipelines. For example, the inspection equipment can be an ultrasonic detector, a thickness gauge, etc.

[0053] The inspection result refers to the analysis data related to the gas pipeline inspection. For example, the inspection result may include whether there are pipeline attachments in the pipeline area to be inspected and the amount of the attachments. For another example, the inspection result may also include whether to perform pipeline cleaning.

[0054] In some embodiments, the gas pipeline network maintenance object platform can aggregate the inspection results uploaded by the inspection robots to generate historical inspection results.

[0055] The deposit content refers to the amount of pipe deposits accumulated on the inner wall of the pipe. For example, the deposit content may include the deposit thickness, the deposit volume, etc.

[0056] In some embodiments, the gas company management platform can obtain the inspection results generated by one or more inspection robots through the gas pipeline maintenance object platform.

[0057] In some embodiments, the gas company management platform can also generate a pipeline inspection sequence based on the risk level of attachments and the geographic location information of the gas pipeline area; and according to the pipeline inspection sequence, conduct on-site inspections of the pipeline area to be inspected by inspection robots to obtain inspection results.

[0058] The attachment risk level refers to the data reflecting the risk of pipeline attachments in different gas pipeline areas. For more information about the attachment risk level, see Figure 3 , Figure 4 and related descriptions.

[0059] Geographic location information can refer to the geographical location of the gas pipeline area. For more information about geographic location information, see Figure 3 and related descriptions.

[0060] The pipeline inspection sequence includes the order in which different pipeline areas to be inspected are inspected. The pipeline inspection sequence can be represented by Arabic numerals or English letters. For example, (AB, 3) means that the pipeline inspection sequence of the gas pipeline area numbered AB is the third. The pipeline inspection sequence can also include not inspecting, for example, (BC, N) means not inspecting the gas pipeline area numbered BC.

[0061] In some embodiments, the gas company management platform can generate one or more inspection groups, each of which includes one or more adjacent pipeline areas to be inspected, and the pipeline inspection order of each inspection group is determined by sorting according to the average value of the risk level of the attachments in the pipeline areas to be inspected in each group. For example, the gas company management platform can divide different inspection routes according to the gas pipeline network, and each inspection route is regarded as an inspection group. Each inspection route includes several gas pipeline areas connected in sequence, and the inspection route can have a preset maximum length (for example, 5 kilometers).

[0062] In some embodiments, the preset maximum length can be determined based on the endurance of the inspection robot (e.g., remaining power), and the endurance can be determined based on historical inspection results. For example, in the last historical inspection, the inspection robot stopped working due to insufficient power after moving 6 kilometers after charging. Before the next inspection, if it is confirmed that the inspection robot has been charged, the preset maximum length can be determined to be 5.5 kilometers, and the actual use parameters of the inspection robot (e.g., the maximum distance moved before the battery is insufficient) are used as reference data for determining the preset maximum length next time.

[0063] In some embodiments of the present specification, the actual preset maximum length of the load can be determined according to the endurance of the inspection robot to ensure inspection efficiency.

[0064] In some embodiments, the gas company management platform can determine whether to inspect the pipeline area to be inspected based on the historical inspection results of the pipeline area to be inspected with an attachment risk level equal to or approximately equal to a preset risk threshold. For example, if the proportion of times that no pipe cleaning is required in the historical inspection results of a pipeline area to be inspected is greater than a preset proportion (e.g., 0.9) pre-entered into the system by the user, the pipeline area to be inspected is determined as an area that does not need to be inspected this time.

[0065] For more information on preset risk thresholds, see Figure 3 and related descriptions.

[0066] In some embodiments, in response to the inspection result of a certain pipeline area to be inspected being that the attachment content meets a preset content condition, the gas company management platform can adjust the pipeline inspection order of non-target pipelines.

[0067] The non-target pipeline may refer to a gas pipeline area associated with the pipeline area to be detected. In some embodiments, the non-target pipeline may be determined based on the influence of the attachment.

[0068] In some embodiments, the gas company management platform may determine non-target pipelines based on the edges of the pipeline network simulation graph.

[0069] It is understandable that only the downstream pipeline of the pipeline area to be inspected is associated with the pipeline area to be inspected, that is, the downstream pipeline of the pipeline area to be inspected may be affected by the transfer and accumulation of pipeline attachments of the upstream pipeline.

[0070] For more information on the impact of attachments and pipe network simulation diagrams, see Figure 3 , Figure 4 and related descriptions.

[0071] The preset content condition may include a judgment condition for determining whether to adjust the inspection order of non-target pipelines. For example, the preset content condition may include that the content of deposits (e.g., the thickness of deposits, etc.) is lower than a preset content threshold (e.g., 3 mm). In some embodiments, the preset content condition may also include a judgment condition for determining whether to perform pigging.

[0072] In some embodiments, the gas company management platform can pre-set a preset content threshold value based on historical basic sensing data. For example, the gas company management platform can obtain the gas transmission rate in the basic sensing data of multiple time periods after the pipe cleaning is not performed in the historical data, and use the minimum thickness of the attachment when the gas transmission efficiency (such as gas flow) is reduced due to the failure to clean the pipe as the preset content threshold value.

[0073] In some embodiments, the gas company management platform may also use a preset algorithm to determine a preset content threshold. For example, the gas company management platform may also determine multiple association groups based on historical sensor data and historical attachment risk levels of different pipeline areas at different times.

[0074] An association group may include one gas pipeline area with an inspection result of not clearing the pipe, and two adjacent gas pipeline areas downstream of the gas pipeline area with a proximity less than or equal to 2. For example, AB, BC, CD, and DE in ABCDE are four gas pipeline areas connected in sequence, and AB is a gas pipeline area with an inspection result of not clearing the pipe, then AB, BC, and CD can be determined as an association group.

[0075] Adjacency refers to a value that reflects the relative position relationship of different gas pipeline areas. For example, a proximity of 2 means that there is one gas pipeline area (such as BC) between two gas pipeline areas (such as AB and CD).

[0076] In some embodiments, the gas company management platform may generate a correlation ratio of the number of association groups whose attachment risk level increases twice in a row to the total number of association groups. Wherein, the attachment risk level increases twice in a row means that the attachment risk level of the downstream gas pipeline area (e.g., BC and CD) in the association group increases twice in a row.

[0077] For example, association group A includes the gas pipeline area AB whose inspection result is not clear, and two adjacent gas pipeline areas BC and CD downstream with a degree of proximity less than or equal to 2. In the subsequent two analysis results of the attachment risk level, the attachment risk level of BC and CD increases each time, for example, by 2 and 3 levels respectively. In this case, association group A is considered to be an association group in which the attachment risk level increases twice in a row.

[0078] In some embodiments, if there are one or more association groups whose association ratio is greater than a preset association threshold, the gas company management platform may determine these association groups as frequent association groups. In some embodiments, the gas company management platform may determine the average value of the minimum attachment thickness of the gas pipeline area (e.g., pipeline AB) whose inspection result is not cleared in these association groups as the preset content threshold.

[0079] In some embodiments, in response to the inspection result of a pipeline area to be inspected that the attachment content meets the preset content condition, the gas company management platform can determine the gas pipeline area in the downstream adjacent pipeline of the pipeline area to be inspected, where the attachment impact degree is not 0 and has been determined as the pipeline area to be inspected, as a non-target pipeline, and then adjust the inspection order of the non-target pipeline. For more information about the attachment impact degree, see Figure 3 and related descriptions.

[0080] Adjusting the inspection sequence may include postponing the inspection sequence of the pipeline area to be inspected or not inspecting it. In some embodiments, adjusting the inspection sequence may also include: if there are other pipeline areas to be inspected around the non-target pipeline (e.g., other gas pipeline areas adjacent to the non-target pipeline), the gas company management platform may issue a pipeline inspection instruction, and inspect the non-target pipeline whose inspection sequence has been adjusted by the inspection robot.

[0081] In some embodiments, the gas company management platform can determine the pipeline area to be inspected or the non-target pipeline corresponding to the edge in the pipeline network simulation map based on the pipeline network simulation map, and then determine to adjust the inspection order according to the proximity between different pipeline areas to be inspected and non-target pipelines, and the influence of the attachments in the gas pipeline area adjacent to the pipeline area downstream of the pipeline area to be inspected. For example, the gas company management platform can adjust the inspection order of non-target pipelines whose proximity to the pipeline area to be inspected is less than or equal to 2, and no longer adjust the inspection order of non-target pipelines whose proximity to the pipeline area to be inspected is greater than 2.

[0082] In some embodiments, the gas company management platform may determine the adjusted attachment risk level of the non-target pipeline, and then redetermine the pipeline inspection sequence according to the adjusted attachment risk level.

[0083] For example, the gas company management platform can determine the adjusted attachment risk level based on the current attachment impact level of the non-target pipeline and the positive correlation between the attachment impact degree through a preset algorithm. For example, the gas company management platform can calculate and determine the adjusted attachment risk level of the non-target pipeline through formula (1): N=O α(1) Wherein, N is the adjusted attachment risk level of the non-target pipeline, O is the current attachment risk level of the non-target pipeline, and α is the attachment impact of the current non-target pipeline.

[0084] For more information on the network simulation diagram, edge, and attachment risk levels, see Figure 3 , Figure 4 and related descriptions.

[0085] In some embodiments of the present specification, when the inspection personnel and materials and equipment required for pipeline network inspection are limited, and the inspection time is restricted, by determining a reasonable pipeline inspection sequence, the pipeline inspection efficiency can be improved, and the inspection needs of pipeline areas to be inspected with higher risks can be prioritized.

[0086] In some embodiments of the present specification, by adjusting the inspection order based on the influence of attachments, the inspection order of downstream pipelines can be reasonably adjusted according to the inspection results of upstream pipelines, which can improve the inspection efficiency and extend the endurance of the inspection robot to a certain extent.

[0087] Step 240, receiving the inspection results collected by the gas pipeline network maintenance object platform, generating gas pipeline cleaning data and sending it to the government safety supervision management platform.

[0088] Pipe cleaning data refers to data related to gas pipeline cleaning. For example, pipe cleaning data may include data such as whether a gas pipeline area is cleaned and the time of cleaning. Pipe cleaning data may be represented as sequence data related to pipe cleaning of multiple pipelines. For example, (AB, Y, 1.3; BC, N, N; CD, Y, 3) may indicate that the gas pipeline areas numbered AB and CD need to be cleaned (Y means that cleaning is required, and N means that cleaning is not required), and the cleaning time is 1.3 days and 3 days respectively; the gas pipeline area numbered BC does not need to be cleaned.

[0089] In some embodiments, the gas company management platform can generate cleaning data based on the inspection results through a variety of methods. For example, it can determine whether to clean the pipeline and the pipeline cleaning area based on the thickness of the attachments in the inspection results. For example, if the attachment thickness in a gas pipeline area reaches a preset content threshold (such as 3 mm), it is determined that the gas pipeline area needs to be cleaned, and the gas pipeline area is determined as a pipeline cleaning area.

[0090] For another example, the gas company management platform can also generate an estimated pipeline cleaning time based on the results of two inspections and the time interval between the inspections. For example, the gas company management platform can divide the difference in the content of pipeline attachments obtained from the two inspections by the time interval between the inspections to obtain the attachment accumulation rate, and then divide the preset content threshold by the attachment accumulation rate and subtract the time interval to obtain the cleaning time.

[0091] Step 250, receiving the cleaning confirmation data returned by the government safety supervision management platform, generating a cleaning instruction based on the cleaning confirmation data, and performing cleaning operations on part of the gas pipelines according to the cleaning instruction.

[0092] Pipe cleaning confirmation data refers to confirmation information related to pipe cleaning data issued by the government safety supervision and management platform. For example, pipe cleaning confirmation data may include whether to clean the pipeline, pipeline cleaning area, pipe cleaning time, and pipe cleaning order. For example, (AB, Y, 1.3, 1; CD, Y, 3, 37) can indicate that the gas pipeline areas numbered AB and CD need to be cleaned (Y means that pipe cleaning is required, and N means that pipe cleaning is not required), the cleaning time of AB pipeline is 1.3 days later, and the cleaning order is the first; the cleaning time of BC pipeline is 3 days later, and the cleaning order is the 37th.

[0093] In some embodiments, the government safety supervision and management platform can sort based on the order of cleaning time and determine the cleaning order. In some embodiments, if the cleaning time of different gas pipelines is the same or similar, the cleaning order can be determined according to the pipeline type (reflecting the importance of the pipeline, such as trunk pipeline, branch pipeline, etc.) or the size of the accumulation rate of attachments. For example, the cleaning time of the two gas pipeline areas numbered AB and BC is 1 day later, but the type of AB pipeline is a trunk pipeline, and the type of BC pipeline is a branch pipeline. Since AB pipeline is more important than BC pipeline, the cleaning order of AB pipeline is higher than that of BC pipeline.

[0094] In some embodiments, the gas company management platform can also generate fan control instructions based on the pipe cleaning confirmation data returned by the government safety supervision management platform and send them to the gas pipeline maintenance object platform, and based on the fan control instructions, control the fan to operate with preset working parameters through the gas pipeline maintenance object platform.

[0095] Fan control instructions refer to instructions related to controlling fans that are communicated between the platform and the device in the smart gas pipeline attachment monitoring system. For example, fan control instructions may include preset working parameters generated by the gas company management platform, and the gas pipeline maintenance object platform may send fan control instructions to different fans to control different fans to operate with preset working parameters.

[0096] A fan is a device used to control the flow of gas in a gas pipeline. In some embodiments, the fan can prevent impurities in the gas from being naturally deposited due to gravity, but rather being transported forward along with the gas, without affecting the normal transportation of the gas in the pipeline. In some embodiments, a removal device for collecting impurities can be deployed at a specific location in the gas pipeline (e.g., downstream of the fan).

[0097] In some embodiments, the fan can stir the gaseous or liquid fuel gas in the pipeline to affect the flow direction of the fuel gas, thereby reducing the rate of accumulation of deposits. For more information on the rate of accumulation of deposits, see Figure 2 The content of other parts.

[0098] The preset working parameters refer to the preset fan operating parameters, such as power, fan blade speed, etc.

[0099] In some embodiments, the preset operating parameters can be pre-entered into the gas company management platform by the user.

[0100] In some embodiments, the gas equipment object platform may issue control instructions through wireless communication to set preset operating parameters of the fan, or adjust the preset operating parameters of the fan.

[0101] In some embodiments, the gas company management platform can obtain preset gas transmission parameters through the government safety supervision management platform, and adjust the preset working parameters according to the basic sensing data and the preset gas transmission parameters. For more information about the basic sensing data and the preset gas transmission parameters, see Figure 2 The content of other parts.

[0102] In some embodiments, the gas company management platform can adjust the preset working parameters through a variety of methods.

[0103] For example, the gas company management platform can adjust the preset working parameters based on the difference between the basic sensing data and the preset gas transmission parameters (such as the median value of the parameter range). For example, the gas company management platform can determine the stability based on the difference between the basic sensing data and the preset gas transmission parameters. If the stability is lower than 0.9, the prediction model is used again to determine the adjusted preset working parameters. For more information about the prediction model, see Figure 5 and related descriptions.

[0104] Stability refers to the degree of stability of the gas delivery process. For example, stability may include gas pressure stability and gas flow rate stability. Gas pressure stability and gas flow rate stability refer to values ​​reflecting the stability of gas pressure and gas flow rate. The greater the gas pressure stability and gas flow rate stability, the more stable the gas pressure and gas flow rate. Gas pressure stability and gas flow rate stability may be values ​​between 0 and 1.

[0105] In some embodiments, the stability can be determined based on the fluctuation of the gas pressure and the gas flow rate. For example, the greater the fluctuation of the gas pressure and the gas flow rate, the lower the stability.

[0106] In some embodiments, the gas company management platform can determine the stability based on the basic sensing data and the preset gas transmission parameters. For example, the gas company management platform can obtain the difference between the basic sensing data and the preset gas transmission parameters, and divide the difference by the preset gas transmission parameters to determine the fluctuation value. If the fluctuation value is within 5%, the stability is determined to be 1; if the fluctuation value is between 5%-15%, the stability is determined to be 0.9, and so on.

[0107] In some embodiments, the gas company management platform can adjust the preset working parameters through the machine learning model. For example, the gas company management platform can analyze the candidate working parameters through the prediction model, determine the predicted working parameters, and then adjust the preset working parameters according to the predicted working parameters.

[0108] For more information on how to adjust the preset working parameters and prediction models, see Figure 5 and related descriptions.

[0109] In some embodiments of the present specification, by dynamically adjusting the operating parameters of the fan such as power and blade speed, the risk of pipeline residue or generation of pipeline attachments and the rate of attachment accumulation can be reduced to a certain extent, thereby reducing the workload of subsequent pipeline cleaning and maintenance.

[0110] In some embodiments of the present specification, the operating parameters of the fan are adjusted by considering the difference between the basic sensing data obtained by monitoring and the preset pipeline operating parameters. Since the change in the monitoring data can also indirectly reflect the accumulation rate of attachments in the pipeline, this can also effectively reduce the risk of residual or generated attachments in the pipeline and reduce the accumulation rate of attachments.

[0111] Pipe cleaning instructions refer to the instructions related to pipe cleaning communicated between the platform and the device in the smart gas pipeline attachment monitoring system. For example, the gas company management platform can generate a pipe cleaning instruction and send it to the gas pipeline network maintenance object platform, which will allocate the personnel, materials, and equipment required for pipe cleaning according to the pipe cleaning instruction, and perform pipe cleaning operations.

[0112] Pipe cleaning operation refers to the operation of cleaning the pipeline attachments on the inner wall of the gas pipeline.

[0113] In some embodiments of the present invention, taking into account the deposition of impurities in the gas itself and the deposition of impurities generated by physical aggregation and chemical reactions during gas transportation, the attachment risk area and the pipeline area to be inspected can be reasonably determined based on the collected basic perception data, thereby ensuring the efficiency of on-site inspections and saving pipeline inspection costs and subsequent pipe cleaning costs.

[0114] It should be noted that the above description of the process 200 is only for example and illustration, 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 the process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0115] Figure 3 is an exemplary flow chart for determining an attachment risk area according to some embodiments of this specification. Figure 3As shown, the process 300 includes the following steps. In some embodiments, the process 300 can be executed by a gas company management platform.

[0116] Step 310, generating geographical location information of the gas pipeline area, geographical location information of the sensor, and gas flow information based on the basic sensing data.

[0117] For more information on gas pipeline areas, see Figure 2 and related descriptions.

[0118] The geographic location information may include information related to the geographic location of the gas pipeline area and / or the sensor. For example, the geographic location information may include the location coordinates of the gas pipeline area and / or the sensor. For another example, the geographic location information may be the relative position relationship between different gas pipelines and sensors. For example, (AB, BC, -10) may indicate that the AB pipeline is upstream of the BC pipeline and is 10 meters away from the BC pipeline.

[0119] In some embodiments, the gas company management platform can determine the number information of the gas pipeline area or sensor (such as the area number or sensor number) corresponding to the basic perception data based on the basic perception data, and then obtain the geographical location information corresponding to different gas pipeline areas or sensors from the government safety supervision management platform.

[0120] Gas flow information refers to data related to gas flow, for example, gas flow information may include gas flow direction, flow rate, etc.

[0121] In some embodiments, the gas company management platform can extract information about gas flow rate, gas flow direction, etc. from the basic sensing data, and then determine the gas flow information based on the gas flow rate. For another example, the gas company management platform can retrieve the gas flow information of the corresponding gas pipeline area from the database of the government safety supervision management platform.

[0122] For more information about basic perception data, see Figure 2 and related descriptions.

[0123] Step 320: Determine the impact of the attachments based on historical pigging data.

[0124] Historical pigging data refers to data related to pipeline pigging that was performed in the past. For example, historical pigging data may include historical pigging time, gas pipeline area corresponding to the pigging location, cumulative pigging times, etc.

[0125] In some embodiments, the gas company management platform can determine historical pigging data based on the pigging log stored in the gas network maintenance object platform. The pigging log refers to data that records and counts all pigging operations.

[0126] The attachment impact refers to the impact of the current gas pipeline area on other adjacent and connected gas pipeline areas when there are pipeline attachments in the current gas pipeline area. The larger the attachment impact, the more likely it is that other gas pipeline areas will have similar pipeline attachments when there are pipeline attachments in the current gas pipeline area.

[0127] For more information on pipe attachments, see Figure 2 and related descriptions.

[0128] In some embodiments, the degree of influence of the attachment may refer to the degree of influence of the upstream gas pipeline area on the downstream gas pipeline area.

[0129] In some embodiments, the gas company management platform can determine the impact of the attachment through a variety of methods based on historical cleaning data. For example, there are three adjacent gas pipeline areas AB, BC, and CD, and AB, BC, and CD are all attachment risk areas, and the attachment thickness AB≥BC≥CD, then it can be considered that the attachment impact at BC is equal to the ratio of the attachment content at BC to the attachment content at AB, and the attachment impact at CD is equal to the ratio of the attachment content at CD to the attachment content at BC.

[0130] In some embodiments, the gas company management platform can construct a data group to be screened based on historical pigging data and related historical basic perception data, wherein the data group to be screened includes multiple sets, each set consisting of historical pigging data of at least two adjacent gas pipeline areas. The gas company management platform can determine the number of sets in the data group to be screened in which both the gas pipeline area AB and its upstream gas pipeline area are detected to have attachments, and the attachment thickness of the upstream gas pipeline area is greater than the attachment thickness of the downstream gas pipeline area, and the ratio to the total number of sets in the data group to be screened that include the gas pipeline area AB, and then determine this ratio as the attachment influence of the gas pipeline area AB.

[0131] Step 330, determining the risk level of the attachment in the gas pipeline area according to the geographical location information, the gas flow information, and the impact of the attachment.

[0132] The attachment risk level refers to data reflecting the risk of attachment in different gas pipeline areas. The higher the attachment risk level, the greater the possibility of attachment in the gas pipeline area. For example, the attachment risk level can be expressed in Arabic numerals, such as 0-10, with 10 being the highest.

[0133] In some embodiments, the gas company management platform can determine the attachment risk level by a variety of methods. For example, the gas company management platform can determine the difference between the basic sensing data and the preset gas transmission parameters at different locations, and determine the attachment risk level based on the ratio of the difference to the preset transmission difference threshold. For example, if the ratio of the difference to the preset transmission difference threshold is less than 1, the attachment risk level is determined to be 1-5 levels (indicating that the risk of attachment is low) according to the ratio. If it is equal to 1, the attachment risk level is 5 (indicating that there is a certain risk). If it is greater than 1, the attachment risk level is determined to be 6-10 levels according to the ratio. For example, when the ratio reaches 2 or above, the level is 10, which is the maximum.

[0134] In some embodiments, the gas company management platform can determine the attachment risk level of other gas pipeline areas based on the attachment impact degree. For example, if the attachment risk level of the current gas pipeline area is level 10 and the attachment impact degree is 0.8, the attachment risk level of the downstream adjacent gas pipeline area may be level 8.

[0135] For more information on presetting the delivery difference threshold, see Figure 2 and related descriptions.

[0136] Step 340: Determine the attachment risk area based on the attachment risk level.

[0137] The attachment risk area refers to the gas pipeline area where there may be pipeline attachments. For more information about the attachment risk area, see Figure 2 and related descriptions.

[0138] In some embodiments, in response to the attachment risk level satisfying a preset risk condition, the gas company management platform may determine that the gas pipeline area corresponding to the attachment risk level is an attachment risk area, wherein the preset risk condition may be determined based on historical inspection results.

[0139] The preset risk condition refers to the judgment rule preset by the gas company management platform for judging whether it is an attachment risk area. For example, the preset risk condition can be a preset risk threshold, such as the attachment risk level is not less than level 6.

[0140] In some embodiments, the gas company management platform can determine and adjust preset risk conditions based on historical inspection results and historical cleaning data.

[0141] For example, the gas company management platform can adjust the preset risk conditions based on the preset algorithm and historical inspection results. For example, the gas company management platform can adjust the preset risk conditions through formula (2): T=Y-Q2 / Q1(2) Among them, T is the adjusted preset risk threshold, Y is the original risk threshold, Q2 is the historical inspection result of the pipeline with the attachment risk level equal to the original risk threshold, the result is the number of times the pipeline needs to be cleaned, and Q1 is the total number of historical inspection results.

[0142] In some embodiments, the gas company management platform may determine the original risk threshold according to the same preset algorithm. For example, the original risk threshold may be the preset risk threshold after the last adjustment.

[0143] In some embodiments, the original risk threshold may be a preset value predetermined by the user and input into the gas company management platform. For example, if the user specifies that the gas pipeline needs to be further inspected when the attachment risk level is level 5 or above, the preset risk threshold may be level 5. In some embodiments, the gas company management platform may determine the attachment risk level of the pipeline to be inspected corresponding to the minimum attachment thickness that needs to be cleaned as the original risk threshold based on the inspection result of the pipeline to be inspected.

[0144] For example, the gas company management platform has identified 800 pipeline areas to be inspected, of which the risk levels of 60 areas to be inspected are exactly equal to or approximately equal to the preset risk threshold (e.g., level 5), which can be approximately equal to a certain range of a certain value (e.g., within the range of ±0.5 of 5). The gas company management platform inspects these 60 pipeline areas to be inspected and finds that 30 of them need to be cleaned. The gas company management platform can adjust the preset risk threshold = 5-30 / 60 = 4.5.

[0145] In some embodiments, if the proportion of inspection results that are equal to or approximately equal to the preset risk threshold and do not require pipe cleaning is greater than a preset proportion (e.g., 0.9), and the number of consecutive occurrences is greater than or equal to a preset number of times (e.g., 5 times), then the adjusted preset risk threshold can be the original risk threshold plus 0.5.

[0146] In some embodiments, the preset number of times may be determined by the gas company management platform based on historical inspection results. For example, in historical inspection results, if the proportion of inspection results that are equal to or approximately equal to the preset risk threshold and do not require cleaning is greater than a preset proportion (e.g., 0.9) for N consecutive times, and all subsequent pipeline inspection results are that cleaning is not required, the gas company management platform may determine N as the preset number of times. The preset proportion may be a preset value determined by the user and input into the gas company management platform.

[0147] Historical inspection results refer to the pipeline inspection data that occurred in the past. For more information about historical inspection results, see Figure 2 and related descriptions.

[0148] In some embodiments of the present specification, by determining the attachment influence degree, the attachment influence relationship between different gas pipeline areas that conforms to reality can be determined, thereby providing reliable data support for the subsequent classification of attachment influence levels and determination of attachment influence areas.

[0149] In some embodiments of the present specification, dynamically setting and adjusting the preset risk threshold according to historical inspection results can effectively ensure the cleaning efficiency and quality, and reduce the cleaning cost to a certain extent.

[0150] It should be noted that the above description of process 300 is only for example and illustration, 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 300 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0151] Figure 4 It is an exemplary schematic diagram of determining the risk level of attachments according to some embodiments of this specification.

[0152] like Figure 4 As shown, the gas company management platform can be further configured to construct a pipeline network simulation map 410; based on the pipeline network simulation map 410, the attachment risk level 430 (such as Figure 4 430-1 and 430-n shown).

[0153] The pipe network simulation graph 410 refers to a knowledge graph used to reflect the actual position relationship between the sensor and the gas pipeline. The knowledge graph refers to a data structure composed of nodes 411 and edges 412.

[0154] In some embodiments, the node 411 of the pipe network simulation map 410 can represent a sensor, and the edge 412 of the pipe network simulation map 410 can represent a gas pipeline between sensors. In some embodiments, in the pipe network simulation map 410, the node attributes of the node 411 can include the attachment influence, basic perception data, and pipeline location information, and the edge attributes can include the attachment risk level. In some embodiments, the edge 412 of the pipe network simulation map 410 can be a directed edge, and the direction of the edge 412 represents the gas flow direction in the gas pipeline. For more information about basic perception data, attachment influence, and attachment risk level, please refer to Figure 2 , Figure 3 Related description.

[0155] The pipeline location information refers to the geographical location of the gas pipeline where the sensor is located.

[0156] In some embodiments, the gas company management platform can construct a pipe network simulation map 410 based on the sensors and the gas pipelines between the sensors, using the attachment impact, basic sensing data and pipeline location information as node attributes and the attachment risk level as edge attributes. For example, the gas company management platform can set each sensor as each node of the pipe network simulation map 410. For another example, if there is a gas pipeline between the sensors, the nodes 411 corresponding to the sensors at both ends of the gas pipeline are connected to form the edge 412 of the pipe network simulation map 410.

[0157] In some embodiments, the gas company management platform can construct a pipe network simulation map 410 based on the distribution of the gas pipeline network in which the gas pipeline is located. For example, the gas company management platform can extract the location coordinates, pipe diameters, connection relationships, and other information of all gas pipelines in the target area through the GIS system (Geographic Information System), and then extract the location coordinates of each sensor in the gas pipeline network, and match this information with the gas pipelines in the GIS system; for each sensor, the gas company management platform can extract basic sensing data and other information of each sensor; finally, the gas company management platform can draw corresponding edges 412 in the pipe network simulation map 410 according to the actual connection relationship of the gas pipelines between the sensors.

[0158] The attachment analysis model 420 refers to a model for generating the attachment risk level 430. In some embodiments, the attachment analysis model 420 may be a machine learning model, such as a graph neural network model (GNN).

[0159] In some embodiments, the input of the deposition analysis model 420 may include the pipe network simulation map 410. In some embodiments, the output of the deposition analysis model 420 may include the deposition risk level 430, for example, the deposition risk level 430 corresponding to each edge 412 in the pipe network simulation map 410.

[0160] In some embodiments, the gas company management platform can train the attachment analysis model 420 based on a large number of first training samples with first labels. In some embodiments, the first training sample includes a sample pipe network simulation map. In some embodiments, the first label can be the historical attachment risk level corresponding to each edge 412 in the sample pipe network simulation map. In some embodiments, the gas company management platform inputs the first training sample into the initial attachment analysis model 420 to obtain the model prediction output corresponding to the first training sample. The gas company management platform constructs a loss function based on the model prediction output corresponding to the first training sample and the first label corresponding to the first training sample. The gas company management platform reversely updates the model parameters in the initial attachment analysis model 420 based on the value of the loss function and an optimization algorithm such as a gradient descent method. When the iteration end condition is met (for example, the loss function converges, the number of iterations reaches a preset iteration threshold, etc.), the gas company management platform ends the iteration and obtains the trained attachment analysis model 420.

[0161] In some embodiments, the gas company management platform can construct a first training sample based on the historical attachment influence, historical basic perception data and historical pipeline location information, and the historical attachment risk level 430 determined at historical time. In some embodiments, the historical attachment risk level can be determined based on the thickness of the attachment actually measured in the historical cleaning data. For example, the gas company management platform can preset them as different attachment risk levels 430 according to the thickness of the attachments actually detected. For example, if the thickness of the attachment is 3mm, then the corresponding attachment risk level 430 is level 5. In some embodiments, the different attachment risk levels 430 corresponding to the thickness of each attachment can be preset based on artificial experience. For example, the greater the actual measured thickness of the attachment of the gas pipeline, the higher the attachment risk level 430 corresponding to the gas pipeline, that is, the higher the attribute value (e.g., level, etc.) of the edge corresponding to this gas pipeline in the pipeline simulation map 410. In some embodiments, the gas company management platform can construct a first label based on the historical attachment risk level determined at historical time.

[0162] In some embodiments of the present specification, by constructing a pipeline simulation map using sensors as nodes and gas pipelines as edges, the spatial relationship between each gas pipeline in the gas pipeline network can be simulated, so that the attachment analysis model can learn the location characteristics of the gas pipeline; by predicting the attachment influence of each gas pipeline based on the pipeline simulation map and using the attachment analysis model, the attachment risk level of different gas pipelines can be evaluated more accurately.

[0163] Figure 5 is an exemplary schematic diagram of a prediction model according to some embodiments of the present specification.

[0164] In some embodiments, Figure 5 As shown, the gas company management platform can be further configured to determine the candidate operating parameters 520 of the fan based on the attachment risk level of the gas pipeline area and the inspection results; determine the predicted operating parameters 550 corresponding to the candidate operating parameters 520 through the prediction model 540 based on the candidate operating parameters 520, the pipeline diameter 510, and the basic perception data 530; and adjust the preset operating parameters based on the predicted operating parameters 550.

[0165] The candidate operating parameters 520 may be determined in a variety of ways. In some embodiments, the gas company management platform may determine a plurality of candidate operating parameters 520 by querying a fan parameter table based on the risk level of attachments in the gas pipeline area and the inspection results.

[0166] In some embodiments, the gas company management platform can construct a fan parameter table based on historical attachment risk levels and historical inspection results. For example, the gas company management platform can use different historical attachment risk levels and fan operating parameters that are proven to be effective under different historical inspection results as corresponding reference candidate operating parameters for query or use.

[0167] For example, if a certain fan operating parameter is applied to a gas pipeline area corresponding to an attachment risk level A and an inspection result M, and the attachment risk level corresponding to this gas pipeline area subsequently decreases or the attachment content in the inspection result decreases, the gas company management platform can set this fan operating parameter as the reference candidate fan parameter corresponding to the attachment risk level A and the inspection result M in the fan parameter table.

[0168] For more information about the candidate operating parameters 520, see Figure 3 and its related description.

[0169] The pipeline diameter 510 refers to the inner diameter of the gas pipeline area, that is, the diameter size of the internal space of the gas pipeline.

[0170] In some embodiments, the gas company management platform may determine the pipeline diameter corresponding to each gas pipeline area based on querying the pipeline diameter table 510. The pipeline diameter table refers to a list for storing each gas pipeline area and its corresponding pipeline diameter.

[0171] In some embodiments, the gas company management platform may also determine the pipeline diameter 510 through distance sensors (eg, laser rangefinders and / or ultrasonic sensors, etc.) deployed on the inspection robot.

[0172] For a description of the basic perception data 530, see Figure 3 and its related description.

[0173] The prediction model 540 refers to a model used to determine the prediction working parameter 550. In some embodiments, the prediction model 540 may be a machine learning model, such as a recurrent neural network (RNN).

[0174] In some embodiments, the inputs to the prediction model 540 may include the candidate operating parameters 520, the pipe diameter 510, and the basic sensory data 530. In some embodiments, the outputs of the prediction model 540 may include the predicted operating parameters 550.

[0175] In some embodiments, the predicted operating parameter 550 may include at least one of an estimated amount of impurity deposition, an estimated gas pressure stability, and / or an estimated gas flow rate stability.

[0176] Impurity deposition refers to the deposition amount (e.g., deposition thickness) of pipeline attachments (e.g., solid polymers, dust, etc.) in the gas pipeline. Estimated impurity deposition refers to the estimated impurity deposition amount.

[0177] Gas pressure stability is an indicator used to measure the pressure fluctuations in the gas pipeline. For example, the higher the gas pressure stability, the smoother the gas flow. Estimated gas pressure stability is the estimated gas pressure stability.

[0178] Gas flow rate stability refers to an indicator used to measure the fluctuation of gas flow rate in the gas pipeline. For example, the higher the gas flow rate stability, the smoother the gas flow. Estimated gas flow rate stability refers to the estimated gas flow rate stability.

[0179] For more information about the predicted working parameters 550, please refer to Figure 2 , Figure 3 and its related description.

[0180] In some embodiments, the gas company management platform may train the prediction model 540 based on a large number of second training samples with second labels by using a gradient descent method or the like.

[0181] In some embodiments, the second training sample may include sample working parameters, sample pipe diameter, sample basic sensing data, etc. at the first historical time. In some embodiments, the second label may be the amount of impurity deposition, gas pressure stability, and gas flow rate stability at the second historical time corresponding to the second training sample. The first historical time is earlier than the second historical time.

[0182] In some embodiments, the gas company management platform can obtain the historical pipeline diameter, historical basic perception data and historical working parameters collected at the first historical time from the storage unit to construct a second training sample. In some embodiments, the gas company management platform can construct a second label based on the impurity deposition amount, gas pressure stability and gas flow rate stability corresponding to the second training sample obtained based on actual monitoring at the second historical time. Based on the basic perception data of the first historical time, candidate working parameters and inspection results corresponding to the second training sample, the impurity deposition amount, gas pressure stability and gas flow rate stability at the second historical time are determined; wherein the stability can be determined based on the fluctuation situation, for example, if the fluctuation is within 5%, the stability is 1, and if the fluctuation is between 5%-15%, the stability is 0.9.

[0183] In some embodiments, the gas company management platform can use different sets of training samples to cross-train the prediction model 540. In some embodiments, the gas company management platform can determine different sets of training data sets based on the amount of impurity deposition, gas pressure stability, and / or gas flow rate over historical time.

[0184] For example, the gas company management platform can set the second training samples whose floating trends in the impurity deposition amount, gas pressure stability and / or gas flow rate are less than the floating threshold in the historical time as the training samples of the same group, set the second training samples whose floating trends are not less than the floating threshold as the training samples of the same group, and cross-train the prediction model 540 based on the training samples of different groups. In some embodiments, the floating threshold can be preset based on manual experience.

[0185] In some embodiments, the gas company management platform can train the initial prediction model 540 through multiple rounds of iterations, wherein at least one round of iterations includes: the gas company management platform selects one or more groups of second training samples, inputs one or more groups of second training samples into the initial prediction model 540, and obtains the model prediction output corresponding to one or more groups of second training samples. The gas company management platform substitutes the model prediction output corresponding to one or more groups of second training samples and one or more groups of second labels corresponding to the one or more second training samples into the formula of the predefined loss function to calculate the value of the loss function. The gas company management platform reversely updates the model parameters in the initial prediction model 540 based on the value of the loss function and optimization algorithms such as the gradient descent method. When the iteration end condition is met (for example, the loss function converges, the number of iterations reaches a preset iteration threshold, etc.), the gas company management platform ends the iteration and obtains the trained prediction model 540.

[0186] In some embodiments, different groups of training samples have different corresponding learning rates during the training process. For example, the learning rate of the group of training samples with a larger number of training samples may be higher than that of the group of training samples with a smaller number of training samples.

[0187] In some embodiments, the gas company management platform may evaluate the predicted operating parameters 550 in a variety of ways based on the estimated impurity deposition amount, the estimated gas pressure stability, and the estimated gas flow rate stability to screen matching predicted operating parameters 550 .

[0188] In some embodiments, the predicted working parameter 550 is negatively correlated with the estimated impurity deposition amount, and positively correlated with the estimated gas pressure stability and the estimated gas flow rate stability. For example, the gas company management platform can evaluate the predicted working parameter 550 based on the following formula (3): (3) in, Indicates the evaluation score of the preset working parameters, , and They represent the estimated impurity deposition amount, the estimated gas pressure stability and the estimated gas flow rate stability respectively. , and They respectively represent the weight coefficient of the estimated impurity deposition amount, the weight coefficient of the estimated gas pressure stability and the weight coefficient of the estimated gas flow rate stability.

[0189] In some embodiments, the gas company management platform can determine the gas supply data in a variety of ways. , and For example, the gas company management platform can perform statistical analysis on historical gas supply data. When the change amplitude of impurity deposition, gas pressure, and gas flow rate in the historical gas supply data reaches the amplitude threshold (for example, 20%), the gas company management platform can determine the impact of downstream gas usage. , and .

[0190] In some embodiments, the gas company management platform adjusts the , and For example, if a user complains that the gas pressure is insufficient, the gas company management platform can appropriately increase the pressure. In some embodiments, the amplitude threshold may be preset based on manual experience.

[0191] In some embodiments, the Can be negative. and Can be a positive number.

[0192] In some embodiments, the gas company management platform can select suitable predicted operating parameters 550 from multiple groups of predicted operating parameters 550, and use the corresponding preset operating parameters as the preset operating parameters of each wind turbine. For example, the gas company management platform can evaluate the predicted operating parameters 550 using the above formula (3), and set the preset operating parameters corresponding to the predicted operating parameters 550 with the highest evaluation score as the current preset operating parameters of the target wind turbine.

[0193] In some embodiments of the present specification, by introducing a prediction model to predict the working parameters of the fan, the accuracy of the predicted working parameters of the fan can be improved, thereby improving the working performance of the fan. By using a large amount of historical data to train and optimize the prediction model, the prediction model can be made to more accurately predict the pipe data of the gas pipeline in the future time period (for example, the amount of impurity deposition, the stability of the gas pressure, the stability of the gas flow rate, etc.), so as to select appropriate preset working parameters of the fan, thereby improving the cleaning effect of the fan.

[0194] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of 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 suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0195] 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, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A pipeline attachment monitoring method based on smart gas Internet of Things, characterized in that: The method is executed by a gas company management platform of a smart gas pipeline attachment monitoring system, and includes: Issue a data collection instruction to control the sensors on the gas pipeline or gas valve to collect basic perception data, and transmit it to the gas equipment object platform for aggregation and storage. The basic perception data is uploaded to the gas company management platform through the gas equipment object platform according to the preset reporting rules; Determining the attachment risk area of ​​the gas pipeline according to the basic sensing data; According to the attachment risk area, determine the pipeline area to be inspected, generate a pipeline inspection instruction and send it to the gas pipeline network maintenance object platform; the gas pipeline network maintenance object platform controls the inspection robot to inspect the pipeline area to be inspected based on the pipeline inspection instruction, and collects the inspection results uploaded by the inspection robot; Receiving the inspection results collected by the gas pipeline network maintenance object platform, generating cleaning data of the gas pipeline and sending it to the government safety supervision management platform; and, Receive the cleaning confirmation data returned by the government safety supervision management platform, generate a cleaning instruction based on the cleaning confirmation data, and perform a cleaning operation on part of the gas pipeline according to the cleaning instruction.

2. The method according to claim 1, characterized in that The determining, according to the basic sensing data, the attachment risk area of ​​the gas pipeline includes: Generate geographical location information and gas flow information according to the basic sensing data; wherein the geographical location information includes geographical location information of the gas pipeline area and / or geographical location information of the sensor; Determine the impact of attachments based on historical pigging data; Determining the risk level of the attachment in the gas pipeline area according to the geographical location information, the gas flow information, and the impact of the attachment; Based on the attachment risk level, the attachment risk area is determined.

3. The method according to claim 1, characterized in that: The method further comprises: Generate pipeline inspection sequence based on the risk level of attachments and geographical location information of the gas pipeline area; Based on the pipeline inspection sequence, the inspection robot performs an on-site inspection of the pipeline area to be inspected, and obtains the inspection result.

4. The method according to claim 3, characterized in that The method further comprises: In response to the inspection result that the attachment content meets the preset content condition, the pipeline inspection order of the non-target pipeline is adjusted; wherein the non-target pipeline is associated with the gas pipeline area corresponding to the inspection result, and the non-target pipeline is determined based on the attachment influence.

5. The method according to claim 1, characterized in that The method further comprises: Based on the cleaning confirmation data returned by the government safety supervision management platform, a fan control instruction is generated and sent to the gas pipeline network maintenance object platform; Based on the fan control instruction, the fan is controlled to operate with preset working parameters through the gas pipeline network maintenance object platform.

6. A pipeline attachment monitoring system based on smart gas Internet of Things, characterized in that: The pipeline attachment monitoring system based on the smart gas Internet of Things includes one or more of a gas company management platform, a gas equipment object platform, a gas pipeline network maintenance object platform, and a government safety supervision management platform; The gas company management platform is configured as follows: Issue a data collection instruction to control the sensor on the gas pipeline or gas valve to collect basic perception data, and transmit it to the gas equipment object platform, and receive the basic perception data uploaded by the gas equipment object platform; Determining the attachment risk area of ​​the gas pipeline according to the basic sensing data; According to the attachment risk area, determine the pipeline area to be inspected, generate a pipeline inspection instruction and send it to the gas pipeline network maintenance object platform; Receiving the inspection results collected by the gas pipeline network maintenance object platform, generating cleaning data of the gas pipeline and sending it to the government safety supervision management platform; Receiving the cleaning confirmation data returned by the government safety supervision management platform, generating a cleaning instruction based on the cleaning confirmation data, and performing a cleaning operation on part of the gas pipeline according to the cleaning instruction; The gas equipment object platform is configured as follows: Receive the basic sensing data transmitted by the sensor, summarize and store it, and upload the basic sensing data to the gas company management platform according to preset reporting rules; The gas network maintenance object platform is configured as follows: Receive the pipeline inspection instruction sent by the gas company management platform, control the inspection robot to inspect the pipeline area to be inspected, and collect the inspection results uploaded by the inspection robot, and send the inspection results to the gas company management platform; The government security supervision management platform is configured as follows: Receive the cleaning data sent by the gas company management platform, generate and send the cleaning confirmation data to the gas company management platform.

7. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: Generate geographical location information and gas flow information according to the basic sensing data; wherein the geographical location information includes geographical location information of the gas pipeline area and / or geographical location information of the sensor; Determine the impact of attachments based on historical pigging data; Determining the risk level of the attachment in the gas pipeline area according to the geographical location information, the gas flow information, and the impact of the attachment; Based on the attachment risk level, the attachment risk area is determined.

8. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: Generate pipeline inspection sequence based on the risk level of attachments and geographical location information of the gas pipeline area; Based on the pipeline inspection sequence, the inspection robot performs an on-site inspection of the pipeline area to be inspected, and obtains the inspection result.

9. The system according to claim 8, characterized in that The gas company management platform is further configured as follows: In response to the inspection result that the attachment content meets the preset content condition, the pipeline inspection order of the non-target pipeline is adjusted; wherein the non-target pipeline is associated with the gas pipeline area corresponding to the inspection result, and the non-target pipeline is determined based on the attachment influence.

10. The system according to claim 6, characterized in that The gas company management platform is further configured as follows: Based on the cleaning confirmation data returned by the government safety supervision management platform, a fan control instruction is generated and sent to the gas pipeline network maintenance object platform; Based on the fan control instruction, the fan is controlled to operate with preset working parameters through the gas pipeline network maintenance object platform.

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