Intelligent gas pipeline sampling monitoring method, Internet of Things system and storage medium

Abstract

The invention provides an intelligent gas pipeline sampling monitoring method, an Internet of Things system and a storage medium, and relates to the technical field of gas supervision. The method is executed by a government safety supervision management platform, and comprises the following steps: acquiring pipeline sensing data; determining sampling parameters based on the pipeline sensing data; and in response to acquisition of the sampling data from the sampling device, determining a fault detection instruction based on the sampling data, and sending the fault detection instruction to the gas maintenance object platform to arrange manual inspection. The Internet of Things system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform, a gas equipment object platform and a gas maintenance object platform. The method can be operated after a computer instruction stored in a computer readable storage medium is read. The gas in the pipeline can be reasonably sampled, so that the running state condition of the pipeline can be accurately evaluated, and the safety and stability of the pipeline are favorably maintained.

Description

Technical Field

[0001] This specification relates to the field of gas supervision, and in particular to a smart gas pipeline sampling and monitoring method, an Internet of Things system and a storage medium. Background Art

[0002] With the development of society, gas has become an indispensable part of people's daily life. To ensure the safety of gas use, strict supervision of gas pipelines is particularly important. The focus of this supervision process is to sample the gas in the pipeline and deeply analyze the data obtained from the sampling. However, the currently commonly used sampling method relies on manual operation of related equipment. This method is not only time-consuming and labor-intensive, but may also introduce errors due to human factors, affecting the accuracy of the sampling data, thereby misleading the accurate assessment of the pipeline operation status.

[0003] Therefore, it is necessary to provide a smart gas pipeline sampling and monitoring method, an Internet of Things system and a storage medium to realize dynamic adjustment of sampling parameters so as to complete the reasonable sampling of the gas in the pipeline, thereby accurately evaluating the status of pipeline operation, which is conducive to maintaining the safety and stability of the pipeline and ensuring the safety of gas transportation. Summary of the invention

[0004] In order to solve the problem of how to reasonably sample the gas in the gas pipeline in order to accurately evaluate the status of the pipeline operation, this specification provides a smart gas pipeline sampling and monitoring method, an Internet of Things system and a storage medium.

[0005] The invention content includes a smart gas pipeline sampling and monitoring Internet of Things system, which includes: a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, a gas equipment object platform and a gas maintenance object platform; the gas equipment object platform includes at least one sampling device and at least one pipeline monitoring device; the gas maintenance object platform includes at least one staff interaction device; the government safety supervision object platform includes the gas company management platform and key gas-using enterprises; the government safety supervision management platform is configured to: obtain pipeline perception data of at least one section of the pipeline collected and uploaded by the gas equipment object platform via the gas company sensor network platform, the gas company management platform, and the government safety supervision sensor network platform in sequence; determine sampling parameters based on the pipeline perception data of the at least one section of the pipeline, send the sampling parameters to the government safety supervision object platform, and further send them to the at least one sampling device by the government safety supervision object platform; in response to obtaining at least one sampling data from the at least one sampling device, determine a fault detection instruction based on the at least one sampling data, send the fault detection instruction to the gas maintenance object platform, and the gas maintenance object platform arranges manual inspection.

[0006] The invention content includes a smart gas pipeline sampling and monitoring method, which is implemented based on a smart gas pipeline sampling and monitoring Internet of Things system. The method is executed by a government safety supervision management platform in the smart gas pipeline sampling and monitoring Internet of Things system. The method includes: obtaining pipeline perception data of at least one section of the pipeline collected and uploaded by the gas equipment object platform via the gas company sensor network platform, the gas company management platform, and the government safety supervision sensor network platform in sequence; determining sampling parameters based on the pipeline perception data of the at least one section of the pipeline, sending the sampling parameters to the government safety supervision object platform, and further sending them to the at least one sampling device by the government safety supervision object platform; in response to obtaining at least one sampling data from the at least one sampling device, determining a fault detection instruction based on the at least one sampling data, sending the fault detection instruction to the gas maintenance object platform, and the gas maintenance object platform arranges manual inspection.

[0007] The invention content includes a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a smart gas pipeline sampling and monitoring method.

[0008] The beneficial effects brought about by the above invention content include but are not limited to: (1) Through pipeline sensing data, the sampling parameters can be reasonably determined so that the sampling equipment can accurately sample to obtain effective sampling data, and analyze the sampling data to determine the fault detection instructions, which is conducive to accurately assessing the fault conditions of pipeline operation and indicating the direction for subsequent troubleshooting, thereby ensuring the effective maintenance of the safety and stability of the pipeline; (2) Through pipeline sensing data, the abnormal pipeline and its corresponding abnormal area are determined, and then the sampling parameters are determined, which can make the sampling more targeted, and can further reduce the number of sampling devices opened and reduce resource consumption; (3) Through machine learning models, the sampling detection capability of the sampling equipment can be automatically and accurately determined; (4) Based on the current pipeline sensing data, the pipeline sensing data for a period of time in the future is determined, and then the sampling time is determined, which can make the determined sampling time more reasonable, reduce the impact of sampling on the gas pipeline network, and reduce the risks and economic benefits caused by sampling. 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 schematic diagram of the platform structure of the smart gas pipeline sampling and monitoring Internet of Things system shown in some embodiments of this specification; Figure 2 is an exemplary flow chart of a smart gas pipeline sampling and monitoring method according to some embodiments of this specification; Figure 3 is an exemplary schematic diagram of determining sampling parameters according to some embodiments of this specification; Figure 4 is an exemplary schematic diagram of a detection capability evaluation model according to some embodiments of this specification; Figure 5 is an exemplary schematic diagram of determining sampling time according to some embodiments of the present specification. DETAILED DESCRIPTION

[0010] The following is a brief introduction to the drawings required for describing the embodiments. The drawings do not represent all implementation methods.

[0011] The "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the words can be replaced by other expressions.

[0012] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0013] When the operations performed in the embodiments of this specification are described in steps, unless otherwise specified, the order of the steps is interchangeable, steps may be omitted, and other steps may be included in the operation process.

[0014] Figure 1 It is a schematic diagram of the platform structure of the smart gas pipeline sampling and monitoring Internet of Things system shown in some embodiments of this specification.

[0015] In some embodiments, Figure 1 As shown, the smart gas pipeline sampling and monitoring Internet of Things system 100 may include a government safety supervision management platform 110, a government safety supervision sensor network platform 120, a government safety supervision object platform 130, a gas company sensor network platform 140, a gas equipment object platform 150 and a gas maintenance object platform 160.

[0016] The government safety supervision and management platform 110 is a platform for supervising and safely managing the gas pipeline network. The government safety supervision and management platform 110 can be used to assess various possible fault risks in the gas pipeline network.

[0017] In some embodiments, the government safety supervision management platform 110 can be configured to: obtain pipeline sensing data of at least one section of the pipeline collected and uploaded by the gas equipment object platform 150 via the gas company sensor network platform 140, the gas company management platform 131, and the government safety supervision sensor network platform 120 in sequence; determine sampling parameters based on the pipeline sensing data of at least one section of the pipeline, send the sampling parameters to the government safety supervision object platform 130, and further send them to at least one sampling device by the government safety supervision object platform 130; in response to obtaining at least one sampling data from at least one sampling device, determine a fault detection instruction based on at least one sampling data, and send the fault detection instruction to the gas maintenance object platform 160, and the gas maintenance object platform 160 arranges manual inspection.

[0018] In some embodiments, the government safety supervision management platform 110 can be further configured to: determine abnormal data items based on at least one sampling data and standard sampling data; determine the detection content based on the abnormal data items, and determine the pipeline sampling position as the inspection pipeline position of the re-inspected pipeline.

[0019] In some embodiments, the government safety supervision management platform 110 can be further configured to: determine the re-inspected pipeline whose pipeline penetration degree in the fault detection instruction is greater than a preset degree threshold as a high-risk pipeline; the preset degree threshold is related to the average penetration degree of the gas pipeline network; and increase the monitoring frequency of pipeline monitoring equipment for high-risk pipelines in a preset future time period.

[0020] In some embodiments, the government safety supervision management platform 110 can be further configured to: determine at least one abnormal pipeline section and its corresponding abnormal area based on pipeline sensing data of at least one pipeline section; determine sampling parameters based on at least one abnormal pipeline section and its corresponding abnormal area.

[0021] In some embodiments, the government safety supervision management platform 110 can be further configured to: for a section of abnormal pipeline and its corresponding abnormal area, determine the sampling detection type based on the pipeline sensing data of the abnormal pipeline and its corresponding abnormal area; in response to the presence of multiple sampling devices on the abnormal pipeline, evaluate the sampling detection capability of the sampling device based on the pipeline sensing data and sampling detection type of the abnormal pipeline; and determine to start the sampling device based on the sampling detection capability of at least one sampling device.

[0022] In some embodiments, the government safety supervision management platform 110 may be further configured to determine the sampling detection capability of the sampling equipment through a detection capability evaluation model based on the pipeline sensing data and sampling detection type of the abnormal pipeline.

[0023] In some embodiments, the government safety supervision management platform 110 can be further configured to: determine the pipeline perception data of at least one section of the pipeline for a period of time in the future based on the pipeline perception data of at least one section of the pipeline; determine the sampling time based on the pipeline perception data of at least one section of the pipeline for a period of time in the future.

[0024] In some embodiments, the government safety supervision management platform 110 can be further configured to: determine at least one candidate sampling time point; determine the sampling stability of at least one candidate sampling time point based on pipeline sensing data of at least one section of the pipeline for a period of time in the future; determine the sampling time based on the sampling stability of at least one candidate sampling time point.

[0025] In some embodiments, the government safety supervision management platform 110 can be further configured to: construct a gas pipeline network map; the nodes of the gas pipeline network map include gas pipelines, gas supply equipment, and gas using equipment, and the edges of the gas pipeline network map are connecting edges of nodes through which gas circulates with each other; based on the gas pipeline network map, determine the sampling stability of at least one candidate sampling time point through a sampling stability evaluation model.

[0026] In some embodiments, the government safety supervision management platform 110 interacts bidirectionally downward with the government safety supervision sensor network platform 120. For example, the government safety supervision management platform 110 may send sampling parameters to the government safety supervision sensor network platform 120, and obtain sampling data related to the sampling parameters from the government safety supervision sensor network platform 120.

[0027] In some embodiments, the government security supervision management platform 110 may also include a processor. The processor may process data and / or information obtained from other platforms. The processor may execute program instructions based on these data, information and / or processing results to perform one or more functions described in this application. In some embodiments, the processor may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-core processing device). As an example only, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), etc. or any combination thereof.

[0028] The government safety supervision sensor network platform 120 refers to a functional platform for monitoring and transmitting gas pipeline sampling related data. In some embodiments, the government safety supervision sensor network platform 120 can be configured as a communication base station, a router, a wireless device, etc., and operates based on a communication network.

[0029] In some embodiments, the government safety supervision sensor network platform 120 interacts bidirectionally downward with the government safety supervision object platform 130. For example, the government safety supervision sensor network platform 120 may send sampling parameters to the government safety supervision object platform 130.

[0030] The government safety supervision object platform 130 refers to an object platform for sensing information generation and controlling information execution. In some embodiments, the government safety supervision object platform 130 can be used to store pipeline sensing data and sampling data in the gas pipeline network, as well as sampling parameters issued by the government safety supervision management platform 110.

[0031] In some embodiments, the government safety supervision object platform 130 may include a gas company management platform 131 and key gas-using enterprises 132 .

[0032] The gas company management platform 131 refers to a comprehensive management platform for relevant information of the gas company, which can be used to manage gas company and pipeline sampling related parameters.

[0033] Key gas-using enterprises 132 refer to enterprises that require special attention in terms of gas usage, such as chemical plants that use a large amount of gas.

[0034] In some embodiments, the gas company management platform 131 can interact bidirectionally with the gas company sensor network platform 140 .

[0035] The gas company sensor network platform 140 refers to a comprehensive management platform for sensor information of the gas company. In some embodiments, the gas company sensor network platform 140 can be configured as a communication network or a gateway.

[0036] In some embodiments, the gas company sensor network platform 140 can interact with the gas company management platform 131 upwards in a two-way manner, and interact with the gas equipment object platform 150 and the gas maintenance object platform 160 downwards in a two-way manner. For example, the gas company sensor network platform 140 can obtain pipeline sensing data of at least one section of the pipeline from the gas equipment object platform 150. For another example, the gas company sensor network platform 140 can send the acquired fault detection instruction to the gas maintenance object platform 160.

[0037] The gas equipment object platform 150 refers to a functional platform for performing pipeline monitoring and sampling. In some embodiments, the gas equipment object platform 150 may include at least one sampling device and at least one pipeline monitoring device.

[0038] Sampling equipment refers to equipment for sampling and testing gas in gas pipelines, which can be used to obtain sampling data. For example, sampling equipment can integrate temperature sensors, flow rate detectors, pressure sensors, concentration detectors, gas analyzers, etc.

[0039] In some embodiments, the sampling device may be deployed in a preset pipeline. The deployment position of the sampling device may be obtained by a positioning sensor installed on the sampling device, or may be directly obtained from relevant positioning records during deployment and installation (e.g., point records reserved when deploying a gas pipeline network).

[0040] In some embodiments, a plurality of sampling devices may be deployed on the gas pipeline network, and one or more sampling devices may be deployed on a section of pipeline. The sampling devices may detect the pipeline conditions within the sampling range.

[0041] Pipeline monitoring equipment refers to equipment that monitors the gas in the gas pipeline and can be used to obtain pipeline sensing data. For example, pipeline monitoring equipment can integrate temperature sensors, flow rate detectors, pressure sensors, etc.

[0042] The deployment of pipeline monitoring equipment is similar to that of sampling equipment and will not be elaborated here.

[0043] The gas maintenance object platform 160 refers to a platform related to gas maintenance. In some embodiments, the gas maintenance object platform 160 includes a staff interaction device.

[0044] The staff interaction device refers to a device that enables interaction with the staff. For example, a mobile phone, a computer, etc. The staff refers to a person who is engaged in work related to the gas pipeline network. For example, a safety officer, a maintenance worker, a storage and transportation worker, etc. In some embodiments, the staff interaction device can obtain a fault detection instruction via the gas maintenance object platform 160 and arrange a manual inspection.

[0045] For more information about the above parameters (such as sampling data, pipeline sensing data, fault detection instructions, etc.), please refer to Figures 2 to 5 Related description.

[0046] Some embodiments of this specification, based on the smart gas pipeline sampling and monitoring Internet of Things system 100, can form an information operation closed loop between various functional platforms, and coordinate and operate regularly under the unified management of the government security supervision and management platform, thereby realizing the informatization and intelligence of smart gas pipeline sampling.

[0047] Figure 2 is an exemplary flow chart of a smart gas pipeline sampling and monitoring method according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps S210 to S230.

[0048] In some embodiments, the smart gas pipeline sampling and monitoring method can be implemented based on the smart gas pipeline sampling and monitoring Internet of Things system 100 and executed by the government security supervision and management platform 110. For example, it is executed by a processor.

[0049] Step S210, obtaining pipeline sensing data of at least one section of the pipeline collected and uploaded by the gas equipment object platform via the gas company sensor network platform, the gas company management platform, and the government safety supervision sensor network platform in sequence.

[0050] Pipeline sensing data refers to sensing data related to pipelines, where the pipeline may refer to a gas pipeline.

[0051] In some embodiments, the pipeline sensing data may include pipeline temperature, pipeline gas pressure, and pipeline gas flow rate.

[0052] Pipeline temperature, pipeline air pressure, and pipeline gas flow rate refer to the temperature, air pressure, and gas flow rate inside the pipeline respectively.

[0053] In some embodiments, the processor may obtain pipeline sensing data from pipeline monitoring equipment in a gas equipment object platform via a gas company sensor network platform.

[0054] In some embodiments, one or more pipeline monitoring devices may be deployed in a section of pipeline. When there are multiple pipeline monitoring devices in a section of pipeline, the pipeline sensing data of the pipeline may be a sequence of pipeline sensing data acquired by monitoring of multiple pipeline monitoring devices.

[0055] Step S220, determining sampling parameters based on pipeline sensing data of at least one section of the pipeline, sending the sampling parameters to the government safety supervision object platform, and further sending the sampling parameters to at least one sampling device by the government safety supervision object platform.

[0056] Sampling parameters refer to parameters related to the operating conditions of the sampling equipment.

[0057] In some embodiments, the sampling parameters may include at least one open sampling device and its corresponding pipeline sampling position.

[0058] The open sampling equipment refers to the sampling equipment that needs to be in the open state (i.e., sampling operations can be carried out).

[0059] The pipeline sampling position refers to the position in the pipeline where the sampling device is turned on for sampling, for example, the deployment position of the turned-on sampling device.

[0060] In some embodiments, the processor may determine the sampling parameters in a variety of ways based on the pipeline sensing data of at least one section of the pipeline. For example, the processor may calculate the data fluctuation values ​​corresponding to each of the pipeline sensing data of multiple pipelines (e.g., calculating the temperature fluctuation values ​​of the pipeline temperature of multiple pipelines, the pressure fluctuation values ​​of the pipeline gas pressure, and the gas flow rate fluctuation values ​​of the pipeline gas flow rate), and determine the sampling parameters based on the data fluctuation values.

[0061] The data fluctuation value refers to a value reflecting the fluctuation of the pipeline sensing data. In some embodiments, the data fluctuation value can be represented by a range or a variance. For example, the temperature fluctuation value can be represented by the variance of the pipeline temperature of all pipelines.

[0062] Exemplarily, if the data fluctuation values ​​corresponding to each item of pipeline perception data are not greater than the preset fluctuation threshold, sampling may not be performed, that is, the sampling parameter may be determined to be 0 or null; if there are one or more data fluctuation values ​​corresponding to one or more items of pipeline perception data that are greater than the preset fluctuation threshold, the sampling device of the default pipeline is turned on for sampling, that is, the sampling device in the default pipeline is determined to be the turned-on sampling device, and the corresponding pipeline sampling position is confirmed.

[0063] The default pipeline may refer to an important pipeline in the gas network. The preset fluctuation threshold refers to the minimum value of the data fluctuation value of the pipeline sensing data that is preset. The default pipeline and the preset fluctuation threshold may be set by the system default or determined by the technician based on experience and reference to historical data evaluation.

[0064] In some embodiments, the processor may also determine at least one abnormal pipeline section and its corresponding abnormal area based on the pipeline sensing data of at least one pipeline section; and determine sampling parameters based on at least one abnormal pipeline section and its corresponding abnormal area. For more information about this section, please refer to Figure 3 The corresponding description.

[0065] In some embodiments, the sampling parameters also include sampling time, and the processor may also determine the pipeline sensing data of at least one section of the pipeline for a period of time in the future based on the pipeline sensing data of at least one section of the pipeline; and determine the sampling time based on the pipeline sensing data of at least one section of the pipeline for a period of time in the future. For more information about this part, please refer to Figure 5 The corresponding description.

[0066] Step S230, in response to obtaining at least one sampling data from at least one sampling device, determining a fault detection instruction based on the at least one sampling data, and sending the fault detection instruction to the gas maintenance object platform, so that the gas maintenance object platform arranges manual inspection.

[0067] The sampling data refers to data related to the gas obtained by the sampling device, for example, at least one of the gas flow rate, gas temperature, gas pressure, gas concentration and gas composition.

[0068] Among them, the gas flow rate, gas temperature, and gas pressure in the sampling data can correspond to the pipeline gas flow rate, pipeline temperature, and pipeline gas pressure in the pipeline sensing data, respectively. The two corresponding items have the same meaning, but due to differences in sampling equipment and pipeline monitoring equipment (for example, the default sampling equipment continues to work normally, while the pipeline monitoring equipment may fail, etc.), the corresponding items may have numerical differences.

[0069] Gas concentration refers to the concentration of gas inside the pipeline, for example, 5%.

[0070] Gas fuel composition refers to the fuel composition of the gas in the pipeline. For example, the gas fuel composition of the pipeline that transports natural gas mainly includes methane.

[0071] In some embodiments, the processor may acquire the sampling data by turning on the sampling device.

[0072] The fault detection instruction refers to an instruction for guiding the staff to perform further fault inspection. In some embodiments, the fault detection instruction may include at least one re-inspected pipeline and the inspection pipeline position and corresponding inspection content of at least one re-inspected pipeline.

[0073] The re-inspected pipeline refers to a pipeline that needs to be inspected again. The inspection pipeline position refers to a position where the re-inspected pipeline is inspected. For example, the inspection pipeline position may be a pipeline sampling position of the re-inspected pipeline.

[0074] Inspection content refers to the items that need to be inspected, such as pipeline tightness, etc.

[0075] In some embodiments, the processor may determine the fault detection instruction in a variety of ways based on at least one sampled data. For example, the processor may search for historical sampled data with a high similarity to the first data of the current sampled data in the historical data based on the current sampled data, and use the fault detection instruction corresponding to the historical sampled data as the current fault detection instruction. The first data similarity may be represented by a vector distance.

[0076] In some embodiments, the processor may determine an abnormal data item based on at least one sampling data and standard sampling data; determine the detection content based on the abnormal data item, and determine the pipeline sampling position as the inspection pipeline position of the re-inspected pipeline.

[0077] Standard sampling data refers to sampling data under normal circumstances. For example, standard sampling data may include standard gas flow rate, standard gas temperature, standard gas pressure, standard gas fuel gas concentration, and standard gas fuel gas composition.

[0078] In some embodiments, the standard sampling data may be set by the system default or by a technician based on historical data. For example, the processor or technician may select a historical sampling parameter that is highly similar to the current sampling parameter, and the historical gas flow rate that appears most frequently in the corresponding historical sampling data as the standard gas flow rate.

[0079] The abnormal sampled data may be sampled data whose similarity with the standard sampled data is lower than a preset similarity threshold.

[0080] In some embodiments, the processor may calculate the similarity between each sampled data and the standard sampled data, and determine the sampled data whose similarity is lower than a preset similarity threshold as abnormal sampled data. The similarity may be represented by a vector distance, etc. The preset similarity threshold may be a preset distance threshold. For example, if the vector distance between the sampled data and the standard sampled data is lower than a preset distance threshold, the sampled data is abnormal sampled data.

[0081] In some embodiments, the preset similarity threshold may be related to the average penetration degree of the gas pipeline network. For example, the larger the average penetration degree of the gas pipeline network, the larger the preset similarity threshold may be.

[0082] The average penetration of the gas network refers to the average penetration of each pipeline in the gas network. For example, 3, etc.

[0083] The pipeline penetration of the current pipeline refers to the number of other pipelines corresponding to the gas flowing into the current pipeline from other pipelines.

[0084] An abnormal data item refers to a data item that specifically contains an abnormality in the abnormal sampling data.

[0085] In some embodiments, the processor may calculate a first difference between each data item in the abnormal sampled data and each corresponding data item in the standard sampled data, and determine a data item whose first difference is greater than a corresponding first difference threshold as an abnormal data item.

[0086] In some embodiments, the first difference may include a gas flow rate difference, a gas temperature difference, a gas pressure difference, a gas concentration difference, and a gas composition difference, and the first difference threshold may include a gas flow rate difference threshold, a gas temperature difference threshold, a gas pressure difference threshold, a gas concentration difference threshold, and a gas composition difference threshold.

[0087] The gas flow rate difference may refer to the absolute value of the difference between the gas flow rate in the abnormal sampling data and the standard gas flow rate in the standard sampling data. The same applies to the gas temperature difference, gas pressure difference, and gas concentration difference. The gas composition difference may refer to the difference in the number of component types between the gas fuel gas component in the abnormal sampling data and the standard gas fuel gas component in the standard sampling data.

[0088] The gas velocity difference threshold may refer to the maximum value of the gas velocity difference under normal circumstances. The same applies to the gas temperature difference threshold, the gas pressure difference threshold, the gas concentration difference threshold, and the gas composition difference threshold.

[0089] In some embodiments, the processor may determine the detection content based on the abnormal data item by querying the first preset relationship table. The first preset relationship table includes the abnormal data item and the corresponding detection content. The first preset relationship table may be constructed based on historical data. For example, for a historical abnormal data item in the historical data, the processor may count the corresponding one or more subsequent historical detection contents actually performed, determine the historical detection content with the highest number of occurrences as the historical detection content corresponding to the historical abnormal data item, traverse multiple historical abnormal data items, and construct the first preset relationship table.

[0090] In some embodiments, the processor may determine an open sampling device corresponding to the abnormal sampling data, and determine a pipeline sampling position corresponding to the open sampling device as an inspection pipeline position of the re-inspected pipeline.

[0091] In some embodiments of the present specification, by calculating the similarity between the sampling data and the standard sampling data, abnormal sampling data and abnormal data items can be accurately determined. At the same time, the preset similarity threshold can be flexibly adjusted according to the average penetration degree of the gas pipeline network to identify subtle differences and improve the reliability of the identified re-inspected pipeline.

[0092] In some embodiments, the processor may also determine the re-inspected pipeline whose pipeline penetration degree in the fault detection instruction is greater than a preset degree threshold as a high-risk pipeline; and increase the monitoring frequency of the pipeline monitoring equipment of the high-risk pipeline in a preset future period.

[0093] The preset degree threshold refers to a preset maximum value of the pipeline penetration degree, for example, 8.

[0094] In some embodiments, the preset degree threshold may be related to the average penetration degree of the gas network. For example, the preset degree threshold may be equal to the average penetration degree of the gas network. For another example, the preset degree threshold may be positively correlated to the average penetration degree of the gas network.

[0095] High-risk pipelines refer to pipelines with a higher risk of pipeline failure, which may include pipeline gas leakage, pipeline blockage, etc.

[0096] In some embodiments, the preset future time period may be a fixed future time period set by the processor by default or manually preset. For example, the next 6 hours after the current moment. In some embodiments, the preset future time period may also refer to the time period from the current moment to the time when the pipeline fault is eliminated.

[0097] Monitoring frequency refers to the frequency at which pipeline monitoring equipment monitors the pipeline to obtain pipeline sensing data, for example, 30 times per minute.

[0098] In some embodiments, after determining the high-risk pipeline, the processor may increase the monitoring frequency of the pipeline monitoring device of the high-risk pipeline in a preset future period based on a preset increase amount. The preset increase amount may be a fixed increase amount set by default by the processor or manually preset. For example, 5 times / minute. The preset increase amount may also be a dynamic increase amount determined by the processor based on the average value of the pipeline penetration of the high-risk pipeline. For example, the preset increase amount may be positively correlated with the average value of the pipeline penetration of the high-risk pipeline.

[0099] In some embodiments of the present specification, by judging whether the pipeline penetration degree of the re-inspected pipeline is greater than a preset degree threshold, it can be effectively determined whether the re-inspected pipeline is a high-risk pipeline, and then by increasing the monitoring frequency of the pipeline monitoring equipment of the high-risk pipeline, it can be further ensured that pipeline faults in the pipeline can be discovered in time.

[0100] In some embodiments of the present specification, sampling parameters can be reasonably determined through pipeline sensing data, so that the sampling equipment can accurately sample to obtain valid sampling data, and analyze the sampling data to determine fault detection instructions, which is conducive to accurately assessing the fault conditions of pipeline operation and providing directions for subsequent troubleshooting, thereby ensuring the effective maintenance of the safety and stability of the pipeline.

[0101] It should be noted that the above description of the process 200 is only for illustration and description, and does not limit the scope of application of the present invention. For those skilled in the art, various modifications and changes can be made to the process 200 under the guidance of the present invention. However, these modifications and changes are still within the scope of the present invention.

[0102] Figure 3 It is an exemplary schematic diagram of determining sampling parameters according to some embodiments of this specification.

[0103] In some embodiments, Figure 3 As shown, the processor can determine at least one section of abnormal pipeline 320 and its corresponding abnormal area 330 based on pipeline sensing data 310 of at least one section of pipeline; and determine sampling parameters 340 based on at least one section of abnormal pipeline 320 and its corresponding abnormal area 330.

[0104] The abnormal pipeline refers to the pipeline whose corresponding pipeline senses data abnormality. The abnormal area refers to the area on the pipeline where the abnormality exists.

[0105] In some embodiments, the processor can calculate the second difference between each data item in the pipeline perception data and each data item in the standard pipeline perception data of the pipeline. If there is a data item whose second difference is greater than the second difference threshold, the data item is determined to be an abnormal data item of the pipeline, and the pipeline is determined to be an abnormal pipeline.

[0106] In some embodiments, the second difference may include a pipeline temperature difference, a pipeline gas pressure difference, and a pipeline gas flow rate difference, and the second difference threshold may include a pipeline temperature difference threshold, a pipeline gas pressure difference threshold, and a pipeline gas flow rate difference threshold.

[0107] The pipeline temperature difference refers to the absolute value of the difference between the pipeline temperature in the pipeline sensing data and the standard pipeline temperature in the standard pipeline sensing data. The pipeline pressure difference and pipeline gas velocity difference are the same.

[0108] The pipeline temperature difference threshold refers to the maximum value of the pipeline temperature difference under normal circumstances. The pipeline pressure difference threshold and pipeline gas velocity difference threshold are the same.

[0109] Standard pipeline sensing data refers to pipeline sensing data under normal circumstances. For example, standard pipeline sensing data may include standard pipeline temperature, standard pipeline air pressure, standard pipeline gas flow rate, etc. In some embodiments, standard pipeline sensing data may be set by system default or determined by a technician based on historical data. For example, the historical pipeline sensing data with the highest frequency of occurrence in the historical pipeline sensing data of the same pipeline in the historical data may be determined as standard pipeline sensing data.

[0110] In some embodiments, the processor may determine the abnormal area based on the abnormal pipe by querying the second preset relationship table. The second preset relationship table includes abnormal pipes and corresponding abnormal areas. The second preset relationship table may be constructed based on historical data. For example, for a historical abnormal pipe in the historical data, the processor may count one or more corresponding historical abnormal areas, determine the historical abnormal area with the highest number of occurrences as the historical abnormal area corresponding to the historical abnormal pipe, traverse multiple historical abnormal pipes, and construct the second preset relationship table.

[0111] In some embodiments, the processor may determine the sampling parameters in a variety of ways based on at least one abnormal pipeline and its corresponding abnormal area. For example, the processor may determine the center position of the abnormal area as the pipeline sampling position, and use the sampling device within the preset range corresponding to the pipeline sampling position as the open sampling device. The preset range may refer to a range that does not exceed a preset distance threshold from the pipeline sampling position.

[0112] In some embodiments, the sampling parameters may also include a sampling detection type.

[0113] The sampling detection type refers to the type of the way in which the sampling device performs sampling. In some embodiments, the sampling detection type may include sampling detection and non-sampling detection.

[0114] Sampling detection refers to a detection method that requires extracting gas from the pipeline for sample detection. Non-sampling detection refers to a detection method that does not require extracting gas from the pipeline for direct detection.

[0115] In some embodiments, for a section of abnormal pipeline and its corresponding abnormal area, the processor can determine the sampling detection type based on the pipeline sensing data of the abnormal pipeline and its corresponding abnormal area; in response to the presence of multiple sampling devices on the abnormal pipeline, evaluate the sampling detection capability of the sampling device based on the pipeline sensing data and sampling detection type of the abnormal pipeline; and determine to turn on the sampling device based on the sampling detection capability of at least one sampling device.

[0116] In some embodiments, the processor may determine the sampling detection type in a variety of ways based on the pipeline sensing data of the abnormal pipeline and its corresponding abnormal area. For example, the processor may determine the sampling detection type by querying the third preset relationship table.

[0117] The third preset relationship table includes the abnormal area and abnormal data items of the abnormal pipeline and the corresponding sampling detection type. The third preset relationship table can be constructed based on historical data. For example, for an abnormal area and abnormal data item of a historical abnormal pipeline in the historical data, the processor can count one or more historical sampling detection types actually used subsequently, determine the historical sampling detection type with the highest number of occurrences as the historical sampling detection type corresponding to the abnormal area and abnormal data item of the historical abnormal pipeline, traverse multiple historical abnormal pipelines, and construct the third preset relationship table.

[0118] The sampling detection capability refers to the ability of the sampling data obtained by the sampling device to accurately reflect the failure problem of the pipeline. In some embodiments, the sampling detection capability can be represented by a numerical value. The higher the numerical value, the greater the sampling detection capability.

[0119] In some embodiments, the processor may evaluate the sampling detection capability of the sampling device in a variety of ways based on the pipeline sensing data and the sampling detection type of the abnormal pipeline. For example, the processor may determine the sampling detection capability of the sampling device by vector matching.

[0120] As an example only, the processor may construct a first standard vector library. The first standard vector library may include multiple first standard vectors and multiple first tags corresponding to them. The first standard vector may include pipeline sensing data and sampling detection type, and the first tag may be the sampling detection capability of the corresponding sampling device.

[0121] In some embodiments, the processor can construct a first clustering vector based on historical pipeline perception data, historical sampling detection types and their corresponding historical sampling detection capabilities in the historical data, cluster multiple first clustering vectors, and construct a first standard vector based on the historical pipeline perception data and historical sampling detection types corresponding to the cluster centers formed by clustering, and use the historical sampling detection capabilities corresponding to the cluster centers as the first label of the first standard vector.

[0122] In some embodiments, the processor may calculate the fault similarity between the historical fault detection instructions corresponding to the historical sampling data and the historical pipeline fault information actually monitored in history, and determine the fault similarity as the historical sampling detection capability.

[0123] Fault similarity can refer to the similarity between historical fault detection instructions and historical pipeline fault information actually monitored in the past. Fault similarity can be expressed by cosine similarity, Euclidean distance, etc.

[0124] In some embodiments, the processor may construct a first vector to be matched based on the pipeline sensing data and sampling detection type of the current abnormal pipeline, match the first vector to be matched with the first standard vector in the first standard vector library, calculate multiple vector similarities between the first vector to be matched and multiple first standard vectors, and determine the first label corresponding to the first standard vector with the highest vector similarity as the sampling detection capability of the current sampling device. The vector similarity may be represented by cosine similarity, Euclidean distance, etc.

[0125] In some embodiments, the processor can determine the sampling detection capability of the sampling device based on the pipeline sensing data and sampling detection type of the abnormal pipeline through the detection capability evaluation model. For more information about this part, please refer to Figure 4 The corresponding description.

[0126] In some embodiments, the processor may determine to enable the sampling device based on the sampling detection capability in a variety of ways. For example, the processor may select one or more sampling devices with the greatest sampling detection capability as the enabled sampling device.

[0127] In some embodiments of the present specification, determining a suitable sampling detection type for abnormal pipeline sensing data can improve the sampling efficiency. By evaluating the sampling detection capability of the sampling equipment, sampling equipment with better sampling effect can be effectively selected, which is beneficial to reducing resource consumption during the sampling process and can also reduce the impact of the sampling equipment on the pipeline.

[0128] In some embodiments of the present specification, abnormal pipelines and their corresponding abnormal areas are determined through pipeline sensing data, and then sampling parameters are determined, which can make sampling more targeted, further reduce the number of sampling devices opened, and reduce resource consumption.

[0129] Figure 4 It is an exemplary schematic diagram of the detection capability evaluation model shown in some embodiments of this specification.

[0130] In some embodiments, Figure 4 As shown, the processor may determine the sampling detection capability 430 of the sampling device through the detection capability evaluation model 420 based on the pipeline sensing data 310 and the sampling detection type 410 of the abnormal pipeline.

[0131] For more information about abnormal pipelines, pipeline sensing data, sampling detection types, sampling detection capabilities, and sampling equipment, see Figures 1 to 3 Related description.

[0132] The detection capability evaluation model refers to a model used to determine the detection capability of a sample. In some embodiments, the detection capability evaluation model may be a machine learning model, such as a neural network (NN).

[0133] In some embodiments, Figure 4 As shown, the input of the detection capability evaluation model 420 may include the pipeline sensing data 310 of the abnormal pipeline and the sampling detection type 410, and the output may be the sampling detection capability 430 of the sampling device.

[0134] In some embodiments, the detection capability evaluation model can be trained and acquired in a variety of ways. For example, it can be acquired through training of a plurality of first training samples with first training labels. A set of first training samples trained may include sample pipeline perception data of sample abnormal pipelines and sample sampling detection types. A first training label corresponding to a set of first training samples is the actual sampling detection capability of the sample sampling device.

[0135] In some embodiments, the processor may select the historical pipeline perception data and historical sampling detection type of a historical abnormal pipeline as a set of first training samples, and the historical sampling detection capability of the historical sampling device corresponding to the set of first training samples as the first training label of the set of first training samples. For the acquisition process of the first training label, please refer to Figure 3 The relevant description of the process of obtaining the first label of the first standard vector library in is not repeated here.

[0136] In some embodiments, the processor may input the sample pipeline perception data and sample sampling detection type of the sample abnormal pipeline into the initial detection capability assessment model, construct a first loss function based on the sampling detection capability of the sampling device output by the initial detection capability assessment model and the first training label, and update the initial detection capability assessment model based on the first loss function. When the first preset condition is met, the initial detection capability assessment model training is completed to obtain a trained detection capability assessment model. The first preset condition may be that the first loss function converges, the number of iterations reaches a threshold, etc.

[0137] In some embodiments, Figure 4 As shown, the input of the detection capability evaluation model 420 also includes a pipeline in-degree 440 , a pipeline out-degree 450 , and the number of upstream and downstream users 460 of the pipeline.

[0138] For more information about pipeline in-degree, see Figure 2 The corresponding description.

[0139] The pipeline out-degree of the current pipeline refers to the number of other pipelines into which the gas flows after flowing out of the current pipeline.

[0140] The number of users upstream and downstream of the pipeline refers to the number of users connected upstream of the pipeline and downstream of the pipeline. Users refer to gas users. In some embodiments, the number of users upstream and downstream of the pipeline can be composed of the number of users upstream of the pipeline and the number of users downstream of the pipeline.

[0141] In some embodiments of the present specification, the pipeline in-degree, pipeline out-degree and the number of users upstream and downstream of the pipeline are used as inputs of the detection capability evaluation model, which can effectively take into account the impact of the surrounding pipeline environment of the pipeline on the measurement, making the evaluation of the sampling detection capability more accurate.

[0142] In some embodiments, the detection capability assessment model may include a first stage of training, and the first stage of training may include training based on a first training set, verification based on a first validation set, and testing based on a first test set; the first training set, the first validation set, and the first test set are data sets composed of historical pipeline perception data, historical sampling detection types, the number of historical upstream and downstream pipelines, and the number of historical upstream and downstream users of the pipeline; the data volume of the first training set, the first validation set, and the first test set constitute a first preset ratio, and there is no data overlap in the first training set, the first validation set, and the first test set, and the sample statistical difference of the first training set is greater than a preset difference threshold, and the preset difference threshold is related to the accident frequency of historical sampling accidents.

[0143] The first training set refers to the data set used to train the internal parameters of the model.

[0144] The first validation set refers to the data set used to verify the status and convergence of the model during the training process.

[0145] The first test set refers to the data set used to test the generalization ability of the model.

[0146] In some embodiments, a historical pipeline perception data and a corresponding historical sampling detection type, a historical pipeline upstream and downstream pipeline number, and a historical pipeline upstream and downstream user number constitute a data group, and the first training set, the first verification set, and the first test set are all composed of multiple data groups.

[0147] The first preset ratio may be set by system default or by a technician based on experience. For example, the first preset ratio may be 8:1:1.

[0148] Data intersection refers to the existence of the same data in different sets, that is, the same data is used in multiple sets. In some embodiments, there is no data intersection among the first training set, the first validation set, and the first test set.

[0149] The sample statistical difference refers to the overall difference of the sample data. In some embodiments, the processor may digitally quantify each sample data in the first training set; calculate the vector distance between each pair of sample data in the first training set; calculate the variance of multiple vector distances; and determine the sample statistical difference based on the variance.

[0150] Among them, digital quantization can refer to corresponding one of the above data groups to a digital vector. Among them, the sampling detection in the historical sampling detection type can be assigned a value of 1 and the non-sampling detection can be assigned a value of 0. Other data are numerical data and can be used directly. The vector distance between two sample data can be represented by the cosine distance between two digital vectors. The larger the variance of multiple vector distances, the greater the sample statistical difference.

[0151] Sampling accidents refer to accidents caused by sampling. For example, poor quality of sampling data, quality problems of pipelines caused by sampling, etc. In some embodiments, the accident frequency of historical sampling accidents may refer to the frequency of historical samplings in which sampling accidents occurred in a large number of historical samplings. The greater the accident frequency of historical sampling accidents, the greater the preset difference threshold may be.

[0152] In some embodiments of the present specification, the model is trained in the first stage through a first training set, a first validation set, and a first test set, and a preset difference threshold corresponding to the sample statistical difference of the first training set is determined through the accident frequency of historical sampling accidents. This can make the sample data for model training more widely distributed. Citing sample statistical differences can make the model more robust and prevent the model from overfitting, which is conducive to the model accurately learning to predict the sampling detection capability.

[0153] In some embodiments of this specification, the sampling detection capability of a sampling device can be automatically and accurately determined through a machine learning model.

[0154] Figure 5 is another exemplary schematic diagram of determining sampling parameters according to some embodiments of this specification.

[0155] In some embodiments, the sampling parameters may also include a sampling time.

[0156] The sampling time refers to the time point when the sampling device starts sampling. The sampling time is a time point in the future of the current time point.

[0157] In some embodiments, Figure 5 As shown, the processor can determine pipeline perception data 510 of at least one section of the pipeline for a period of time in the future based on pipeline perception data 310 of at least one section of the pipeline; and determine sampling time 520 based on pipeline perception data 510 of at least one section of the pipeline for a period of time in the future.

[0158] The future period refers to a period of time after the current time point.

[0159] In some embodiments, the processor may determine the pipeline sensing data of at least one section of the pipeline for a period of time in the future based on the pipeline sensing data of at least one section of the pipeline in a variety of ways.

[0160] For example, the processor may calculate multiple first similarities between the pipeline perception data at the current time point and the historical pipeline perception data at multiple historical time points in the historical data, determine the historical time point corresponding to the historical pipeline perception data with the highest first similarity, and use the historical pipeline perception data of a subsequent period of time of the historical time point as the pipeline perception data of a future period of time at the current time point. The subsequent period of time of the historical time point has the same length as the future period of time of the current time point. The first similarity may be represented by a vector distance.

[0161] In some embodiments, the processor may determine the sampling time in a variety of ways based on the pipeline sensing data of at least one section of the pipeline for a period of time in the future. For example, the processor may select multiple future time points within a period of time in the future, and for each future time point, calculate the gradient between the pipeline sensing data of each pipeline at the future time point and the pipeline sensing data of the adjacent pipeline, and determine the gradient mean corresponding to all pipelines in the gas pipeline network at the future time point, one future time point corresponding to one gradient mean, and determine the future time point with the smallest gradient mean among the multiple gradient mean values ​​corresponding to the multiple future time points as the sampling time.

[0162] The method for selecting multiple future time points within a future period of time can refer to the method for determining candidate sampling time points below, which will not be described in detail here.

[0163] In some embodiments, the processor may determine at least one candidate sampling time point; determine the sampling stability of at least one candidate sampling time point based on pipeline sensing data of at least one section of the pipeline for a period of time in the future; and determine the sampling time based on the sampling stability of at least one candidate sampling time point.

[0164] In some embodiments, the processor can determine the candidate sampling time points in a variety of ways. For example, the processor can divide a future period of time into a preset length to obtain multiple sub-time periods, and use the start time point or end time point of each sub-time period as a candidate sampling time point. The preset length is negatively correlated with the number of candidate sampling time points. The larger the preset length, the fewer the number of candidate sampling time points. The number of candidate sampling time points can be set by the system default or by a technician according to actual needs.

[0165] Sampling stability refers to the stability of the gas network at the time of sampling. In some embodiments, sampling stability may be affected by the sampling behavior or the gas network itself. For example, at a certain point in time, multiple large chemical companies began to use gas, and the gas in the gas network fluctuated greatly, and the corresponding sampling stability may be poor. For another example, when the sampling equipment is sampling, a large amount of gas overflows due to improper operation, which in turn causes instability in the gas network.

[0166] In some embodiments, the processor may determine the sampling stability of at least one candidate sampling time point in a variety of ways based on pipeline sensing data of at least one pipeline section for a period of time in the future. For example, the processor may determine the sampling stability of at least one candidate sampling time point by vector matching.

[0167] As an example only, the processor may construct a second standard vector library. The second standard vector library may include multiple second standard vectors and multiple second tags corresponding thereto. The second standard vector may include pipeline sensing data and sampling time, and the second tag may be sampling stability of the corresponding sampling time.

[0168] In some embodiments, the processor can construct a second clustering vector based on historical pipeline perception data, historical sampling time and its corresponding historical sampling stability in the historical data, cluster multiple second clustering vectors, and construct a second standard vector based on the historical pipeline perception data and historical sampling time corresponding to the cluster center formed by clustering, and use the historical sampling stability corresponding to the cluster center as the second label of the second standard vector.

[0169] In some embodiments, the processor may calculate a second similarity between the pipeline sensing data before the historical sampling time and the pipeline sensing data after the historical sampling time, and determine the historical sampling stability based on the second similarity (e.g., subtracting the second similarity from 1 as the historical sampling stability). The second similarity may be represented by cosine similarity, Euclidean distance, etc.

[0170] In some embodiments, the processor may construct a second vector to be matched based on the current pipeline sensing data for a period of time in the future and the candidate sampling time point, match the second vector to be matched with the second standard vector in the second standard vector library, calculate multiple vector similarities between the second vector to be matched and multiple second standard vectors, and determine the second label corresponding to the second standard vector with the highest vector similarity as the sampling stability of the candidate sampling time point. The vector similarity may be represented by cosine similarity, Euclidean distance, etc.

[0171] In some embodiments, Figure 5As shown, the processor can construct a gas network map 540 ; based on the gas network map 540 , the sampling stability 530 of at least one candidate sampling time point is determined through a sampling stability evaluation model 550 .

[0172] The gas network graph refers to a graph that can reflect the connection relationship between each network and equipment in the gas network. In some embodiments, the gas network graph is composed of nodes and edges.

[0173] In some embodiments, the nodes of the gas network map may include gas pipeline nodes, gas supply equipment nodes, and gas consumption equipment nodes.

[0174] The gas pipeline node is the gas pipeline node; the gas supply equipment node is the gas supply equipment node; the gas consumption equipment node is the gas consumption equipment node. Gas supply equipment refers to equipment that provides gas, such as gas mixers, gas pressure regulating boxes, etc. Gas consumption equipment refers to terminal equipment that uses gas, such as residential gas consumption equipment, etc.

[0175] In some embodiments, the node attributes of a gas pipeline node may include the current pipeline sensing data of the pipeline and the pipeline sensing data for a period of time in the future; the node attributes of a gas supply equipment node may include a gas supply schedule; and the node attributes of a gas user equipment may include gas user equipment information and the current gas consumption rate.

[0176] The gas supply arrangement refers to the output parameters of the gas at each time point. For example, the output flow rate, output volume, output gas pressure, output gas concentration, output gas composition, etc. In some embodiments, the processor can obtain the gas supply arrangement from the gas company management platform. Gas equipment information refers to information related to the gas equipment. For example, the type of gas equipment, etc. The current gas consumption rate refers to the current rate of gas consumption, for example, 5m / s, etc.

[0177] In some embodiments, the node attribute of the gas pipeline node of the gas pipeline network map may further include the pipeline in-degree of the gas pipeline node.

[0178] In some embodiments of the present specification, the node attributes of the gas pipeline node also take into account the pipeline penetration, which can characterize the complexity of the gas pipeline network and improve the accuracy of subsequent determination of sampling stability.

[0179] In some embodiments, the edges of the gas network graph may be connecting edges of nodes where gas flows between each other. The edge attributes of the gas network graph may include a gas flow direction and a gas flow rate. The gas flow direction may refer to the direction from the node where gas flows out to the node where gas flows in.

[0180] The sampling stability evaluation model is a model for determining the sampling stability of a candidate sampling time point. In some embodiments, the sampling stability evaluation model may be a machine learning model, such as a graph neural network (GNN) model.

[0181] In some embodiments, the input of the sampling stability assessment model may include a gas network map and candidate sampling time points, and the output may be the sampling stability of the candidate sampling time points.

[0182] In some embodiments, the processor may train the sampling stability evaluation model based on a plurality of second training samples with second training labels. The training process may refer to Figure 4 The training process of the detection capability evaluation model will not be described here.

[0183] A group of second training samples may include a sample gas network map and sample candidate sampling time points, and a second training label corresponding to a group of second training samples may be the sampling stability of the sample candidate sampling time points corresponding to the training samples.

[0184] In some embodiments, the processor may select a historical gas network map and a historical sampling time as a set of second training samples, and use the historical sampling stability of the historical sampling time corresponding to the set of second training samples as the second training label of the set of second training samples. The process of obtaining the second training label can refer to the relevant description of the process of obtaining the second label of the second standard vector library in the above text, which will not be repeated here.

[0185] In some embodiments of the present specification, the sampling stability of candidate sampling time points can be accurately determined through a machine learning model, and then the appropriate sampling time can be determined to sample the gas pipeline, thereby improving work efficiency.

[0186] In some embodiments, the processor may determine the sampling time in a variety of ways based on the sampling stability of at least one candidate sampling time point. For example, the processor may sort the sampling stability of multiple candidate sampling time points and select one or more candidate sampling time points with the highest sampling stability as the sampling time.

[0187] Some embodiments of the present specification can more accurately measure the impact of different sampling times on the gas pipeline network by measuring the stability of candidate sampling time points, which is conducive to further reducing the risks and benefit losses caused by sampling.

[0188] In some embodiments of the present specification, pipeline sensing data for a period of time in the future is determined based on current pipeline sensing data, and then a sampling time is determined. This can make the determined sampling time more reasonable, reduce the impact of sampling on the gas pipeline network, and reduce the risks and economic benefits caused by sampling.

[0189] One or more embodiments of the present specification provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a smart gas pipeline sampling and monitoring method.

[0190] Certain features, structures or characteristics in one or more embodiments of the present invention may be appropriately combined.

[0191] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in the present invention, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of the present invention. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of the present invention. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0192] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the referenced materials of this invention and the contents of this invention, the descriptions, definitions, and / or usage of terms in this invention shall prevail.

Claims

1. A smart gas pipeline sampling and monitoring Internet of Things system, characterized in that: The smart gas pipeline sampling and monitoring IoT system includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, a gas equipment object platform, and a gas maintenance object platform; The gas equipment object platform includes at least one sampling device and at least one pipeline monitoring device; The gas maintenance object platform includes at least one staff interaction device; The government safety supervision object platform includes the gas company management platform and key gas-using enterprises; The government security supervision management platform is configured as follows: Obtaining pipeline sensing data of at least one section of the pipeline collected and uploaded by the gas equipment object platform via the gas company sensor network platform, the gas company management platform, and the government safety supervision sensor network platform in sequence; Determine sampling parameters based on the pipeline sensing data of the at least one section of the pipeline, send the sampling parameters to the government safety supervision object platform, and further send them to the at least one sampling device by the government safety supervision object platform; the sampling parameters include at least one open sampling device and a corresponding pipeline sampling position; In response to obtaining at least one sampling data from the at least one sampling device, a fault detection instruction is determined based on the at least one sampling data, and the fault detection instruction is sent to the gas maintenance object platform, and the gas maintenance object platform arranges a manual inspection.

2. The Internet of Things system according to claim 1, characterized in that: The government security supervision management platform is further configured as follows: Based on the pipeline sensing data of the at least one section of the pipeline, determining at least one section of abnormal pipeline and its corresponding abnormal area; The sampling parameters are determined based on the at least one abnormal pipe section and its corresponding abnormal area.

3. The Internet of Things system according to claim 2, characterized in that: The sampling parameters also include a sampling detection type, and the sampling detection type includes sampling detection and non-sampling detection; The government security supervision management platform is further configured as follows: For a section of abnormal pipeline and its corresponding abnormal area, Determining the sampling detection type based on the pipeline sensing data of the abnormal pipeline and the corresponding abnormal area; In response to the presence of a plurality of the sampling devices on the abnormal pipeline, based on the pipeline sensing data of the abnormal pipeline and the sampling detection type, evaluating the sampling detection capability of the sampling device; The starting of the sampling device is determined based on the sampling detection capability of the at least one sampling device.

4. The Internet of Things system according to claim 1, characterized in that: The sampling parameters also include sampling time, and the government security supervision management platform is further configured as follows: Determining pipeline sensing data of at least one section of the pipeline for a period of time in the future based on the pipeline sensing data of the at least one section of the pipeline; The sampling time is determined based on the pipeline sensing data of the at least one section of the pipeline for a period of time in the future.

5. The Internet of Things system according to claim 4, characterized in that: The government security supervision management platform is further configured as follows: determining at least one candidate sampling time point; Determining the sampling stability of the at least one candidate sampling time point based on the pipeline sensing data of the at least one section of the pipeline for a period of time in the future; The sampling time is determined based on the sampling stability of the at least one candidate sampling time point.

6. A smart gas pipeline sampling and monitoring method, characterized in that: The method is implemented based on a smart gas pipeline sampling and monitoring Internet of Things system, which includes a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, a gas equipment object platform, and a gas maintenance object platform; The method is performed by the government security supervision management platform, and the method includes: Obtaining pipeline sensing data of at least one section of the pipeline collected and uploaded by the gas equipment object platform via the gas company sensor network platform, the gas company management platform, and the government safety supervision sensor network platform in sequence; Determine sampling parameters based on the pipeline sensing data of the at least one section of the pipeline, send the sampling parameters to the government safety supervision object platform, and further send them to the at least one sampling device by the government safety supervision object platform; the sampling parameters include at least one open sampling device and a corresponding pipeline sampling position; In response to obtaining at least one sampling data from the at least one sampling device, a fault detection instruction is determined based on the at least one sampling data, and the fault detection instruction is sent to the gas maintenance object platform, and the gas maintenance object platform arranges a manual inspection.

7. The method according to claim 6, characterized in that The determining of sampling parameters based on the pipeline sensing data of the at least one section of the pipeline includes: Based on the pipeline sensing data of the at least one section of the pipeline, determining at least one section of abnormal pipeline and its corresponding abnormal area; The sampling parameters are determined based on the at least one abnormal pipe section and its corresponding abnormal area.

8. The method according to claim 7, characterized in that The sampling parameters also include a sampling detection type, and the sampling detection type includes sampling detection and non-sampling detection; The step of determining the sampling parameters based on the at least one abnormal pipeline section and its corresponding abnormal area includes: For a section of abnormal pipeline and its corresponding abnormal area, Determining the sampling detection type based on the pipeline sensing data of the abnormal pipeline and the corresponding abnormal area; In response to the presence of a plurality of the sampling devices on the abnormal pipeline, based on the pipeline sensing data of the abnormal pipeline and the sampling detection type, evaluating the sampling detection capability of the sampling device; The starting of the sampling device is determined based on the sampling detection capability of the at least one sampling device.

9. The method according to claim 6, characterized in that The sampling parameters also include sampling time, and the determining of the sampling parameters based on the pipeline sensing data of the at least one section of the pipeline further includes: Determining pipeline sensing data of at least one section of the pipeline for a period of time in the future based on the pipeline sensing data of the at least one section of the pipeline; The sampling time is determined based on the pipeline sensing data of the at least one section of the pipeline for a period of time in the future.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the smart gas pipeline sampling and monitoring method as described in any one of claims 6-9.

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