Pipe cleaning method, system and medium based on intelligent gas supervision Internet of Things

Abstract

The present invention provides a pipeline cleaning method, system and medium based on the intelligent gas supervision Internet of Things, relating to the technical field of the Internet of Things. The method includes: acquiring sewage discharge work data and gas monitoring data, determining pipeline cleaning parameters based on the sewage discharge work data and the gas monitoring data, generating a cleaning control instruction based on the pipeline cleaning parameters, and sending the cleaning control instruction to the intelligent gas equipment object platform to control the target sewage discharge equipment to perform cleaning with cleaning work parameters. The Internet of Things system includes an intelligent gas government safety supervision management platform, an intelligent gas government safety supervision sensor network platform, an intelligent gas government safety supervision object platform, an intelligent gas gas company sensor network platform, and an intelligent gas equipment object platform. The method can also be run after computer instructions stored in a computer-readable storage medium are read. The method can determine accurate target sewage discharge equipment and its corresponding cleaning work parameters.

Description

Technical Field

[0001] This specification relates to the technical field of the Internet of Things, and particularly to a pipeline cleaning method, system, and medium based on the intelligent gas supervision Internet of Things. Background Art

[0002] When the urban gas pipeline system transports gas to gas users, it filters the gas through sewage discharge equipment. The filtered impurities will accumulate in the sewage discharge pipeline of the sewage discharge equipment. After a period of time, too many impurities will accumulate in the sewage discharge pipeline, affecting the subsequent gas filtration effect and the quality of the downstream gas.

[0003] In order to realize the automatic cleaning of gas filtration devices, Chinese Patent Application No. CN111359333B proposes an automatic filter element cleaning and sewage discharge system applicable to gas. This system uses the differential pressure data collected by the electric control cabinet. When the pressure difference reaches a specified value, it starts the automatic cleaning operation and switches the connection of the pipeline from the gas outlet to the gas inlet to avoid affecting the quality of the downstream gas. However, this existing technology only sets the specified value based on experience to determine whether to conduct sewage discharge cleaning, and fails to systematically evaluate whether sewage discharge cleaning is required from various data, as well as the impact of unknown sewage discharge cleaning on downstream pipelines.

[0004] Therefore, it is necessary to provide a pipeline cleaning method, system, and medium based on the intelligent gas supervision Internet of Things to determine accurate target sewage discharge equipment and its corresponding cleaning working parameters. Summary of the Invention

[0005] To solve the problem of how to determine accurate target sewage discharge equipment and its corresponding cleaning working parameters, this specification provides a pipeline cleaning method, system, and medium based on the intelligent gas supervision Internet of Things.

[0006] The invention content includes a pipeline cleaning method based on the intelligent gas supervision Internet of Things. The method is characterized in that it is executed by the intelligent gas supervision Internet of Things system, which includes an intelligent gas government safety supervision management platform, an intelligent gas government safety supervision sensor network platform, an intelligent gas government safety supervision object platform, an intelligent gas company sensor network platform, and an intelligent gas equipment object platform; the intelligent gas government safety supervision object platform includes an intelligent gas company management platform, and the intelligent gas company management platform includes a gas database, which is configured to store devices; the intelligent gas government safety supervision management platform and the intelligent gas company management platform are configured as servers; the intelligent gas government safety supervision sensor network platform and the intelligent gas company sensor network platform are configured as communication networks, and the intelligent gas equipment object platform is configured as a sewage discharge device and a gas monitoring device. The sewage discharge device includes at least one of a filtering device and a sewage discharge pipeline, and the gas monitoring device is arranged on the gas pipeline; the method includes: the intelligent gas company management platform obtains the sewage discharge work data and gas monitoring data collected by the intelligent gas equipment object platform through the intelligent gas company sensor network platform, and stores the sewage discharge work data and gas monitoring data in the gas database; the intelligent gas company management platform determines pipeline cleaning parameters based on the sewage discharge work data and gas monitoring data, and uploads the pipeline cleaning parameters to the intelligent gas government safety supervision management platform. The pipeline cleaning parameters include the target sewage discharge device and its corresponding cleaning work parameters, and the cleaning work parameters include the opening level of the sewage discharge valve, the monitoring level, and the intelligent work parameters of the cleaning crawler robot; and the intelligent gas government safety supervision management platform generates a cleaning control instruction based on the pipeline cleaning parameters, and issues the cleaning control instruction to the intelligent gas equipment object platform to control the target sewage discharge device to perform cleaning with the cleaning work parameters.

[0007] The invention content includes an intelligent gas supervision Internet of Things system, characterized in that the Internet of Things system includes an intelligent gas government safety supervision management platform, an intelligent gas government safety supervision sensor network platform, an intelligent gas government safety supervision object platform, an intelligent gas gas company sensor network platform, and an intelligent gas equipment object platform; the intelligent gas government safety supervision object platform includes an intelligent gas gas company management platform, and the intelligent gas gas company management platform includes a gas database, and the gas database is configured to store equipment; the intelligent gas government safety supervision management platform and the intelligent gas gas company management platform are configured as servers; the intelligent gas government safety supervision sensor network platform and the intelligent gas gas company sensor network platform are configured as communication networks, and the intelligent gas equipment object platform is configured as a sewage discharge device and a gas monitoring device, and the sewage discharge device includes at least one of a filtering device and a sewage discharge pipe, and the gas monitoring device is arranged on the gas pipeline; the Internet of Things system is configured to: the intelligent gas gas company management platform obtains the sewage discharge work data and gas monitoring data collected by the intelligent gas equipment object platform through the intelligent gas gas company sensor network platform, and stores the sewage discharge work data and gas monitoring data into the gas database; the intelligent gas gas company management platform determines pipeline cleaning parameters based on the sewage discharge work data and gas monitoring data, and uploads the pipeline cleaning parameters to the intelligent gas government safety supervision management platform, and the pipeline cleaning parameters include the target sewage discharge device and its corresponding cleaning work parameters, and the cleaning work parameters include the sewage discharge valve opening level, the monitoring level, and the intelligent work parameters of the cleaning crawler robot; and the intelligent gas government safety supervision management platform generates a cleaning control instruction based on the pipeline cleaning parameters, and issues the cleaning control instruction to the intelligent gas equipment object platform to control the target sewage discharge device to perform cleaning according to the cleaning work parameters.

[0008] The invention content includes a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the above-mentioned pipeline cleaning method based on the intelligent gas supervision Internet of Things.

[0009] The beneficial effects brought by the above invention content include but are not limited to: through the collaborative work of the intelligent gas gas company management platform and the intelligent gas gas company sensor network platform, it is possible to effectively obtain and store the sewage discharge work data and gas monitoring data collected by the intelligent gas equipment object platform into the gas database. In addition, the intelligent gas gas company management platform can determine pipeline cleaning parameters based on the sewage discharge work data and gas monitoring data, and upload the pipeline cleaning parameters to the intelligent gas government safety supervision management platform. Furthermore, the intelligent gas government safety supervision management platform can use the pipeline cleaning parameters to generate a cleaning control instruction and issue it to the intelligent gas equipment object platform to precisely control the target sewage discharge device to operate according to the established cleaning work parameters, ensuring the timeliness, efficiency, safety, and compliance of the pipeline cleaning operation. Brief Description of the Drawings

[0010] This specification will further illustrate by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0011] Figure 1 is a platform structure diagram of the intelligent gas supervision Internet of Things system shown in some embodiments of this specification;

[0012] Figure 2 is an exemplary flowchart of a pipeline cleaning method based on the intelligent gas supervision Internet of Things shown in some embodiments of this specification;

[0013] Figure 3 is a schematic flowchart of determining the target sewage discharge equipment shown in some embodiments of this specification;

[0014] Figure 4 is a schematic flowchart of determining the target sewage discharge equipment shown in some other embodiments of this specification;

[0015] Figure 5 is an exemplary flowchart of determining the cleaning work parameters shown in some embodiments of this specification. Detailed Description of the Embodiments

[0016] The accompanying drawings to be used in the description of the embodiments will be briefly introduced below. The drawings do not represent all embodiments.

[0017] As used herein, "system", "device", "unit" and / or "module" are a way to distinguish 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.

[0018] Unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" 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.

[0019] When describing the operations performed step by step in the embodiments of this specification, unless otherwise specified, the order of the steps can be adjusted, the steps can be omitted, and other steps can also be included during the operation process.

[0020] Figure 1It is a platform structure diagram of the intelligent gas supervision Internet of Things system shown in some embodiments of this specification. The intelligent gas supervision Internet of Things system involved in the embodiments of this specification will be described in detail below. It should be noted that the following embodiments are only used to explain this specification and do not constitute a limitation to this specification.

[0021] In some embodiments, as Figure 1 shown, the intelligent gas supervision Internet of Things system 100 includes an intelligent gas government safety supervision management platform 110, an intelligent gas government safety supervision sensor network platform 120, an intelligent gas government safety supervision object platform 130, an intelligent gas company sensor network platform 150, and an intelligent gas equipment object platform 160.

[0022] The intelligent gas government safety supervision management platform 110 refers to a platform where government users coordinate and cooperate with the connections between various functional platforms, and gather all the information of the Internet of Things, providing perception management and control management functions for the Internet of Things operation system.

[0023] In some embodiments, the intelligent gas government safety supervision management platform 110 is configured as a server.

[0024] The server can be used to process data related to the intelligent gas supervision Internet of Things system 100. In some embodiments, the server is a single server or a server group. In some embodiments, the server group is centralized or distributed.

[0025] The intelligent gas government safety supervision sensor network platform 120 refers to a functional platform for managing sensor communication. In some embodiments, the intelligent gas government safety supervision sensor network platform 120 has the functions of sensing information sensor communication and control information sensor communication.

[0026] In some embodiments, the intelligent gas government safety supervision sensor network platform 120 is connected to the intelligent gas government safety supervision management platform 110 and the intelligent gas government safety supervision object platform 130 to realize information interaction among the intelligent gas government safety supervision sensor network platform 120, the intelligent gas government safety supervision management platform 110, and the intelligent gas government safety supervision object platform 130.

[0027] In some embodiments, the intelligent gas government safety supervision sensor network platform 120 is configured as a communication network.

[0028] In some embodiments, the communication network includes any suitable network that can facilitate the information and / or data exchange of the intelligent gas supervision Internet of Things system 100. In some embodiments, the intelligent gas government safety supervision management platform 110 obtains information and / or data from the intelligent gas government safety supervision object platform 130 through the communication network.

[0029] The Smart Gas Government Safety Supervision Object Platform 130 is a functional platform for generating sensing information and executing control information.

[0030] In some embodiments, the Smart Gas Government Safety Supervision Object Platform 130 includes the Smart Gas Gas Company Management Platform 140.

[0031] In some embodiments, the Smart Gas Gas Company Management Platform 140 includes a gas database.

[0032] In some embodiments, the gas database is a storage device.

[0033] The gas database refers to a database that stores information related to the Smart Gas Supervision IoT System 100. In some embodiments, the gas database is configured to store information such as sewage discharge work data and gas monitoring data. For more information on sewage discharge work data and gas monitoring data, please refer to Figure 2 and its related descriptions.

[0034] A storage device refers to a device that stores data information. Storage devices can include various types of memories, such as Random Access Memory (RAM), Read-Only Memory (ROM), etc.

[0035] The Smart Gas Gas Company Sensing Network Platform 150 refers to a functional platform for managing sensing communication. In some embodiments, the Smart Gas Gas Company Sensing Network Platform 150 realizes the functions of sensing communication for sensing information and sensing communication for control information.

[0036] In some embodiments, the Smart Gas Government Safety Supervision Object Platform 130 obtains gas-related data from the Smart Gas Device Object Platform 160 through the Smart Gas Gas Company Sensing Network Platform 150.

[0037] In some embodiments, the Smart Gas Device Object Platform is configured as various types of gas pipeline equipment and monitoring equipment. For example, the Smart Gas Device Object Platform is configured as sewage discharge equipment and gas monitoring equipment.

[0038] Sewage discharge equipment refers to equipment that filters and discharges impurities from the flowing gas. In some embodiments, the sewage discharge equipment includes filtering equipment, sewage discharge pipes, etc.

[0039] Filtering equipment is equipment used to filter gas. In some embodiments, the filtering equipment includes filter elements, such as metal filter meshes, fiber filters, etc.

[0040] A sewage discharge pipe refers to a pipe used to collect and discharge impurities generated during the filtering process.

[0041] In some embodiments, when the filtering device filters the fuel gas, the impurities filtered out will adhere to and accumulate in the sewage discharge pipe. The more impurities accumulate in the sewage discharge pipe, the easier it is to cause blockage of the sewage discharge pipe. At the same time, the worse the filtering effect of the filtering device, the more impurities carried by the fuel gas delivered to the downstream pipe. Therefore, it is necessary to clean the sewage discharge pipe.

[0042] The fuel gas monitoring device is a device used to monitor fuel gas data. For example, a pressure sensor, a temperature sensor, a flow sensor, a flow velocity sensor, a gas composition monitor, etc.

[0043] In some embodiments, the fuel gas monitoring device monitors the parameters of the fuel gas in the fuel gas pipeline. For example, the parameters of the fuel gas in the fuel gas pipeline include pressure data, temperature data, flow data, flow velocity sensor, gas composition data, etc.

[0044] In some embodiments, the fuel gas monitoring device is arranged on the fuel gas pipeline.

[0045] In some embodiments of this specification, based on the intelligent fuel gas supervision Internet of Things system 100, an information operation closed loop can be formed between the intelligent fuel gas government safety supervision management platform 110 and the intelligent fuel gas device object platform 160, and under the unified management of the intelligent fuel gas company management platform 140, it can operate coordinately and regularly, realizing the informatization and intelligentization of pipeline cleaning management based on the intelligent fuel gas supervision Internet of Things.

[0046] Figure 2 It is an exemplary flowchart of a pipeline cleaning method based on the intelligent fuel gas supervision Internet of Things shown in some embodiments of this specification. As Figure 2 shown, the process 200 includes the following steps. In some embodiments, the process 200 is executed by the intelligent fuel gas supervision Internet of Things system.

[0047] Step S210, the intelligent fuel gas company management platform obtains the sewage discharge work data and fuel gas monitoring data collected by the intelligent fuel gas device object platform through the intelligent fuel gas company sensing network platform, and stores the sewage discharge work data and fuel gas monitoring data in the fuel gas database.

[0048] In some embodiments, step S210 is executed based on the intelligent fuel gas company management platform.

[0049] The sewage discharge work data refers to the working parameters related to the operation of the sewage discharge equipment. For example, the sewage discharge work data includes but is not limited to the working duration, pressure drop, filtration accuracy, and working temperature of the sewage discharge equipment.

[0050] The working duration refers to the total duration since the last sewage discharge and cleaning of the sewage discharge equipment. The pressure drop refers to the pressure loss when the fluid (such as gas, etc.) flows through the sewage discharge equipment. The filtration accuracy refers to the size of the solid particles that the sewage discharge equipment can filter out from the fluid. The working temperature refers to the temperature of the sewage discharge equipment during operation. For the content about the sewage discharge equipment, reference can be made to Figure 1 and its related descriptions.

[0051] In some embodiments, the intelligent gas company management platform obtains the sewage discharge work data of the sewage discharge equipment based on the intelligent gas equipment object platform (such as sensors, monitoring equipment, etc.). For example, the intelligent gas company management platform obtains the working duration of the sewage discharge equipment through a timer, etc. For another example, the intelligent gas company management platform obtains the pressure drop of the sewage discharge equipment through a pressure sensor. For another example, the intelligent gas company management platform directly obtains the filtration accuracy of the sewage discharge equipment through the intelligent gas equipment object platform, and the filtration accuracy is regulated by the intelligent gas equipment object platform. For another example, the intelligent gas company management platform obtains the working temperature of the sewage discharge equipment through a temperature sensor.

[0052] Gas monitoring data refers to the gas-related data collected by gas monitoring equipment. For example, gas monitoring data includes but is not limited to pressure data, temperature data, flow data, flow velocity data, gas component data. Among them, the flow data refers to the flow within a unit time period (such as 0.5h, 1h, 2h, etc.).

[0053] For more content about gas monitoring equipment, reference can be made to Figure 1 and its related descriptions.

[0054] In some embodiments, the intelligent gas company management platform collects the sewage discharge work data of the sewage discharge equipment based on the gas monitoring equipment installed on the gas pipeline.

[0055] In some embodiments, the intelligent gas company management platform stores the sewage discharge work data and gas monitoring data in the gas database of the intelligent gas company management platform.

[0056] Step S220, the intelligent gas company management platform determines the pipeline cleaning parameters based on the sewage discharge work data and gas monitoring data, and uploads the pipeline cleaning parameters to the intelligent gas government safety supervision management platform.

[0057] In some embodiments, step S220 is executed based on the intelligent gas company management platform.

[0058] The pipeline cleaning parameters refer to the relevant data during the pipeline cleaning operation. For example, the pipeline cleaning parameters include but are not limited to the target sewage discharge equipment and its corresponding cleaning work parameters.

[0059] The target sewage disposal equipment refers to the sewage disposal equipment that needs to be cleaned. For example, the target sewage disposal equipment includes a filtration equipment that needs to be cleaned, a sewage pipe that needs to be cleaned, etc.

[0060] The cleaning operation parameters refer to the parameters used to characterize the cleaning operation. For example, the cleaning operation parameters include but are not limited to the opening level of the sewage valve, the monitoring level, and the intelligent operation parameters of the crawling robot.

[0061] The opening level of the sewage valve refers to a value, letter, etc. that can reflect the degree of opening of the sewage valve during the cleaning process. For example, the opening level of the sewage valve is represented by a value. The larger the value, the greater the opening level of the sewage valve and the greater the degree of opening of the sewage valve.

[0062] The monitoring level refers to a value, letter, etc. that can reflect the monitoring frequency and monitoring accuracy of the target sewage disposal equipment during the cleaning operation. For example, the monitoring level is represented by a value. The larger the value, the higher the monitoring level, the greater the monitoring frequency, and the higher the monitoring accuracy.

[0063] In some embodiments, the monitoring level is positively correlated with the monitoring frequency and the monitoring accuracy.

[0064] The monitoring frequency refers to the frequency of monitoring the target sewage disposal equipment during the cleaning operation. For example, the monitoring frequency is to monitor the target sewage disposal equipment 10 times per hour, etc., and there is no limitation here.

[0065] The monitoring accuracy refers to the amount of data collected in each acquisition interval. Only as an example, in response to the intelligent gas company management platform collecting data at 1-hour intervals and the monitoring accuracy being 5, it means that the intelligent gas company management platform continuously collects data 5 times per hour.

[0066] In some embodiments, different monitoring levels correspond to different monitoring frequencies and monitoring accuracies. For example, the higher the monitoring level, the greater the monitoring frequency and the higher the monitoring accuracy of the target sewage disposal equipment.

[0067] The cleaning crawling robot is an automated device equipped with cleaning tools.

[0068] In some embodiments, the cleaning crawling robot can select appropriate cleaning tools according to different pollution degrees. For example, a brush is suitable for mild pollution and can remove surface dust and slight attachments. A scraper is suitable for heavier dirt or hard deposits. A high-pressure water gun is used to spray water to dissolve and wash away impurities that are difficult to remove.

[0069] In some embodiments, the cleaning crawling robot is also capable of selectively carrying vacuum cleaners with different functions, sucking impurities into a dust collection bag or container through suction for easy later cleaning to meet the cleaning requirements of various sewage disposal devices. For example, when the cleaning crawling robot carries a magnetic vacuum cleaner, it uses a magnetic field to clean iron or magnetic substances in the sewage disposal device. Another example is that when the cleaning crawling robot selects to carry an ordinary vacuum cleaner, it generates a strong airflow to suck in dust, dirt, and other small particulate substances, and then sucks the dirt into a dust collection bag or a dust collection bucket through a suction nozzle or a brush.

[0070] Intelligent working parameters refer to the working parameters of the cleaning crawling robot during the cleaning operation. For example, the intelligent working parameters include, but are not limited to, the moving speed, cleaning intensity, and number of repeated cleanings of the cleaning crawling robot.

[0071] The moving speed refers to the traveling speed of the cleaning crawling robot during pipeline cleaning. For example, 1 m / s, etc., which is not limited here.

[0072] In some embodiments, the intelligent gas company management platform adjusts the moving speed based on the complexity and cleaning difficulty of the pipeline. Only as an example, the greater the complexity of the pipeline and the higher the cleaning difficulty, the slower the moving speed of the cleaning crawling robot.

[0073] The cleaning intensity refers to the force exerted by the cleaning tools or equipment of the cleaning crawling robot on the pollutants. For example, the cleaning intensity is represented by a numerical value, and the larger the numerical value, the higher the cleaning intensity.

[0074] In some embodiments, the greater the cleaning force of the brush in the cleaning tool, the greater the cleaning force of the scraper, the greater the intensity of the sprayed water flow, and the greater the flow rate of the sprayed water flow, the higher the cleaning intensity of the cleaning crawling robot.

[0075] In some embodiments, the intelligent gas company management platform adjusts the moving speed based on the intensity of the cleaning task. Only as an example, the higher the intensity of the cleaning task, the higher the cleaning intensity of the cleaning crawling robot.

[0076] The number of repeated cleanings refers to the number of times the cleaning crawling robot repeats the cleaning of the target sewage disposal device. For example, 5 times, etc., which is not limited here.

[0077] In some embodiments, the intelligent gas company management platform determines the target sewage pipeline in the pipeline cleaning parameters in various ways. For example, the intelligent gas company management platform determines the sewage disposal device with the working duration of the current sewage disposal device exceeding the duration threshold, the total amount of filtered gas within a unit time period being lower than the filtration threshold, and the filtration score being lower than the score threshold as the target sewage disposal device.

[0078] The duration threshold refers to the threshold used to determine whether the working duration of the sewage discharge device is too long. In some embodiments, the duration threshold is set by professional technicians or the system default.

[0079] The total amount of gas filtered within a unit time period refers to the total amount of gas that has passed through the sewage discharge device and been filtered within a unit time period (e.g., 1 min, 30 min, 60 min, etc.).

[0080] In some embodiments, the intelligent gas company management platform obtains the total amount of gas filtered by the sewage discharge device within a unit time period corresponding to the sewage discharge device based on the flow data in the gas monitoring data.

[0081] The filtration threshold refers to the threshold used to determine whether the total amount of gas filtered by the sewage discharge device within a unit time period is too small. In some embodiments, the filtration threshold is set by professional technicians or the system default.

[0082] The filtration score refers to a quantitative evaluation of the filtration effect of the sewage discharge device, which can reflect the current working state and performance of the sewage discharge device.

[0083] In some embodiments, the intelligent gas company management platform determines the filtration score based on the amount of impurities decreased before and after sewage discharge and the gas filtration flow through a first preset rule. The first preset rule includes a positive correlation between the filtration score and the amount of impurities decreased before and after sewage discharge and the gas filtration flow.

[0084] The score threshold refers to the threshold used to determine whether the filtration effect of the sewage discharge device reaches an acceptable level. In some embodiments, in response to the filtration score being lower than the score threshold, it may indicate that the sewage discharge device needs to be cleaned. In some embodiments, the score threshold is set by professional technicians or the system default.

[0085] In some embodiments, the intelligent gas company management platform determines the cleaning working parameters corresponding to the target sewage pipeline in the pipeline cleaning parameters based on various methods. For example, the intelligent gas company management platform determines the opening level of the sewage valve, the monitoring level, and the intelligent working parameters through a second preset rule based on the flow data of the gas. The second preset rule includes a positive correlation between the opening level of the sewage valve, the monitoring level, and the intelligent working parameters and the flow data of the gas.

[0086] For another example, the intelligent gas company management platform determines the opening level of the sewage valve, the monitoring level, and the intelligent working parameters according to the flow data of the gas and the pipeline importance score through a third preset rule. The third preset rule includes a positive correlation between the opening level of the sewage valve, the monitoring level, and the intelligent working parameters and the flow data of the gas and the pipeline importance score.

[0087] For the content regarding the pipeline importance score, reference can be made to Figure 3Step S330 and its related description.

[0088] For some other embodiments of determining pipeline cleaning parameters based on sewage discharge work data and gas monitoring data, reference can be made to Figure 3 , Figure 4 , Figure 5 and its related description.

[0089] In some embodiments, the intelligent gas company management platform uploads the pipeline cleaning parameters to the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensor network platform.

[0090] In step S230, the intelligent gas government safety supervision management platform generates a cleaning control instruction based on the pipeline cleaning parameters, and issues the cleaning control instruction to the intelligent gas equipment object platform to control the target sewage discharge equipment to perform cleaning with the cleaning work parameters.

[0091] In some embodiments, step S230 is executed based on the intelligent gas government safety supervision management platform.

[0092] The cleaning control instruction refers to an instruction for controlling the target sewage discharge equipment to perform cleaning with the pipeline cleaning parameters.

[0093] In some embodiments, the cleaning control instruction further includes an indirect control instruction, which is configured to increase the monitoring level of the monitoring equipment on the pipeline gas when cleaning the target sewage discharge equipment.

[0094] The affected pipeline refers to the pipeline related to the sewage discharge pipeline corresponding to the target sewage discharge equipment.

[0095] In some embodiments, the intelligent gas government safety supervision management platform uses the downstream sewage discharge pipeline whose distance from the target sewage discharge equipment is within the distance threshold range as the affected pipeline.

[0096] The downstream sewage discharge pipeline refers to the sewage discharge pipeline following the sewage discharge pipeline corresponding to the target sewage discharge equipment along the gas fluid direction. The gas fluid direction refers to the direction of gas flow.

[0097] The distance threshold refers to the threshold for determining whether it can be used as an affected pipeline. For example, the distance threshold is 100 meters, etc., and there is no limitation here.

[0098] In some embodiments, the intelligent gas government safety supervision management platform determines the distance threshold by querying a preset relationship table based on the flow rate of the sewage discharge pipeline corresponding to the target sewage discharge equipment.

[0099] In some embodiments, the preset relationship table includes the flow data of the sewage discharge pipeline corresponding to the target sewage discharge device and the true distance threshold. Only as an example, the distance threshold in the first preset relationship table is positively correlated with the flow rate of the sewage discharge pipeline corresponding to the target sewage discharge device.

[0100] In some embodiments of the present specification, in response to the switching of the sewage discharge pipeline during the cleaning process, it may cause situations such as gas fluctuations (such as changes in flow rate and pressure). Therefore, it is necessary not only to adjust the monitoring level of the target sewage discharge device, but also to increase the monitoring level of the gas monitoring devices on the affected pipelines, so as to ensure real-time monitoring of all affected downstream sewage discharge pipelines, improve the monitoring priority, and effectively prevent the occurrence of accidents.

[0101] In some embodiments, the intelligent gas government safety supervision and management platform generates a cleaning control instruction based on the pipeline cleaning parameters and sends the cleaning control instruction to the intelligent gas device object platform.

[0102] In some embodiments of the present specification, through the collaborative work of the intelligent gas company management platform and the intelligent gas company sensor network platform, it is possible to effectively obtain and store the sewage discharge work data and gas monitoring data collected by the intelligent gas device object platform into the gas database. In addition, the intelligent gas company management platform can determine the pipeline cleaning parameters based on the sewage discharge work data and gas monitoring data, and upload the pipeline cleaning parameters to the intelligent gas government safety supervision and management platform. Furthermore, the intelligent gas government safety supervision and management platform can generate a cleaning control instruction using the pipeline cleaning parameters and send it to the intelligent gas device object platform to precisely control the target sewage discharge device to operate according to the established cleaning work parameters, ensuring the timeliness, efficiency, safety, and compliance of the pipeline cleaning operation.

[0103] Figure 3 is a schematic flow chart of determining the target sewage discharge device shown in some embodiments of the present specification. As Figure 3 shown, process 300 includes the following steps. In some embodiments, process 300 is executed by the intelligent gas company management platform.

[0104] Step S310, construct a stacking feature map based on the sewage discharge work data and gas monitoring data.

[0105] The stacking feature map refers to a knowledge graph used to represent the distribution and attributes of sewage discharge pipelines and ordinary pipelines. In some embodiments, the stacking feature map is composed of at least one node and at least one edge, the edge connects the nodes, and the nodes and edges have attributes.

[0106] In some embodiments, the nodes of the stacking feature map include sewage discharge pipeline nodes and ordinary pipeline nodes.

[0107] In some embodiments, the sewage pipeline node corresponds to the sewage pipeline existing in the preset area.

[0108] In some embodiments, the attributes of the sewage pipeline node reflect the characteristics of the sewage node. For example, the attributes of the sewage pipeline node include at least one of, but are not limited to, the type of filtering device, sewage operation data, gas monitoring data, weather data, and pipeline physical characteristics. Among them, the preset area may include, but is not limited to, cities, districts, and streets.

[0109] The type of filtering device refers to the type of device installed on the sewage pipeline node for filtering impurities in the gas. For example, the type of filtering device includes, but is not limited to, bag filters and activated carbon filters.

[0110] In some embodiments, the sewage operation data includes current sewage data and historical sewage data.

[0111] The current sewage data refers to the working parameters recorded during the current operation of the sewage device. The historical sewage data refers to the working parameters corresponding to the sewage device within a historical preset time period.

[0112] For more content about the sewage operation data, reference can be made to Figure 2 and its related descriptions.

[0113] In some embodiments, the gas monitoring data includes current monitoring data and historical monitoring data.

[0114] The current monitoring data refers to the gas monitoring data collected during the current operation of the gas monitoring device. The historical monitoring data refers to the gas monitoring data collected by the gas monitoring device within a historical preset time period.

[0115] For more content about the gas monitoring data, reference can be made to Figure 2 and its related descriptions.

[0116] The weather data refers to the climate condition data related to the area where the node is located in the accumulation feature map. For example, the weather data includes, but is not limited to, temperature and humidity.

[0117] In some embodiments, the weather data includes historical weather data and current weather data.

[0118] In some embodiments, the intelligent gas company management platform obtains the weather data based on the intelligent gas device object platform (such as temperature sensors, humidity sensors, etc.).

[0119] The pipeline physical characteristics refer to the physical attributes corresponding to the pipeline. For example, the pipeline physical characteristics include physical characteristics such as the pipeline material data, pipeline shape and size, and pipeline length corresponding to the sewage pipeline.

[0120] In some embodiments, the ordinary pipeline node corresponds to an ordinary pipeline existing in a preset area.

[0121] In some embodiments, the attributes of the ordinary pipeline node reflect the characteristics of the ordinary node. For example, the attributes of the ordinary pipeline node include, but are not limited to, at least one of gas monitoring data, weather data, and pipeline physical characteristics.

[0122] Among them, the ordinary pipeline refers to a gas transmission pipeline without a filtering device installed.

[0123] In some embodiments, when there is a physical connection between the pipelines corresponding to two nodes, the connection line between the two nodes forms an edge. In some embodiments, the edge is a directed edge, and the attributes of the edge include the gas fluid direction.

[0124] In some embodiments, the intelligent gas company management platform constructs a stacking feature map based on the above nodes, edges, node attributes, and edge attributes.

[0125] Step S320: Based on the stacking feature map, determine the current stacking amount set through the stacking amount determination model.

[0126] The current stacking amount refers to a set composed of the pipelines corresponding to each node in the stacking feature map and the impurity stacking amount corresponding to the pipeline. For example, the current stacking amount set is represented by {(A1, B1), (A2, B2), …, (A i , B i ), …, (A n , B n ), …, (A i represents the pipeline corresponding to the node in the stacking feature map, and B i represents the impurity stacking amount of pipeline A i .

[0127] The current impurity stacking amount refers to the total weight of impurities formed by solid particles, sediments, etc. accumulated on the inner wall of the pipeline.

[0128] In some embodiments, the intelligent gas company management platform determines the current stacking amount set through the stacking amount determination model based on the stacking feature map.

[0129] The stacking amount determination model refers to a model used to determine the current stacking amount set. In some embodiments, the stacking amount determination model is a machine learning model. For example, the stacking amount determination model is a Graph Neural Network (GNN) model.

[0130] The input of the stacking amount determination model can be the stacking feature map, and the output can be the current stacking amount set.

[0131] In some embodiments, the stacking quantity determination model is trained and obtained based on a first sample training set. The first sample training set includes multiple sample stacking feature maps and their corresponding first labels. The first sample training set is input into the initial stacking quantity determination model, and a loss function is constructed through the first labels and the results of the initial determination model. The parameters of the initial determination model are iteratively updated based on the loss function. When the loss function of the initial stacking quantity determination model meets a preset condition, the model training is completed, and a trained stacking quantity determination model is obtained. Among them, the preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0132] In some embodiments, the first sample training set is a sample stacking feature map. The intelligent gas company management platform constructs a sample stacking feature map based on the first historical moment of the first sample training set and the historical sewage discharge work data and historical gas monitoring data before the first historical moment.

[0133] In some embodiments, the first label can be the current stacking quantity of the sample actually collected at the first historical moment corresponding to the first sample training set. For example, the intelligent gas company management platform is based on a cleaning crawler robot equipped with an ultrasonic sensor to detect the thickness of impurity accumulation. In response to the average thickness of the impurity accumulation being thicker, the impurity accumulation quantity is higher.

[0134] In some embodiments, the intelligent gas company management platform takes the total thickness of the impurity accumulation of all impurities as the impurity accumulation quantity. For another example, the intelligent gas company management platform cleans the impurities in the pipeline and obtains the impurity accumulation quantity by weighing.

[0135] In some embodiments, the intelligent gas company management platform corrects the stacking quantity determination model based on actual working parameters, or provides training samples for further strengthening the stacking quantity determination model. For example, the intelligent gas company management platform determines the actual impurity accumulation quantity based on the actual impurity accumulation thickness. In response to the difference between the actual impurity accumulation quantity and the impurity accumulation quantity output by the stacking quantity determination model, it indicates that there is a deviation in the stacking quantity determination model and may need to be corrected, such as adjusting the parameters of the stacking quantity determination model. In addition, the actual working parameters can provide training samples for optimizing the stacking quantity determination model.

[0136] For the content of the actual working parameters, reference can be made to Figure 5 and its related descriptions.

[0137] Step S330: Determine the target sewage discharge device based on the current stacking quantity set and the cleaning threshold.

[0138] The cleaning threshold refers to the threshold used to determine whether the current stacking quantity combination is too high.

[0139] In some embodiments, the cleaning threshold is set by professional technicians or by system default. In some embodiments, the cleaning threshold is also determined based on the physical characteristics of the pipeline, historical monitoring data, and the pipeline importance score. For more information on the physical characteristics of the pipeline and historical monitoring data, reference can be made to Figure 3 step S310 and its related description in

[0140] The pipeline importance score refers to a numerical value used to characterize the importance of the pipeline. In some embodiments, the higher the numerical value, the higher the pipeline importance score.

[0141] In some embodiments, the intelligent gas company management platform determines the pipeline importance score through the following formula (1) based on the positive correlation between the pipeline importance score and the gas flow data, the number of associated pipelines, and the gas flow data of the associated pipelines.

[0142] (1)

[0143] Where, is the pipeline importance score, is the gas flow data, is the number of associated pipelines, is the gas flow data of the associated pipelines. , , are the weight coefficients of the gas flow data, the number of associated pipelines, and the gas flow data of the associated pipelines respectively, and can be preset and obtained.

[0144] For more information on the flow data, reference can be made to Figure 2 and its related description.

[0145] An associated pipeline refers to a pipeline whose impurity accumulation is affected by the upstream sewage discharge pipeline.

[0146] In some embodiments, the intelligent gas company management platform determines the associated pipelines based on multiple methods. For example, the intelligent gas company management platform determines the associated pipelines based on historical data. Only as an example, when the intelligent gas formula management platform responds to the impurity in the gas component data in the downstream pipeline decreasing after the sewage treatment equipment is cleaned, then this pipeline is the associated pipeline of this sewage treatment equipment.

[0147] In some embodiments, for a pipeline, the intelligent gas company management platform adjusts the features (such as sewage discharge work data) of the nodes of the sewage discharge pipeline corresponding to the pipeline in the accumulation feature map; then, based on the adjusted accumulation feature map, through the accumulation amount determination model, the adjusted impurity accumulation amount set is determined. Since the accumulation amount of impurities in the first associated pipeline is affected by the upstream sewage discharge pipeline, by comparing the impurity accumulation amount set obtained before adjustment (i.e., obtained using real data) with the adjusted impurity accumulation amount set, the pipelines in the downstream of the sewage discharge pipeline where the impurity accumulation amount sets obtained before and after adjustment are different are determined as the first associated pipelines of the sewage discharge pipeline corresponding to the pipeline.

[0148] The number of associated pipelines refers to the sum of the numbers of all downstream associated pipelines related to the sewage discharge pipeline.

[0149] In some embodiments, the intelligent gas company management platform determines the cleaning threshold through the following formula (2) based on the positive correlation between the cleaning threshold and the pipeline length and filtration score, and the negative correlation between the cleaning threshold and the pipeline inner diameter size and pipeline importance score.

[0150] (2)

[0151] Wherein, is the cleaning threshold, is the pipeline length, is the pipeline inner diameter size, is the pipeline importance score, is the filtration score. 、 、 、 are the weight coefficients of the pipeline length, pipeline inner diameter size, pipeline importance score, and filtration score respectively. In some embodiments, 、 are positive numbers, 、 are negative numbers and can be preset to obtain.

[0152] In some embodiments of this specification, even if the impurity accumulation amount is the same, the degree of influence on the pipeline may be different. Therefore, based on the pipeline physical characteristics, historical monitoring data, and pipeline importance score, the situation of the pipeline can be evaluated more comprehensively, and by setting more adaptable cleaning thresholds for different sewage discharge pipelines, the sewage discharge pipelines can be cleaned more adaptably.

[0153] In some embodiments, in response to the impurity accumulation amount of the sewage discharge pipeline being greater than the cleaning threshold, the intelligent gas company management platform determines the sewage discharge pipeline as the target sewage discharge device.

[0154] In some embodiments of this specification, based on the sewage discharge work data and gas monitoring data, a stacking feature map is constructed. Then, based on the stacking feature map, the current stacking amount set is determined through the stacking amount determination model. Constructing the stacking feature map can provide rich information, enhance the reasoning ability and accuracy of the stacking amount determination model, and help the stacking amount determination model better understand and process complex relationships. Determining the current stacking amount set can accurately determine the pipeline and its corresponding impurity stacking amount, which helps the system to timely discover and handle problems. Based on the current stacking amount set and the cleaning threshold, the target sewage discharge equipment can be determined more effectively, improving the cleaning efficiency.

[0155] In some embodiments, the current stacking amount set includes the sewage discharge pipeline corresponding to at least one sewage discharge pipeline node and its current impurity stacking amount. For the sewage discharge pipeline corresponding to a sewage discharge pipeline node and its current impurity stacking amount, in response to the current impurity stacking amount being less than the cleaning threshold and greater than the attention threshold, the intelligent gas company management platform determines the sewage discharge pipeline corresponding to the current impurity stacking amount as the attention sewage discharge pipeline, generates a monitoring enhancement instruction, and sends the monitoring enhancement instruction to the intelligent gas equipment object platform to increase the monitoring level of the gas monitoring equipment for the attention sewage discharge pipeline.

[0156] The attention threshold refers to the threshold used to determine whether a pipeline needs to be strengthened in monitoring and attention.

[0157] In some embodiments, the intelligent gas company management platform determines the attention threshold based on the flow data of the gas through the fourth preset rule. The fourth preset rule includes that the attention threshold has a negative correlation with the flow data of the gas.

[0158] The attention sewage discharge pipeline refers to a sewage discharge pipeline whose current impurity stacking amount has not reached the cleaning threshold but has exceeded the attention threshold.

[0159] The monitoring enhancement instruction refers to an instruction used to increase the monitoring level of the attention sewage discharge pipeline.

[0160] In some embodiments, the above-mentioned increased attention amplitude is obtained by professional technicians or system presetting.

[0161] In some embodiments, the intelligent gas company management platform determines the above-mentioned increased attention amplitude based on the impurity stacking amount of the current attention sewage discharge pipeline through the fifth preset rule. The fifth preset rule includes that the increased attention amplitude is positively correlated with the impurity stacking amount of the current attention sewage discharge pipeline.

[0162] In some embodiments of this specification, by increasing the monitoring level, it is possible to detect in a timely manner before the current impurity accumulation amount approaches the cleaning threshold, so as to clean in a timely manner and reduce the risk of failures caused by impurity accumulation. In addition, increasing the monitoring level for the sewage discharge pipes of concern can avoid the resources consumed by high-frequency monitoring of all pipes while strengthening the monitoring, achieving an effective allocation of resources.

[0163] In some embodiments, after determining the target sewage discharge device based on steps S310 - S330, the sewage discharge devices that are not determined as the target sewage discharge device are determined as the first sewage discharge devices. In response to the number of the first sewage discharge devices being at least one, for one first sewage discharge device, the intelligent gas company management platform, based on the gas monitoring data, obtains the first associated pipe of the sewage discharge pipe corresponding to the first sewage discharge device; and determines the target sewage discharge device based on the current accumulation amount set, the first associated pipe, and the association threshold.

[0164] The first sewage discharge device refers to the sewage discharge device that is not determined as the target sewage discharge device.

[0165] The first associated pipe refers to the associated pipe of the sewage discharge pipe corresponding to the first sewage discharge device.

[0166] For more content about the associated pipe, reference can be made to Figure 3 Step S330 and its related descriptions.

[0167] In some embodiments, the obtaining method of the first associated pipe is similar to the obtaining method of the associated pipe. Please refer to Figure 3 Step S330 and its related descriptions, which will not be elaborated here.

[0168] The association threshold refers to the threshold used to determine whether the impurity accumulation amount in the associated pipe is too high.

[0169] In some embodiments, different first associated pipes correspond to different association thresholds. Since the physical characteristics, usage conditions, and importance of different first associated pipes may be different, by setting different association thresholds for different first associated pipes, it is possible to more accurately reflect the characteristics of each first associated pipe, and thus contribute to improving the accuracy of determining the first associated pipe.

[0170] In some embodiments, the association threshold is negatively correlated with the inner diameter size of the first associated pipe and positively correlated with the pipe importance score of the first associated pipe.

[0171] In some embodiments, the intelligent gas company management platform determines the current impurity accumulation amount of the first associated pipe based on the current accumulation amount set, and determines the number of the current impurity accumulation amounts of the first associated pipes that exceed the association threshold. The sewage discharge devices with the above number exceeding the number threshold are determined as the target sewage discharge devices.

[0172] The quantity threshold refers to the threshold for determining whether there are too many first associated pipelines corresponding to the sewage discharge equipment. In some embodiments, the quantity threshold is set by professional technicians or by default in the system.

[0173] In some embodiments of this specification, by obtaining the first associated pipelines of the sewage pipelines corresponding to each first sewage discharge equipment and determining the target sewage discharge equipment according to the current accumulation amount set, the first associated pipelines, and the association threshold, on the basis of determining the target sewage discharge equipment in steps S310 - S330, since some impurities may be carried when inputting gas into the downstream pipelines, further considering the downstream first associated pipelines helps reduce the risk of failures caused by impurity accumulation in the downstream pipelines, thereby improving the accuracy of determining the target sewage discharge equipment and further improving the sewage discharge efficiency.

[0174] Figure 4 It is a schematic flowchart of determining the target sewage discharge equipment shown in some other embodiments of this specification. As Figure 4 shown, process 400 includes the following steps. In some embodiments, process 400 is executed by the intelligent gas company management platform.

[0175] Step S410, construct an estimated feature map based on the sewage discharge work data and the gas monitoring data.

[0176] The definition and structure of the estimated feature map are similar to those of the accumulation feature map, and will not be elaborated here.

[0177] In some embodiments, the nodes of the estimated feature map include sewage pipeline nodes and ordinary pipeline nodes.

[0178] The definition of the sewage pipeline node can be referred to Figure 3 and its related descriptions. In some embodiments, the attributes of the sewage pipeline node include but are not limited to the filtration equipment type, sewage discharge work data, future sewage discharge data, current accumulation amount set, historical accumulation amount sequence, future maintenance data, gas monitoring data, weather data, pipeline physical characteristics, and pipeline aging degree.

[0179] More content about the filtration equipment type, sewage discharge work data, current accumulation amount set, and gas monitoring data can be referred to Figure 3 and its related descriptions, and will not be elaborated here.

[0180] The future sewage discharge data refers to the data of the sewage discharge equipment working within a preset future time period.

[0181] In some embodiments, the future sewage discharge data is obtained by means such as being read by the background or manually input.

[0182] In some embodiments, future sewage discharge data can also be set by professional technicians or the system default.

[0183] The historical accumulation amount sequence refers to the sequence composed of the pipelines corresponding to each node at each historical time point within a historical preset time period, and the impurity accumulation amount corresponding to the pipeline. For example, the historical accumulation amount sequence is represented by {[((C1, D 11 ), (C2, D 12 ), …… (C i ), D 1i ), …… (C n ), D 1n ), [((C1, D 21 ), (C2, D 22 ), …… (C i ), D 2i ), …… (C n ), D 2n ), …… [((C1, D j1 ), (C2, D j2 ), …… (C i ), D ji ), …… (C n ), D jn ), …… [((C1, D m1 ), (C2, D m2 ), …… (C i ), D mi ), …… (C n ), D mn )]}. Among them, [((C1, D j1 ), (C2, D j2 ), …… (C i ), D ji ), …… (C n ), D jn )] represents the pipelines corresponding to each node at the j-th historical time point and the impurity accumulation amount corresponding to the pipeline. C i represents the pipeline corresponding to the i-th node, and D ji represents the impurity accumulation amount of pipeline C i at the j-th historical time point. The number of nodes is n, and the number of historical time points is m.

[0184] In some embodiments, the intelligent gas company management platform obtains the historical accumulation amount sequence based on the pipelines corresponding to each node and their impurity accumulation amounts at each historical time point within a historical preset time period in the historical data.

[0185] Future maintenance data refers to the data related to the maintenance of the pipelines corresponding to the nodes. For example, future maintenance data includes, but is not limited to, future regular maintenance plans (such as regular repair, replacement of pipeline components and their replacement times).

[0186] In some embodiments, future maintenance data is set by professional technicians according to the current situation of the pipeline.

[0187] The definition of weather data is similar to that in Figure 3 However, different from that, weather data may also include temperature, humidity, etc. of future weather.

[0188] In some embodiments, the intelligent gas company management platform obtains weather data based on weather APIs (such as Open WeatherMap and other APIs).

[0189] The degree of pipeline aging refers to letters, numerical values, etc. used to characterize the aging condition of the pipeline corresponding to the node.

[0190] In some embodiments, the degree of pipeline aging may affect the accumulation rate of pipeline impurities. For example, the higher the degree of pipeline aging, the easier it is to generate impurities.

[0191] In some embodiments, the intelligent gas company management platform determines the degree of pipeline aging based on the actual use of the pipeline through a sixth preset rule. The sixth preset rule includes that the degree of pipeline aging is positively correlated with the use time of the pipeline.

[0192] In some embodiments, the intelligent gas company management platform determines the degree of pipeline aging based on the historical impurity accumulation amount of the gas pipeline, pipeline physical characteristics, historical weather data, and historical gas composition data.

[0193] Historical gas composition data refers to the gas composition in the pipeline within a historical preset time period.

[0194] In some embodiments, the intelligent gas company management platform obtains historical gas composition data based on historical data.

[0195] In some embodiments, the intelligent gas company management platform constructs a first feature vector based on the historical impurity accumulation amount of the gas pipeline, pipeline physical characteristics, historical weather data, and historical gas composition data, and then retrieves in the first vector database based on the first feature vector to determine the degree of pipeline aging.

[0196] Among them, the first vector database is a database used to determine the degree of pipeline aging. In some embodiments, the first vector database contains multiple first reference vectors and the corresponding reference pipeline aging degree for each first reference vector.

[0197] In some embodiments, the intelligent gas company management platform constructs a first reference vector based on the historical impurity accumulation amount, historical pipeline physical characteristics, historical weather data, and historical gas composition data corresponding to the first reference data, and uses the historical actual pipeline aging degree corresponding to the first reference data as the reference pipeline aging degree. Among them, the first reference data refers to the historical data used to construct the first vector database.

[0198] In some embodiments, the reference pipeline aging degree is set by professional technicians or the system default.

[0199] In some embodiments, the intelligent gas company management platform calculates the similarity between the first reference vector and the first feature vector respectively, and determines the pipeline aging degree corresponding to the first feature vector. For example, the first reference vector with the maximum similarity is used as the target vector, and the reference pipeline aging degree corresponding to the target vector is used as the pipeline aging degree corresponding to the first feature vector. The similarity between the first reference vector and the first feature vector is negatively correlated with the vector distance between the first reference vector and the first feature vector, and the vector distance is determined based on the cosine distance, etc. For example, the similarity is the reciprocal of the vector distance.

[0200] In some embodiments of this specification, based on the historical impurity accumulation amount, pipeline physical characteristics, historical weather data, and historical gas composition data of the gas pipeline, through the first vector database, the actual aging degree of the pipeline can be evaluated more accurately.

[0201] The definition of a common pipeline node can be found in Figure 3 and its related descriptions. In some embodiments, the attributes of a common pipeline node include but are not limited to future maintenance data, gas monitoring data, weather data, pipeline physical characteristics, and pipeline aging degree.

[0202] In some embodiments, the edges and their attributes of the estimated feature map, as well as the construction method of the estimated feature map, are similar to those of the accumulation feature map, and will not be elaborated here.

[0203] Step S420, based on the estimated feature map, determine the future accumulation amount sequence through the future estimation model.

[0204] The future accumulation amount sequence refers to a set composed of the pipelines corresponding to each node at multiple future time points and the impurity accumulation amount of the pipelines. The form of the future accumulation amount sequence is similar to that of the historical accumulation amount sequence, and will not be elaborated here.

[0205] In some embodiments, the intelligent gas company management platform determines the future accumulation amount sequence based on the estimated feature map through the future estimation model.

[0206] The future prediction model refers to a model used to determine the future accumulation quantity sequence. In some embodiments, the future prediction model is a machine learning model. For example, the future prediction model is a GNN model.

[0207] The input of the future prediction model can be a predicted feature map, and the output can be the future accumulation quantity sequence.

[0208] In some embodiments, the future prediction model is obtained by training based on a sample training set; the sample training set includes a plurality of sample predicted feature maps and their corresponding labels; each sample predicted feature map is constructed based on the sample filtering device type, sample sewage discharge work data, sample gas monitoring data, sample weather data, sample pipeline physical characteristics, sample pipeline aging degree, sample future maintenance data, sample current accumulation quantity set, sample historical accumulation quantity sequence, and sample future sewage discharge data; the label is the sample future accumulation quantity sequence, and the acquisition time point of the sample future accumulation quantity sequence is after the acquisition time point of the sample current accumulation quantity set. Among them, the acquisition time point refers to the time point when the impurity accumulation quantity is acquired.

[0209] In some embodiments, the training method of the future prediction model is similar to that of the accumulation quantity determination model. Please refer to Figure 3 its related description and will not be elaborated here.

[0210] In some embodiments, the sample training set is a historical sample predicted feature map. The intelligent gas company management platform constructs the historical sample predicted feature map based on the first historical moment of the sample training set and the historical sewage discharge work data and historical gas monitoring data before the first historical moment. The label is the future accumulation quantity sequence actually collected at a plurality of second historical moments corresponding to the sample training set. The first historical moment is before the second historical moment. That is, the acquisition time point of the sample future accumulation quantity sequence is a plurality of second historical moments, the acquisition time point of the sample current accumulation quantity set is the first historical moment, and the acquisition time point of the sample future accumulation quantity sequence is after the acquisition time point of the sample current accumulation quantity set.

[0211] In some embodiments, among the plurality of sample predicted feature maps and their corresponding labels, the sample acquisition accuracy of the sewage pipeline corresponding to the sewage pipeline node is greater than the sample acquisition accuracy of the pipeline corresponding to the ordinary node; and the sample acquisition accuracy of the pipeline corresponding to the node is related to the importance score of the pipeline corresponding to the node.

[0212] In some embodiments, the labels of each node have the same time interval. For example, for the sewage pipeline nodes, the data is collected every hour in the future, and the label of the sewage pipeline node is the sewage pipeline and the actual impurity accumulation amount per hour. For the ordinary pipeline nodes, the data is collected every two hours in the future, and the label of the ordinary pipeline node is the ordinary pipeline and the actual impurity accumulation amount per hour corresponding thereto, and the impurity accumulation amount corresponding between two hours is the average value of the corresponding collected data. Only as an example, in response to the ordinary node collecting the impurity accumulation amount at 10:00 and 12:00 in the future, the label corresponding to the ordinary node is the ordinary pipeline, and the impurity accumulation amounts at 10:00, 11:00, and 12:00. The impurity accumulation amount at 11:00 is the average value of the impurity accumulation amounts at 10:00 and 12:00.

[0213] The acquisition accuracy refers to the degree of data accuracy in the data acquisition process.

[0214] In some embodiments, the higher the acquisition accuracy, the more the number of acquisition time points of the sample future accumulation amount sequence within the same time period, that is, the smaller the time interval of the acquisition time points of the sample future accumulation amount sequence. For example, for nodes with a high important score (such as key sewage pipeline nodes), the time interval of their labels may be once per hour; while for ordinary nodes, the time interval may be once every two hours or once every three hours, which is not limited herein. Another example is that in response to the acquisition accuracy of node 1 being greater than that of node 2, the future time points corresponding to the label of node 1 may be 10:00, 11:00, 12:00, etc., and the future time points corresponding to the label of node 2 may be 10:00, 12:00, etc., which is not limited herein.

[0215] In some embodiments of the present specification, in the sample training set, it is ensured that the acquisition accuracy of important nodes is higher than that of ordinary nodes to improve the prediction accuracy of the future prediction model on key nodes. Specifically, the time interval of the acquisition time points of the sample future accumulation amount sequence of important nodes is shorter, and the collected data is more abundant, so that the future prediction model can more accurately capture the change trend of impurity accumulation. This not only enhances the robustness of the future prediction model, but also ensures that the prediction of key nodes is more accurate, thereby more effectively guiding future maintenance and cleaning work. In addition, this differential way of resource allocation can more efficiently utilize limited monitoring resources and optimize the monitoring strategy.

[0216] Step S430, determine the target sewage treatment equipment based on the future accumulation amount sequence and the future threshold.

[0217] The future threshold refers to the threshold for judging whether the average increase amplitude of the impurity accumulation amount in the future time is too large.

[0218] In some embodiments, the intelligent gas company management platform determines a future threshold based on the flow data and importance score of a pipeline through a seventh preset rule. The seventh preset rule includes that the future threshold is negatively correlated with the flow data and importance score of the pipeline.

[0219] For more content on the flow data and importance score of the pipeline, reference can be made to Figure 2 and its related descriptions.

[0220] In some embodiments, in response to the mean increase in the impurity accumulation amount of a sewage disposal device at a future time being greater than the future threshold, the intelligent gas company management platform determines the sewage disposal device as a target sewage disposal device. Wherein, the increase rate of the impurity accumulation amount at a future time refers to the change rate of the impurity accumulation amount corresponding to the future time relative to the initial value or a certain previous time point.

[0221] In some embodiments, the intelligent gas company management platform identifies the time point when the impurity accumulation amount at a future time exceeds the cleaning threshold. In response to the time interval between this time point and the current time point being less than the time threshold, then the sewage disposal device is a target sewage disposal device.

[0222] For content on the cleaning threshold, reference can be made to Figure 3 step S330 and its related descriptions in

[0223] In some embodiments, the intelligent gas company management platform determines the cleaning threshold based on the pipeline importance score and the flow data of the pipeline through an eighth preset rule. The eighth preset rule reflects that the time threshold is negatively correlated with the pipeline importance score and the flow data of the pipeline.

[0224] In some embodiments of this specification, based on sewage disposal work data and gas monitoring data, an estimated feature map is constructed, which helps to more accurately predict the future accumulation amount sequence subsequently. Based on the estimated feature map, the future accumulation amount is predicted through a future prediction model, which can better reflect the complex relationships between data and improve the accuracy and reliability of the prediction. Based on the future accumulation amount sequence and the future threshold, the target sewage disposal device is determined, and the system can more accurately determine the sewage disposal devices that need to be cleaned preferentially, which helps to improve the efficiency of the cleaning work and reduce resource waste.

[0225] In some embodiments, after determining the target sewage disposal device based on steps S410 - S430, the sewage disposal devices that are not determined as target sewage disposal devices are determined as second sewage disposal devices. In response to the number of second sewage disposal devices being at least one, for one second sewage disposal device, the intelligent gas company management platform obtains the second associated pipeline of the sewage disposal pipeline corresponding to the second sewage disposal device based on the gas monitoring data; and determines the target sewage disposal device based on the future accumulation amount sequence, the second associated pipeline, and the association threshold.

[0226] The second sewage disposal device, the second associated pipeline, and the method of obtaining the second associated pipeline are similar to the first sewage disposal device, the first associated pipeline, and the method of obtaining the first associated pipeline. For details, please refer to Figure 3 its related descriptions and will not be elaborated here.

[0227] In some embodiments, the intelligent gas company management platform determines the target sewage disposal device as those whose increase rate of the impurity accumulation amount in the second associated pipeline of the second sewage disposal device exceeds the associated threshold and the number of such devices exceeds the quantity threshold.

[0228] In some embodiments, within a preset future time period, if the number of second associated pipelines with impurity accumulation amounts exceeding the associated threshold of the second sewage disposal device is greater than the quantity threshold, then the sewage disposal device is determined as the target sewage disposal device.

[0229] In some embodiments of this specification, by obtaining the second associated pipeline of the sewage pipeline corresponding to the second sewage disposal device, the impact of the second associated pipeline on the second sewage disposal device can be evaluated, providing more dimensional information for determining the target sewage disposal device. At the same time, by combining the future accumulation amount sequence, the second associated pipeline, and the associated threshold, the target sewage disposal device can be determined more accurately. This not only considers the situation of the second sewage disposal device itself but also the impact of its associated pipeline, thereby improving the efficiency of the cleaning work. In addition, since some impurities may be carried when gas is input into the downstream pipeline, further considering the downstream second associated pipeline also helps to reduce the failure risk caused by impurity accumulation in the downstream pipeline, and thus improves the accuracy of determining the target sewage disposal device.

[0230] Figure 5 is an exemplary flowchart for determining the cleaning work parameters according to some embodiments of this specification. As Figure 5 shown, process 500 includes the following steps. In some embodiments, process 500 can be executed by the intelligent gas company management platform.

[0231] Step S510, obtain at least one candidate work parameter.

[0232] The candidate work parameter refers to one or more parameters that can perform the pipeline cleaning work. The content of the candidate work parameter is similar to the content of the cleaning work parameter. For example, the candidate work parameter includes the opening level of the sewage valve, the monitoring level, and the intelligent work parameters of the cleaning crawler robot.

[0233] In some embodiments, the candidate operating parameters are set empirically. In some embodiments, the intelligent gas company management platform randomly generates candidate operating parameters. For example, the opening level of the sewage discharge valve, the monitoring level, and the intelligent operating parameters for cleaning the crawling robot are randomly generated within their respective operating ranges. Among them, the operating range can be preset. Exemplarily, the operating range is a safe operating range, and the safe operating range is determined according to the factory parameters.

[0234] Step S520, for a candidate operating parameter, based on the candidate operating parameter, the current accumulation amount set of the target sewage discharge device, the accumulation impurity composition, and the pipeline physical characteristics, determine the cleaning effect score and cleaning time corresponding to the candidate operating parameter.

[0235] The accumulation impurity composition refers to the composition of the accumulated impurities in the sewage discharge pipeline. For example, the accumulation impurity composition may include sand, rust, water, water vapor, sulfides, carbon dioxide, grease, dust, etc. In some embodiments, the accumulation impurity composition is obtained based on the gas composition data of the gas before and after filtration. For example, the impurity components with a decrease in the composition data before and after filtration are the accumulation impurity composition.

[0236] The cleaning effect score refers to the score of the cleaning effect of cleaning the sewage discharge pipeline based on the candidate operating parameter. For example, the cleaning effect score can be set as a value from 0 to 1. 0 means no cleaning, 1 means very clean cleaning, and the higher the value, the better the cleaning effect.

[0237] The cleaning time refers to the time required to clean the sewage discharge pipeline based on the candidate operating parameter. For example, it takes 1 hour to complete the cleaning using a certain candidate operating parameter.

[0238] In some embodiments, the intelligent gas company management platform constructs a second feature vector based on the candidate operating parameter, the current accumulation amount set of the target sewage discharge device, the accumulation impurity composition, and the pipeline physical characteristics, and then performs a search in the second vector database based on the second feature vector to determine the cleaning effect score and cleaning time corresponding to the candidate operating parameter.

[0239] Among them, the second vector database is a database for determining the cleaning effect score and cleaning time. In some embodiments, the second vector database contains multiple second reference vectors, as well as the reference cleaning effect score and reference cleaning time corresponding to each second reference vector.

[0240] In some embodiments, the intelligent gas company management platform constructs a second reference vector based on the historical impurity accumulation amount, historical pipeline physical characteristics, historical weather data, and historical gas component data corresponding to the second reference data, and uses the historical cleaning effect score and historical cleaning time corresponding to the second reference data as the reference cleaning effect score and reference cleaning time. Herein, the second reference data refers to the historical data used to construct the second vector database.

[0241] In some embodiments, the reference cleaning time is the actual cleaning time corresponding to the second reference data.

[0242] In some embodiments, the intelligent gas company management platform determines the reference cleaning effect score based on the differences in the filtration scores before and after cleaning, the differences in the flow rate data before and after cleaning, and the differences in the filtration ratios before and after cleaning in the second reference data. The differences in the filtration scores before and after cleaning, the differences in the flow rate data before and after cleaning, and the differences in the filtration ratios before and after cleaning are proportional to the reference cleaning effect score. For more information on the filtration score and flow rate data, please refer to Figure 2 and its related descriptions.

[0243] The filtration ratio is equal to the filtered flow rate data divided by the flow rate data before filtration. The flow rate data before filtration refers to the flow rate data on the front side of the sewage discharge device (the side entering the sewage discharge device), and the filtered flow rate data refers to the flow rate data on the rear side of the sewage discharge device (the side discharging from the sewage discharge device).

[0244] In some embodiments, the intelligent gas company management platform ensures that the data volume in the second vector database is sufficient by continuously updating the data in the second vector database. For example, the data recorded during each cleaning constitutes the second reference vector, and the cleaning effect is scored after cleaning, so as to store the determined second reference vector and its corresponding reference cleaning effect score and reference cleaning time in the second vector database.

[0245] The process of retrieving the second vector database is similar to the process of retrieving the first vector database, which will not be elaborated here.

[0246] Step S530: Determine the cleaning working parameters based on the cleaning effect scores and cleaning times of at least one candidate working parameter.

[0247] In some embodiments, the intelligent gas company management platform selects the one with the highest comprehensive score among at least one candidate working parameter as the determined cleaning working parameter.

[0248] In some embodiments, the intelligent gas company management platform determines the comprehensive score through the following formula (3):

[0249] (3)

[0250] Among them, is the comprehensive score; is the cleaning effect score; t is the cleaning time, , are respectively and coefficients; among them, is a positive number, is a negative number.

[0251] In some embodiments, the intelligent gas company management platform determines the cleaning work parameters based on the cleaning effect score, the cleaning time, and the pipeline importance score of the target sewage discharge equipment. For example, the cleaning effect score in formula (3) The coefficient is related to the pipeline importance score of the target sewage discharge equipment. The pipeline importance score of the target sewage discharge equipment is proportional to the coefficient is proportional.

[0252] In some embodiments of this specification, when determining the cleaning work parameters, considering the pipeline importance score, for pipelines with a higher importance score, more resources can be allocated to ensure the effectiveness of the cleaning work. By assigning a higher weight to the cleaning effect score of important pipelines with a high importance score, it can ensure that these pipelines obtain the best cleaning effect, reduce the possibility of impurity residue, thereby extending the service life of the equipment and reducing the failure risk.

[0253] In some embodiments of this specification, determining the cleaning work parameters based on the cleaning effect score and the cleaning time can efficiently select reasonable work parameters, reduce the working time while achieving the cleaning purpose, and improve the work efficiency of cleaning.

[0254] In some embodiments, in response to the accumulated impurity components meeting the additional cleaning conditions, the intelligent gas company management platform generates an additional cleaning instruction based on the accumulated impurity components, and issues the additional cleaning instruction to the intelligent gas equipment object platform to control the cleaning crawler robot to carry an additional cleaning component to clean the target sewage discharge equipment.

[0255] The additional cleaning condition refers to the condition for judging whether other cleaning methods except the basic cleaning method (for example, jetting water flow) need to be used for cleaning. In some embodiments, the additional cleaning condition includes that the content of a specific impurity component is greater than the corresponding condition threshold. Among them, the specific impurity component refers to the impurity that cannot be removed by the basic cleaning method. The corresponding condition thresholds for different specific impurity components are preset thresholds. For example, the additional cleaning conditions include that the content of corrosive substances in the accumulated impurity components exceeds m%, the content of chemical deposits in the accumulated impurity components exceeds n%, the content of microorganisms in the accumulated impurity components exceeds v%, etc.

[0256] Understandably, different cleaning methods are applicable to different specific impurity components. For example, additional cleaning conditions include that the content of corrosive substances in the accumulated impurity components exceeds m%, or the content of chemical deposits in the accumulated impurity components exceeds n%. In this case, ordinary cleaning methods cannot effectively remove the accumulated impurities. At this time, chemical cleaning agents or solvents can be used to assist in the removal. For another example, when the content of microorganisms in the accumulated impurity components exceeds v%, a biocide that does not cause damage to the pipeline (such as a fungicide or disinfectant without side effects) can be used for cleaning.

[0257] The additional cleaning instruction refers to controlling the cleaning crawler robot to carry an additional cleaning component to clean the target sewage discharge device. Among them, the additional cleaning component refers to a component other than the cleaning component used in the basic cleaning method. For example, the additional cleaning instruction includes an instruction to direct the robot to load a fungicide sprayer, evenly spray disinfectant on the inner wall of the pipeline, and stay for a period of time.

[0258] In some embodiments, the intelligent gas company management platform generates additional cleaning instructions based on the accumulated impurity components through the ninth preset rule. Among them, the ninth preset rule refers to different additional cleaning conditions corresponding to additional cleaning instructions, and the additional cleaning instructions are set by technicians. For example, the ninth preset rule includes that when the content of microorganisms in the accumulated impurity components exceeds v%, the additional cleaning instruction is an instruction to direct the robot to load a fungicide sprayer, evenly spray disinfectant on the inner wall of the pipeline, and stay for a period of time.

[0259] According to some embodiments of this specification, for the accumulated impurities that are difficult to clean, through additional cleaning instructions, controlling the cleaning crawler robot to perform additional cleaning operations on the difficult-to-clean accumulated impurities can conduct a more comprehensive cleaning of the sewage pipeline and improve the cleaning effect.

[0260] In some embodiments, the cleaning crawler robot further includes an ultrasonic sensor. Before cleaning, the cleaning crawler robot collects ultrasonic data based on the ultrasonic sensor and uploads the ultrasonic data to the intelligent gas equipment object platform and then to the intelligent gas company management platform; and the intelligent gas company management platform determines the actual pipeline information and the actual working parameters of the cleaning crawler robot based on the ultrasonic data.

[0261] An ultrasonic sensor refers to a sensor that converts ultrasonic signals into other energy signals (usually electrical signals). Ultrasonic waves have a good penetration effect on liquids and solids. When they encounter impurities or interfaces, significant reflections will occur to form reflected echoes. Therefore, it can be used to measure the actual thickness of the pipeline, the thickness of the accumulated impurities, etc.

[0262] Ultrasonic data refers to the data collected by using an ultrasonic sensor.

[0263] The actual pipeline information refers to the information of the real pipeline and the accumulated impurities. For example, the actual pipeline information may include the actual thickness of the pipeline, the thickness of the accumulated impurities, etc.

[0264] In some embodiments, the intelligent gas company management platform obtains the actual pipeline information based on ultrasonic data. In some embodiments, various methods can be used to process the ultrasonic data to obtain the actual pipeline information. For example, algorithms such as the Pulse-Echo Method (PEM) and the Through-Transmission Method (TTM) can be used.

[0265] The actual working parameters refer to the parameters used by the cleaning crawler robot during actual cleaning work.

[0266] In some embodiments, the intelligent gas company management platform determines the actual working parameters according to the actual pipeline information through the tenth preset rule.

[0267] The tenth preset rule refers to the rule of the corresponding actual working parameters for different actual pipeline information. The actual working parameters are set by technicians. For example, the tenth preset rule includes that when the pipeline thickness at a certain position in the pipeline is lower than the thickness threshold, and / or the thickness of the accumulated impurities is lower than the accumulation threshold, to avoid further damage to the pipeline wall, the cleaning crawler robot uses a brush and a jet of water for cleaning, and the cleaning intensity of the brush and the jet of water is positively correlated with the thickness of the impurities accumulated at that position. Another example is that the tenth preset rule includes that when the pipeline thickness at a certain position in the pipeline is higher than the thickness threshold, and the thickness of the accumulated impurities is higher than the accumulation threshold, the cleaning crawler robot uses a scraper and a jet of water for cleaning, and the cleaning intensity of the brush and the jet of water is positively correlated with the thickness of the impurities accumulated at that position. Among them, the thickness threshold and the accumulation threshold are preset by technicians.

[0268] According to some embodiments of this specification, by using ultrasonic sensors, the real pipe wall thickness and the thickness of the accumulated impurities can be determined, more accurate actual working parameters can be determined, the cleaning effect can be achieved while protecting the pipe wall, and further damage to the pipe wall can be avoided.

[0269] This specification provides a computer-readable storage medium, and the storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the above-mentioned pipeline cleaning method based on the intelligent gas supervision Internet of Things.

[0270] The embodiments in this specification are only for illustration and explanation, and do not limit the scope of application of this specification. For those skilled in the art, various corrections and changes that can be made under the guidance of this specification are still within the scope of this specification.

[0271] In one or more embodiments of this specification, certain features, structures, or characteristics may be appropriately combined.

[0272] If there are any inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the materials cited in this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

Claims

1. A pipeline cleaning method based on smart gas monitoring Internet of Things, characterized in that: The method is performed by a smart gas supervision Internet of Things system, which includes a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a smart gas gas company sensor network platform and a smart gas equipment object platform; the smart gas government safety supervision object platform includes a smart gas gas company management platform, the smart gas gas company management platform includes a gas database, and the gas database is configured as a storage device; the smart gas government safety supervision management platform and the smart gas gas company management platform are configured as servers; the smart gas government safety supervision sensor network platform and the smart gas gas company sensor network platform are configured as a communication network, and the smart gas equipment object platform is configured as a sewage discharge device and a gas monitoring device, the sewage discharge device includes at least one of a filtering device and a sewage discharge pipeline, and the gas monitoring device is arranged on the gas pipeline; the method includes: The smart gas company management platform obtains the sewage discharge work data and gas monitoring data collected by the smart gas equipment object platform through the smart gas company sensor network platform, and stores the sewage discharge work data and the gas monitoring data in the gas database; The smart gas company management platform: constructing a stacking characteristic diagram based on the sewage discharge work data and the gas monitoring data; Based on the accumulation feature graph, a current accumulation amount set is determined by an accumulation amount determination model, wherein the accumulation amount determination model is a machine learning model; Determining a target sewage discharge device based on the current accumulation amount set and the cleaning threshold; Uploading the target sewage discharge equipment and its corresponding cleaning work parameters to the smart gas government safety supervision and management platform, wherein the cleaning work parameters include the sewage discharge valve opening level, the monitoring level and the intelligent working parameters of the cleaning crawler robot; and The smart gas government safety supervision and management platform generates a cleaning control instruction based on the target sewage discharge equipment and its corresponding cleaning work parameters, and sends the cleaning control instruction to the smart gas equipment object platform to control the target sewage discharge equipment to clean with the cleaning work parameters.

2. The method according to claim 1, characterized in that The cleaning and control instructions also include indirect control instructions, which are configured to increase the monitoring level of the gas monitoring equipment on the affected pipeline when cleaning the target sewage discharge equipment. The affected pipeline refers to a pipeline related to the sewage pipe corresponding to the target sewage discharge equipment.

3. The method according to claim 1, characterized in that The stacking feature graph is composed of nodes and edges, the nodes include sewage pipe nodes and ordinary pipe nodes, the attributes of the sewage pipe nodes include filtering equipment type, the sewage working data, the gas monitoring data, weather data and pipeline physical characteristics, the attributes of the ordinary pipe nodes include the gas monitoring data, the weather data and pipeline physical characteristics, the edges include the lines between two physically connected nodes, the attributes of the edges include gas fluid direction, the sewage working data include current sewage data and historical sewage data, the gas monitoring data include current monitoring data and historical monitoring data, and the weather data include current weather data and historical weather data.

4. The method according to claim 3, characterized in that The current accumulation amount set includes the sewage pipeline corresponding to at least one sewage pipeline node and its current impurity accumulation amount, and the method further includes: For the sewage pipe corresponding to a sewage pipe node and its current impurity accumulation amount, in response to the current impurity accumulation amount being less than the cleaning threshold and greater than the attention threshold, the smart gas company management platform determines the sewage pipe corresponding to the current impurity accumulation amount as a concerned sewage pipe, and generates a monitoring enhancement instruction, and sends the monitoring enhancement instruction to the smart gas equipment object platform to increase the monitoring level of the gas monitoring equipment of the concerned sewage pipe.

5. The method according to claim 3, characterized in that Determination of the cleaning threshold includes: The cleaning threshold is determined based on the pipeline physical characteristics, the historical monitoring data and the pipeline importance score.

6. The method according to claim 3, characterized in that In response to the number of the first sewage discharge equipment being at least one, the first sewage discharge equipment being the sewage discharge equipment that is not determined as the target sewage discharge equipment, the method further includes: For one of the first sewage treatment equipment, Based on the gas monitoring data, obtaining a first associated pipe of the sewage pipe corresponding to the first sewage discharge equipment; and The target sewage discharge equipment is determined based on the current accumulation amount set, the first associated pipeline and the associated threshold.

7. The method according to claim 1, characterized in that In response to the target sewage discharge equipment being determined, the method further includes: obtaining at least one candidate working parameter; For one of the candidate operating parameters, based on the candidate operating parameter, the current accumulation amount set of the target sewage discharge equipment, the accumulation impurity composition and the physical characteristics of the pipeline, determine the cleaning effect score and cleaning time corresponding to the candidate operating parameter; and The cleaning operating parameter is determined based on the cleaning effect score and the cleaning time of the at least one candidate operating parameter.

8. A smart gas monitoring Internet of Things system, characterized in that: The Internet of Things system includes a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a smart gas gas company sensor network platform and a smart gas equipment object platform; the smart gas government safety supervision object platform includes a smart gas gas company management platform, the smart gas gas company management platform includes a gas database, and the gas database is configured as a storage device; the smart gas government safety supervision management platform and the smart gas gas company management platform are configured as servers; the smart gas government safety supervision sensor network platform and the smart gas gas company sensor network platform are configured as a communication network, and the smart gas equipment object platform is configured as a sewage discharge device and a gas monitoring device, the sewage discharge device includes at least one of a filtering device and a sewage discharge pipeline, and the gas monitoring device is arranged on the gas pipeline; the Internet of Things system is configured as: The smart gas company management platform obtains the sewage discharge work data and gas monitoring data collected by the smart gas equipment object platform through the smart gas company sensor network platform, and stores the sewage discharge work data and the gas monitoring data in the gas database; The smart gas company management platform: constructing a stacking characteristic diagram based on the sewage discharge work data and the gas monitoring data; Based on the accumulation feature graph, a current accumulation amount set is determined by an accumulation amount determination model, wherein the accumulation amount determination model is a machine learning model; Determining a target sewage discharge device based on the current accumulation amount set and the cleaning threshold; Uploading the target sewage discharge equipment and its corresponding cleaning work parameters to the smart gas government safety supervision and management platform, wherein the cleaning work parameters include the sewage discharge valve opening level, the monitoring level and the intelligent working parameters of the cleaning crawler robot; and The smart gas government safety supervision and management platform generates a cleaning control instruction based on the target sewage discharge equipment and its corresponding cleaning work parameters, and sends the cleaning control instruction to the smart gas equipment object platform to control the target sewage discharge equipment to clean with the cleaning work parameters.

9. 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 pipeline cleaning method based on the smart gas supervision Internet of Things as described in claim 1.

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