Intelligent gas pipeline temperature control method and system based on Internet of Things

Abstract

The embodiment of the invention provides an intelligent gas pipeline temperature control method and system based on the Internet of Things, and the method is realized based on the intelligent gas pipeline temperature control system, and comprises the steps: obtaining the pipeline information of a gas pipeline and basic sensing data collected and uploaded by a gas equipment object platform based on a gas company management platform, and obtaining candidate parameters from the government safety supervision management platform, determining transmission parameters through at least one round of iterative interaction with the government safety supervision management platform, and controlling air compression equipment of the fuel gas equipment object platform to adjust the fuel gas transmission temperature based on the transmission parameters. By introducing an intelligent Internet of Things system, a temperature monitoring technology and emergency response measures, real-time and accurate monitoring on the temperature of the gas pipeline is realized, and effective measures are taken for intervention and adjustment on a problem source, so that safe and stable operation of the natural gas pipeline is ensured, and continuity and reliability of natural gas supply are guaranteed.

Description

Technical Field

[0001] This specification relates to the field of gas pipelines, and in particular to a smart gas pipeline temperature control method and system based on the Internet of Things. Background Art

[0002] As an important infrastructure for modern energy transportation, natural gas pipelines bear the heavy responsibility of safely and efficiently transporting natural gas. Maintaining a suitable temperature is crucial for natural gas pipelines. Too high or too low temperatures are not conducive to transportation. If the temperature in the natural gas pipeline is too low, the water in the natural gas may form hydrates, which will further accumulate and freeze, causing pipeline blockage and reducing the efficiency of natural gas transportation. High temperatures are particularly challenging, as they can easily cause the pipeline to expand and deform under pressure, causing the pipeline to lose its bearing capacity, shorten its life, and even create the risk of rupture, threatening the stability and safety of energy supply.

[0003] CN113124327B discloses a monitoring device for a natural gas pipeline, which uses a natural gas pipeline sensor to monitor temperature and pressure data, evaluates leakage risk based on a temperature and pressure combination criterion, and establishes a pipeline coordinate system to locate and alarm the leakage point. However, this solution does not consider adjusting the pipeline through pipeline cooling, gas flow regulation, etc. to reduce the risk of leakage. Summary of the invention

[0004] Some embodiments of this specification provide a smart gas pipeline temperature control method and system based on the Internet of Things to solve many problems caused by excessively high or low temperatures in the gas pipeline, thereby ensuring the safe and stable operation of the natural gas pipeline and guaranteeing the continuity and reliability of the natural gas supply.

[0005] One or more embodiments of the present specification provide a smart gas pipeline temperature control method based on the Internet of Things, which is implemented based on a smart gas pipeline temperature control system. The method includes: based on a gas company management platform, obtaining pipeline information of the gas pipeline and basic perception data collected and uploaded by a gas equipment object platform, and obtaining candidate parameters from a government safety supervision management platform, and determining the transmission parameters through at least one round of iterative interaction with the government safety supervision management platform; and based on the transmission parameters, controlling the air compressor equipment of the gas equipment object platform to adjust the gas transmission temperature; wherein one round of the iterative interaction includes: the gas company management platform determines the temperature impact of the gas pipeline through a first model based on the candidate parameters, the pipeline information and the environmental information; the first model is a machine learning model; the temperature impact of the gas pipeline is sent to the government safety supervision management platform; the government safety supervision management platform determines the deformation evaluation of the gas pipeline based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information and the basic perception data; based on the deformation evaluation of the gas pipeline, the candidate parameters are updated to obtain the updated candidate parameters; and in response to the deformation evaluation of the gas pipeline meeting the preset deformation condition, the updated candidate parameters are determined as the transmission parameters.

[0006] One or more embodiments of the present specification provide a smart gas pipeline temperature control system based on the Internet of Things, characterized in that the system includes a management platform, a sensor network platform and a gas equipment object platform respectively configured on different servers; the management platform includes a gas company management platform and a government safety supervision management platform, and the gas company management platform and the government safety supervision management platform exchange data through the sensor network platform; the sensor network platform includes a gas company sensor network platform and a government safety supervision sensor network platform, and the sensor network platform operates based on data communication equipment; the gas company management platform is configured to: obtain pipeline information of the gas pipeline and basic perception data collected and uploaded by the gas equipment object platform, and obtain candidate parameters from the government safety supervision management platform, and communicate with at least one of the government safety supervision management platform round of iterative interaction to determine the delivery parameters; and based on the delivery parameters, control the air compressor equipment of the gas equipment object platform to adjust the gas delivery temperature; wherein, one round of the iterative interaction includes: based on the candidate parameters, the pipeline information and the environmental information, determining the temperature impact of the gas pipeline through a first model; the first model is a machine learning model; sending the temperature impact of the gas pipeline to the government safety supervision and management platform; the government safety supervision and management platform is configured to: determine the deformation evaluation of the gas pipeline based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information and the basic perception data; update the candidate parameters based on the deformation evaluation of the gas pipeline to obtain updated candidate parameters; and in response to the deformation evaluation of the gas pipeline satisfying the preset deformation conditions, determine the updated candidate parameters as the delivery parameters.

[0007] In some embodiments of this specification, the gas company management platform can realize the real-time supervision of the gas company and gas pipelines by the government safety supervision management platform through iterative interaction with the government safety supervision management platform. In addition, since the gas temperature in the gas pipeline will change during the gas transportation process, it is beneficial for the government safety supervision management platform to determine more accurate transportation parameters by considering the temperature impact of the gas pipeline and sending it to the government safety supervision management platform. The gas company management platform then controls the air compressor to adjust the gas transportation temperature based on the transportation parameters, which can effectively avoid gas pipeline safety problems caused by excessively high or low temperatures, and is also beneficial to ensuring gas transportation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1is an exemplary schematic diagram of a smart gas pipeline temperature control Internet of Things system according to some embodiments of this specification; Figure 2 is an exemplary module diagram of a management platform according to some embodiments of this specification; Figure 3 is an exemplary schematic diagram of a smart gas pipeline temperature control method based on the Internet of Things according to some embodiments of this specification; Figure 4 is an exemplary flow chart of a method for determining a delivery parameter according to some embodiments of this specification; Figure 5 is an exemplary schematic diagram of a first model according to some embodiments of this specification; Figure 6 is an exemplary schematic diagram of a second model according to some embodiments of this specification; Figure 7 is an exemplary schematic diagram of a method for adjusting deformation assessment of each pipeline segment according to some embodiments of the present specification; Figure 8 is an exemplary flow chart of a method for adjusting deformation assessment of each pipeline segment according to other embodiments of the present specification; Fig. 9 is an exemplary flow chart of a method for updating candidate parameters according to some embodiments of this specification; Fig.10 is an exemplary flow chart of a method for adjusting delivery parameters according to some embodiments of the present specification. DETAILED DESCRIPTION

[0009] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0010] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 It is an exemplary schematic diagram of a smart gas pipeline temperature control Internet of Things system according to some embodiments of this specification.

[0012] Data communication equipment refers to equipment used to realize data exchange and transmission between different platforms in a control system. Exemplary data communication equipment may include but is not limited to gateways, etc. In some embodiments, the data communication equipment supports data communication between the gas company management platform and the government safety supervision management platform, and between the gas company management platform and the gas equipment object platform, ensuring real-time sharing and processing of information.

[0013] The management platform is configured as a platform for coordinating the connection and collaboration between various functional platforms. The gas company management platform refers to a platform used to exchange data with the government safety supervision management platform and the gas equipment object platform to jointly realize the intelligent monitoring and management of the gas pipeline. In some embodiments, the gas company management platform is configured to: obtain pipeline information of the gas pipeline and basic perception data collected and uploaded by the gas equipment object platform, and obtain candidate parameters from the government safety supervision management platform, and determine the transportation parameters through at least one round of iterative interaction with the government safety supervision management platform; and based on the transportation parameters, control the air compressor equipment of the gas equipment object platform to adjust the gas transportation temperature. With the help of the sensor network platform, the gas company management platform can realize collaborative operations with government regulatory departments through data exchange with the government safety supervision management platform and the gas equipment object platform to ensure the safe, efficient and intelligent operation of the gas pipeline.

[0014] The government safety supervision and management platform refers to a platform for safety supervision of gas pipelines and gas companies. That is to say, the object platform of the government safety supervision and management platform (referred to as the government safety supervision object platform) may include a gas company management platform. In some embodiments, the government safety supervision and management platform is configured to: determine the deformation evaluation of the gas pipeline based on the temperature influence, candidate parameters, pipeline information and basic sensing data of the gas pipeline; update the candidate parameters based on the deformation evaluation of the gas pipeline to obtain updated candidate parameters; and in response to the deformation evaluation of the gas pipeline meeting the preset deformation conditions, determine the updated candidate parameters as the transmission parameters. The government safety supervision and management platform and the gas company management platform can be configured on different servers respectively to process the data and / or information obtained by each and execute relevant program instructions. The government safety supervision and management platform exchanges data with the gas company management platform through the sensor network platform, which can realize the safety supervision and intelligent management of the gas pipeline, ensure the safe operation of the gas pipeline and meet the government supervision requirements.

[0015] A sensor network platform is configured as a communication network and a gateway. In some embodiments, the sensor network platform includes a gas company sensor network platform and a government safety supervision sensor network platform. The gas company sensor network platform refers to a platform used to exchange data with a gas company management platform and a gas equipment object platform. In some embodiments, the gas company sensor network platform can receive basic perception data uploaded by the gas equipment object platform and transmit it to the gas company management platform. The government safety supervision sensor network platform refers to a platform used to exchange data with the government safety supervision management platform and the gas company management platform. In some embodiments, the government safety supervision sensor network platform can receive the temperature impact of the gas pipeline determined by the gas company management platform and transmit it to the government safety supervision management platform. In some embodiments, the government safety supervision sensor network platform can also receive the delivery parameters determined by the government safety supervision management platform and send them to the gas company management platform, so that the gas company management platform controls the air compressor of the gas equipment object platform to adjust the gas delivery temperature based on the delivery parameters.

[0016] The gas equipment object platform refers to a platform for obtaining data and / or information related to gas pipeline equipment. For example, the gas equipment object platform can be used to obtain various data such as the actual temperature of the checkpoint and basic sensing data.

[0017] The gas equipment object platform may be composed of a plurality of sensor devices, including but not limited to temperature sensors, flow meters, etc. In some embodiments, the gas equipment object platform may also include other devices, such as interactive devices, etc.

[0018] For more information about the IoT-based smart gas pipeline temperature control system, please refer to the relevant description below.

[0019] Figure 2 is an exemplary module diagram of a management platform according to some embodiments of this specification. Figure 2 As shown, the management platform is configured with a parameter acquisition module 210, a temperature impact determination module 220, a deformation assessment determination module 230, a candidate parameter update module 240, a transport parameter determination module 250, and a transport temperature adjustment module 260. In some embodiments, the parameter acquisition module 210, the temperature impact determination module 220, the deformation assessment determination module 230, the candidate parameter update module 240, the transport parameter determination module 250, and the transport temperature adjustment module 260 are all communicatively connected. Exemplary communication connection methods include, but are not limited to, Bluetooth, WIFI, 5G, etc. In some embodiments, the parameter acquisition module 210, the temperature impact determination module 220, the deformation assessment determination module 230, the candidate parameter update module 240, the transport parameter determination module 250, and the transport temperature adjustment module 260 may have their own processors, or may share a processor.

[0020] The processor may process data and / or information obtained from other devices or system components. The processor may execute program instructions based on these data, information and / or processing results to perform one or more functions described in this application. In some embodiments, the processor may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-core processing device). By way of example only, the processor may include a central processing unit (CPU), a controller, a microprocessor, etc., or any combination thereof.

[0021] In some embodiments, the parameter acquisition module 210 may be configured to acquire basic sensory data, candidate parameters, and pipeline information. In some embodiments, the parameter acquisition module 210 may include a sensing device. Exemplary sensing devices may include, but are not limited to, temperature sensors, speed sensors, flow meters, and the like. In some embodiments, temperature sensors and flow meters may be arranged at different locations of the gas pipeline (such as a delivery station, etc.) to respectively collect the outside temperature and gas delivery rate of the gas pipeline at different locations. In some embodiments, the parameter acquisition module 210 may also include an interactive device. For example, an electronic component with interactive functions such as an operation console and a desktop computer. In some embodiments, managers may input candidate parameters and pipeline information through an interactive device.

[0022] In some embodiments, the temperature impact determination module 220 may be configured to determine the temperature impact of the gas pipeline through a first model based on the candidate parameters, the pipeline information, and the environmental information.

[0023] In some embodiments, the deformation assessment determination module 230 may be configured to determine the deformation assessment of the gas pipeline based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information, and the basic perception data. In some embodiments, the gas pipeline includes a plurality of pipeline segments. The deformation assessment determination module 230 may be further configured to determine the deformation assessment of the gas pipeline through a second model based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information, and the basic perception data; the second model is a machine learning model. The second model includes a plurality of deformation layers, each of which is configured to determine the deformation assessment of a pipeline segment among the plurality of pipeline segments.

[0024] In some embodiments, the candidate parameter updating module 240 may be configured to update the candidate parameters based on the deformation assessment of the gas pipeline to obtain updated candidate parameters. In some embodiments, the candidate parameter updating module 240 may be further configured to determine a first amplitude of the candidate parameters based on the deformation assessment corresponding to the candidate parameters; determine a second amplitude of the candidate parameters based on the consistency of the deformation assessment and the gas flow direction; and update the candidate parameters based on the first amplitude and the second amplitude.

[0025] In some embodiments, the delivery parameter determination module 250 may be configured to determine the updated candidate parameter as the delivery parameter in response to the deformation assessment of the gas pipeline satisfying a preset deformation condition.

[0026] In some embodiments, the delivery temperature adjustment module 260 may be configured to control the air compressor of the gas equipment object platform to adjust the gas delivery temperature based on the delivery parameters.

[0027] In some embodiments, the smart gas pipeline temperature control system 200 based on the Internet of Things further includes a deformation assessment adjustment module 270 and a delivery parameter adjustment module 280. The deformation assessment adjustment module 270 and the delivery parameter adjustment module 280 are both communicatively connected with the aforementioned modules. In some embodiments, the deformation assessment adjustment module 270 and the delivery parameter adjustment module 280 may also have their own processors, or share a processor with other modules.

[0028] In some embodiments, the deformation assessment adjustment module 270 can be configured to determine a first confidence level of each pipeline segment based on pipeline information, environmental information, transport parameters, inlet data of each pipeline segment, and historical pipeline leakage information; and adjust the deformation assessment of each pipeline segment based on the first confidence level of each pipeline segment. In some embodiments, the gas pipeline is provided with multiple checkpoints. The deformation assessment adjustment module 270 can be further configured to obtain the actual temperatures of the multiple checkpoints; determine the second confidence level of each pipeline segment based on the actual temperatures of the multiple checkpoints and historical predictions; and adjust the deformation assessment of each pipeline segment based on the first confidence level and the second confidence level.

[0029] In some embodiments, the gas pipeline is provided with multiple checkpoints, and the multiple checkpoints correspond to at least one delivery station. The delivery parameter adjustment module 280 can be configured to obtain the actual temperature of the multiple checkpoints; determine the confidence of at least one delivery station corresponding to the multiple checkpoints based on the actual temperature of the multiple checkpoints and the historical prediction; and adjust the delivery parameters based on the confidence of at least one delivery station.

[0030] It should be noted that the above description of the management platform and its modules is only for convenience of description and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the smart gas pipeline temperature control system based on the Internet of Things, it is possible to arbitrarily combine the modules or form a subsystem connected with other modules without deviating from this principle.

[0031] Some embodiments of this specification provide a smart gas pipeline temperature control method based on the Internet of Things, which is implemented based on a smart gas pipeline temperature control system. For more information about the smart gas pipeline temperature control system, please refer to Figure 1-Figure 2 and its related description.

[0032] Figure 3 This is an exemplary schematic diagram of a smart gas pipeline temperature control method based on the Internet of Things according to some embodiments of this specification. Figure 3 As shown, the smart gas pipeline temperature control method based on the Internet of Things includes: based on the gas company management platform 350, obtaining the pipeline information 310 of the gas pipeline and the basic perception data 320 collected and uploaded by the gas equipment object platform 340, and obtaining the candidate parameters 330 from the government safety supervision management platform 360, and determining the delivery parameters 370 through iterative interaction with the government safety supervision management platform 360; and based on the delivery parameters 370, controlling the air compressor equipment of the gas equipment object platform 340 to adjust the gas delivery temperature 380.

[0033] The pipeline information of the gas pipeline refers to various data and / or information related to the gas pipeline. In some embodiments, the pipeline information of the gas pipeline may include at least one of the topological structure, length, diameter, bending degree, material and thickness of the gas pipeline. In some embodiments, the pipeline information of the gas pipeline may also include the setting information of the corrugated compensator. The setting information of the corrugated compensator refers to the information related to the setting position of the corrugated compensator. The corrugated compensator is a compensating element used in the pipeline system, which is mainly used to compensate for the dimensional changes caused by factors such as temperature changes, mechanical displacement and vibration, so as to ensure the safe and stable operation of the pipeline system. The working principle of the corrugated compensator is mainly based on the "corrugation effect", that is, by forming a corrugated shape on the pipe blank by hydraulic or mechanical means, the pipe blank has elasticity and compensation ability. In some embodiments, the gas company management platform can directly retrieve the pipeline information of the gas pipeline from the database. In some embodiments, the pipeline information of the gas pipeline can also be obtained through input by management personnel.

[0034] Basic sensing data refers to information related to gas pipelines and gas transmission. Figure 3As shown, the basic perception data 320 may include at least one of environmental information 321 and flow information 322. In some embodiments, the basic perception data can be collected by sensing devices (such as temperature sensors, flow meters, etc.) located at different gas pipelines or valve positions, and then uploaded by the sensing devices to the gas equipment object platform for aggregation and storage. The gas equipment object platform reports the basic perception data to the gas company management platform according to the preset reporting rules. Among them, the preset reporting rules can be determined based on manual input or prior experience. For example, the preset reporting rules can be to report the basic perception data to the gas company management platform at preset time intervals (such as 30s, 1min). For more information on how to obtain basic perception data, please refer to Figure 1 and its related description.

[0035] Environmental information refers to the gas temperature information and external environmental information at different locations of the gas pipeline. In some embodiments, the external environmental information may include at least one of the soil layer thickness and the external temperature associated with a specific location. Among them, the specific location refers to the location in the gas pipeline where the environmental information needs to be observed, and the specific location can be determined manually. The soil layer thickness refers to the depth of the gas pipeline buried in the soil layer, which can be determined by the pipeline installation information. The pipeline installation information includes relevant installation information of the gas pipeline when it is initially installed (such as the soil layer thickness of the gas pipeline, the installation location, etc.). In some embodiments, the pipeline installation information can be obtained by manual input. The external temperature can be collected and obtained in real time by a temperature sensor. The gas temperature information refers to the gas temperature in the gas pipeline. In some embodiments, the gas temperature information can also be collected and obtained in real time by a temperature sensor.

[0036] Flow information refers to information related to gas transmission in a gas pipeline. In some embodiments, the flow information may include at least one of an inlet flow and an outlet flow. In some embodiments, the flow information may be obtained by a flow meter.

[0037] The candidate parameter is the temperature or velocity of the gas as it is transported along the gas flow direction. For more information on gas flow direction, see Fig. 9 In some embodiments, the candidate parameters may include at least one of the candidate gas delivery temperature and the candidate gas delivery rate of each delivery station. For more information about the delivery station, please refer to the following (such as Fig.10 In some embodiments, the candidate parameters may be obtained by input from a management personnel.

[0038] The delivery parameters refer to the relevant parameters of the gas transmission in the gas pipeline. In some embodiments, the delivery parameters may include at least one of the gas delivery temperature and the gas delivery rate of each delivery station. Among them, the gas delivery temperature refers to the temperature of the delivered gas. The gas delivery rate refers to the rate of delivering the gas. In some embodiments, each delivery station can set its own gas delivery temperature and gas delivery rate, and the gas delivery temperature and gas delivery rate of each delivery station can be the same or different. In some embodiments, the gas company management platform can obtain the delivery parameters from the government safety supervision management platform through at least one round of iterative interaction with the government safety supervision management platform, thereby determining the delivery parameters. For specific instructions on how the government safety supervision management platform determines the delivery parameters, please refer to Figure 4 A round of iterative interaction includes: determining the temperature impact of the gas pipeline through a first model based on candidate parameters, pipeline information and environmental information; and sending the temperature impact of the gas pipeline to a government safety supervision management platform.

[0039] The temperature impact of the gas pipeline can reflect the temperature change of the gas pipeline. In some embodiments, the gas pipeline includes multiple pipeline segments, so the temperature impact of the gas pipeline includes the temperature impact of each pipeline segment in the multiple pipeline segments. In some embodiments, a delivery station can be used to control one or more gas pipelines or gas pipeline segments. When the delivery station controls multiple pipeline segments, since each pipeline segment will be affected by various factors (such as external temperature, gas delivery rate, pipeline information, etc.), the gas temperature in each pipeline segment may change during the gas transportation process. In other words, the temperature impact of each pipeline segment may be the same or different. In some embodiments, the dividing point of the pipeline segment may be a position where a flow meter and a temperature sensor are simultaneously provided in the gas pipeline. In some embodiments, the gas pipeline can also be divided in any other feasible manner to form multiple pipeline segments. For example, random division, etc. For more information about pipeline segments and their dividing points, please refer to the following text (such as Figure 5 ) in the relevant description.

[0040] In some embodiments, the temperature impact of the gas pipeline can be represented by an array. For example, the temperature impact of the gas pipeline can be represented as ((a1, b1, m11), (a1, b2, m12), ..., (a2, b1, m21), ...). Wherein, a1 and a2 represent pipeline segment 1 and pipeline segment 2 respectively; b1 and b2 represent candidate parameter 1 and candidate parameter 2 respectively; m11 represents the temperature impact of pipeline segment 1 under candidate parameter 1, m12 represents the temperature impact of pipeline segment 1 under candidate parameter 2, and m21 represents the temperature impact of pipeline segment 2 under candidate parameter 1. As an example only, if m11 is +2°C or -5°C, it means that the outlet temperature of pipeline segment 1 is 2°C higher or 5°C lower than the inlet temperature under candidate parameter 1, that is, the temperature impact of candidate parameter 1 on pipeline segment 1 is +2°C or -5°C. Wherein, the outlet temperature refers to the gas temperature when the gas is output from each pipeline segment along the gas flow direction. The inlet temperature refers to the gas temperature when the gas enters each pipeline segment. In some embodiments, the outlet temperature of the preceding segment is the inlet temperature of the current segment. The current segment refers to the current pipe segment; the preceding segment refers to the pipe segment located one segment above the current segment relative to the current segment. In some embodiments, the gas flows to the current segment via the preceding segment.

[0041] In some embodiments, the first model can process candidate parameters, pipeline information, and environmental information to determine the temperature impact of the gas pipeline. The first model refers to a model used to determine the temperature impact of the gas pipeline. In some embodiments, the first model can be a machine learning model. For example, the first model can include a combination of one or more of a deep neural network (DNN) model, a neural network (NN) model, and a recurrent neural network (RNN) model. For more information on how the first model determines the temperature impact of the gas pipeline, see Figure 5 and its related description.

[0042] In some embodiments, the gas company management platform may also determine the temperature impact of the gas pipeline based on the pipeline information and environmental information in the following manner: S1: Based on the candidate parameters, pipeline information and environmental information, the theoretical outlet temperature of the first pipeline segment is calculated by Fourier's Law, Newton's Law of Cooling and Stefan-Boltzmann Law, and then the theoretical inlet temperature and theoretical outlet temperature of each pipeline segment are obtained. As mentioned above, the outlet temperature of the previous segment is the inlet temperature of the current segment. Therefore, for other pipeline segments, the theoretical outlet temperatures of other pipeline segments can be calculated based on the theoretical outlet temperature of the previous segment and the pipeline information and environmental information of the current segment by Fourier's Law, Newton's Law of Cooling and Stefan-Boltzmann Law. Combining the theoretical outlet temperature of the first pipeline segment with the theoretical outlet temperatures of other pipeline segments, the theoretical inlet temperature and theoretical outlet temperature of each pipeline segment can be obtained.

[0043] S2: Based on the theoretical inlet temperature and the theoretical outlet temperature of each pipeline segment, the theoretical temperature difference of each pipeline segment is calculated. For example, the theoretical temperature difference = |theoretical outlet temperature - theoretical inlet temperature|.

[0044] S3: Based on the environmental information, determine the measured inlet temperature and the measured outlet temperature of each pipeline segment. As mentioned above, the environmental information includes the outside temperature, and the dividing point of the pipeline segment is the position where the flow meter and the temperature sensor are both provided in the gas pipeline, so the measured inlet temperature and the measured outlet temperature of each pipeline segment can be obtained based on the environmental information.

[0045] S4: Based on the measured inlet temperature and the measured outlet temperature of each pipeline segment, the actual temperature difference of each pipeline segment is calculated. For example, the actual temperature difference = |measured outlet temperature - measured inlet temperature|.

[0046] S5: Based on the theoretical temperature difference and the actual temperature difference, determine the temperature impact of each pipeline segment. For example, temperature impact = [theoretical temperature difference + actual temperature difference] / 2.

[0047] In some embodiments, the gas company management platform can send the temperature impact of the gas pipeline to the government safety supervision management platform based on the sensor network platform.

[0048] In some embodiments, the gas company management platform can control the operation of the air compressor based on the delivery parameters to adjust the gas delivery temperature. Just as an example, if the gas temperature of the current delivery station is higher than the gas delivery temperature in the delivery parameters, the gas company management platform can control the air compressor to refrigerate and reduce the gas temperature of the current delivery station so that the delivery station delivers gas according to the delivery parameters. Among them, the temperature reduction range can be the difference between the gas temperature of the current delivery station and the gas delivery temperature in the delivery parameters. For example, if the gas delivery temperature in the delivery parameters is 20°C and the gas temperature of the current delivery station is 25°C, the temperature reduction range is 5°C. Among them, the air compressor is also called an air compressor, which mainly uses the compression and expansion of air to achieve the refrigeration effect. In some embodiments, the air compressor can be set in each delivery station and communicated with the gas company management platform so that the gas delivery temperature can be adjusted in time under the control of the gas company management platform.

[0049] In some embodiments of this specification, the gas company management platform can realize the real-time supervision of the gas company and gas pipelines by the government safety supervision management platform through iterative interaction with the government safety supervision management platform. In addition, since the gas temperature in the gas pipeline will change during the gas transportation process, it is beneficial for the government safety supervision management platform to determine more accurate transportation parameters by considering the temperature impact of the gas pipeline and sending it to the government safety supervision management platform. The gas company management platform then controls the air compressor to adjust the gas transportation temperature based on the transportation parameters, which can effectively avoid gas pipeline safety problems caused by excessively high or low temperatures, and is also beneficial to ensuring gas transportation efficiency.

[0050] Figure 4 FIG. 1 is an exemplary flow chart of a method for determining a delivery parameter according to some embodiments of the present specification. Figure 4 As shown, the process 400 may include the following steps. In some embodiments, the process 400 may be executed by a government security supervision management platform.

[0051] Step S410, based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information and the basic sensing data, determine the deformation assessment of the gas pipeline. In some embodiments, step 410 can be performed by the deformation assessment determination module 230. For more information about temperature impact, candidate parameters, pipeline information and basic sensing data, please refer to Figure 3 and its related description.

[0052] The deformation assessment of the gas pipeline refers to the data on possible deformation of different positions of the gas pipeline due to excessively high or low temperatures. In some embodiments, the deformation assessment of the gas pipeline may include at least one of the deformation amount and deformation probability of the gas pipeline. The deformation probability refers to the probability that the deformation amount exceeds the deformation threshold. Among them, the deformation threshold refers to the maximum value that the deformation amount can reach, and the deformation threshold can be determined based on historical data, etc. In some embodiments, the deformation amount and the deformation probability can be converted into each other. For example, the deformation probability can be the ratio of the deformation amount to the deformation threshold. For another example, the deformation amount can be the product of the deformation probability and the deformation threshold. As mentioned above, the gas pipeline includes multiple pipeline segments, so the deformation assessment of the gas pipeline includes the deformation assessment of each pipeline segment in the multiple pipeline segments.

[0053] In some embodiments, the deformation assessment of the gas pipeline can be represented by an array. For example, the deformation assessment can be represented as ((a1, b1, d11), (a1, b2, d12), ..., (a2, b1, d21), ...). Wherein, a1 and a2 represent pipeline segment 1 and pipeline segment 2 respectively; b1 and b2 represent candidate parameter 1 and candidate parameter 2 respectively; d11 represents the deformation amount (or deformation probability) of pipeline segment 1 under candidate parameter 1, d12 represents the deformation amount (or deformation probability) of pipeline segment 1 under candidate parameter 2, and d21 represents the deformation amount (or deformation probability) of pipeline segment 2 under candidate parameter 1. For example only, if d11 is +1mm or -0.5mm, it means that the deformation amount of pipeline segment 1 under candidate parameter 1 is +1mm or -0.5mm, that is, the gas may cause the pipe diameter of pipeline segment 1 to increase by 1mm or shrink by 0.5mm. If d11 is 90%, it means that the deformation probability of pipeline segment 1 under candidate parameter 1 is 90%.

[0054] In some embodiments, the government safety supervision management platform can determine the deformation assessment of the gas pipeline in a variety of ways based on the temperature impact of the gas pipeline, candidate parameters, pipeline information and basic sensing data. For example: S411: Based on the temperature impact of the gas pipeline and the pipeline information, calculate the pipeline expansion or pipeline contraction caused by the temperature change of each pipeline segment. An exemplary calculation formula may include: .in, The amount by which the diameter of a pipe segment increases or contracts due to temperature changes; is the thermal expansion coefficient of the pipeline segment material; is the original diameter of the pipe segment at the initial temperature; For temperature changes.

[0055] S412: Determine the measured inlet flow and measured outlet flow of each pipeline segment based on the basic sensing data. As mentioned above, the basic sensing data includes flow information, and the segmentation point of the pipeline segment is the location where the flow meter is set in the gas pipeline, so the measured inlet flow and measured outlet flow of each pipeline segment can be obtained based on the basic sensing data.

[0056] S413: Based on the measured inlet flow and the measured outlet flow of each pipeline segment, the flow change of each pipeline segment is calculated. For example, flow change=|measured outlet flow-measured inlet flow|.

[0057] S414: Based on the flow change of each pipeline segment, pipeline information and environmental information in the basic sensing data, according to the geometric characteristics of the pipeline segment and fluid mechanics related formulas, calculate the pressure change of each pipeline segment caused by the flow change.

[0058] S415: Based on the above pressure change, according to the thin-walled cylinder strength calculation formula, the deformation of each pipeline segment caused by the above pressure change is calculated.

[0059] S416: Based on the expansion or contraction of each pipeline segment due to temperature change and the deformation due to pressure change, the deformation amount (i.e., deformation evaluation) of each pipeline segment is obtained by weighted summation. It should be noted that the weight coefficient when weighted summing the two can be determined based on past experience or other methods.

[0060] For more information on how to determine the deformation assessment of gas pipelines, please refer to Figure 6 and related instructions.

[0061] Step S420 , updating the candidate parameters based on the deformation assessment of the gas pipeline to obtain updated candidate parameters. In some embodiments, step 420 may be performed by the candidate parameter updating module 240 .

[0062] The updated candidate parameters refer to the candidate parameters adjusted according to the deformation assessment of the gas pipeline. In some embodiments, the government safety supervision management platform can update the candidate parameters through preset rules based on the deformation assessment corresponding to the candidate parameters to obtain updated candidate parameters. Among them, the preset rules may include reducing the gas delivery temperature in the candidate parameters by a preset reduction amplitude in response to the deformation assessment exceeding the deformation threshold. Among them, the preset reduction amplitude can be set manually in advance or determined based on the first preset table. For example, if the mean of the deformation assessment (such as the deformation amount) of multiple gas pipeline segments exceeds 2 times the deformation threshold, the gas delivery temperature in the candidate parameters is reduced by 5°C. For another example, if the mean of the deformation assessment (such as the deformation amount) of multiple gas pipeline segments is between 1-2 times the deformation threshold, the gas delivery temperature in the candidate parameters is reduced by 2°C. In some embodiments, the first preset table can be constructed based on historical data.

[0063] For more information on how to update candidate parameters based on deformation assessment of gas pipelines, see Fig. 9 and related instructions.

[0064] Step S430 , in response to the deformation assessment of the gas pipeline satisfying a preset deformation condition, determining the updated candidate parameter as the delivery parameter. In some embodiments, step 430 may be performed by the delivery parameter determination module 250 .

[0065] The preset deformation condition refers to a pre-set judgment condition. In some embodiments, the preset deformation condition may include that the average (or maximum) value of the deformation evaluation of multiple pipeline segments is less than a preset value, etc. The preset value may be manually set in advance.

[0066] In some embodiments, in response to the mean (or maximum value) of the deformation assessment of multiple pipeline segments being less than a preset value, the government safety supervision management platform can determine the candidate parameter corresponding to the minimum mean (or maximum value) of the deformation assessment of multiple pipeline segments as the transportation parameter. Just as an example, if the pipeline segment is 100 segments and the candidate parameters are 10, then the deformation assessment is 1000 items. Among them, 100 deformation assessments are deformation assessments of different pipeline segments under candidate parameter 1, 100 deformation assessments are deformation assessments of different pipeline segments under candidate parameter 2, ..., and 100 deformation assessments are deformation assessments of different pipeline segments under candidate parameter 10. When determining the transportation parameter based on these 10 candidate parameters, for each candidate parameter, the mean (or maximum value) of the corresponding 100 deformation assessments is calculated, and the candidate parameter corresponding to the minimum mean (or maximum value) of the 100 deformation assessments (such as candidate parameter 2) is selected as the transportation parameter.

[0067] In some embodiments, in response to the deformation assessment of the gas pipeline not meeting the preset deformation condition (i.e., when the mean (or maximum) of the deformation assessment of multiple pipeline segments exceeds the preset value), the government safety supervision management platform can reduce the gas transmission temperature in the candidate parameters based on the first amplitude, and synchronously update the deformation assessment of the gas pipeline based on the reduced candidate parameters until the deformation assessment of the gas pipeline meets the preset deformation condition. At this time, the transmission parameter is the candidate parameter corresponding to the deformation assessment of the gas pipeline meeting the preset deformation condition. Among them, the first amplitude can be a fixed value. For more information about the first amplitude, please refer to Fig. 9 and its related description.

[0068] In some embodiments of this specification, based on the temperature influence of the gas pipeline, candidate parameters, pipeline information and basic sensing data, the deformation assessment of the gas pipeline is determined, and the possibility of causing the gas pipeline to deform is analyzed from multiple angles to obtain more comprehensive deformation assessment analysis data of the gas pipeline. Further, based on the deformation assessment, the candidate parameters are updated until the deformation assessment meets the preset deformation conditions, and the updated candidate parameters are determined as the transmission parameters. By adjusting the gas transmission parameters within the allowable deformation amount or deformation probability range of the gas pipeline, not only the danger caused by severe deformation of the gas pipeline is effectively avoided, but also the service life of the gas pipeline is extended and the maintenance or replacement cost is reduced.

[0069] Figure 5 is an exemplary schematic diagram of a first model according to some embodiments of the present specification.

[0070] In some embodiments, the gas pipeline includes a plurality of pipeline segments, and the first model includes a plurality of temperature layers, each of which is configured to determine the temperature impact and outlet data of one of the plurality of pipeline segments. Pipeline segmentation refers to a plurality of pipeline segments into which the gas pipeline is divided according to a cutting point. In some embodiments, the temperature impact determination module 220 can segment the gas pipeline based on pipeline length, delivery station, etc. For example, the gas pipeline is segmented with a preset pipeline length. The preset pipeline length can be determined based on past experience or historical data, etc. For more information on how to segment the gas pipeline, please refer to the relevant description in other parts of this specification.

[0071] The temperature layer can be used to determine the temperature impact and outlet data of the pipeline segment. In some embodiments, one temperature layer corresponds to one pipeline segment, and one temperature layer can be used to determine the temperature impact and outlet data of one corresponding pipeline segment. In some embodiments, each temperature layer is a neural network (NN) model.

[0072] The outlet data refers to the relevant data at the outlet of the pipeline segment. In some embodiments, the outlet data may include at least one of the outlet temperature and the outlet rate. In some embodiments, each pipeline segment has its own outlet data, and the outlet data of the previous segment is the inlet data of the current segment. For more information about the previous segment and the current segment, please refer to Figure 3 and its related description. Inlet data refers to relevant data at the inlet of the pipeline segment. In some embodiments, the inlet data may include at least one of the inlet temperature and the inlet rate. In some embodiments, the input of each temperature layer may include the inlet data, pipeline information and environmental information of the corresponding pipeline segment, and the output of each temperature layer may include the temperature impact and outlet data of the corresponding pipeline segment.

[0073] like Figure 5 As shown, for the first temperature layer (i.e., the temperature layer 510-1 corresponding to the first pipeline segment), its input may include the inlet data (i.e., candidate parameters 330), the first pipeline information (310-1), and the first environmental information (321-1) of the first pipeline segment, and its output may include the first temperature impact (520-1) and the first outlet data (530-1) of the first pipeline segment. For the Nth temperature layer (i.e., the temperature layer 510-n corresponding to other pipeline segments), its input may include the N-1th temperature impact (520-(n-1)) and the N-1th outlet data (530-(n-1)) of the previous segment, the Nth pipeline information (310-n) and the Nth environmental information (321-n) of the current segment, and its output may include the Nth temperature impact (520-n) and the Nth outlet data (530-n) of the current segment.

[0074] It is worth noting that the subsequent segments are calculated iteratively between multiple temperature layers according to the gas flow direction. For example, the output of the temperature layer of the previous segment is used as the input of the temperature layer of the subsequent segment. However, when a sensor device (such as a temperature sensor or a flow meter) is set at the inlet position of the subsequent segment, the data obtained by the sensor device in real time (such as the gas delivery temperature and the gas delivery rate) needs to be used as the inlet data.

[0075] In some embodiments, the output of the first model may be a sequence of the output data and temperature effects of all temperature layers, for example, ((first output data, first temperature effect), (second output data, second temperature effect), ..., (Nth output data, Nth temperature effect)).

[0076] In some embodiments, the first temperature layer can be obtained through training based on a large number of first training samples with first sample labels. For example, multiple labeled training samples can be input into the initial first temperature layer, and a loss function can be constructed by using the labels and the results of the initial first temperature layer. The parameters of the initial first temperature layer can be iteratively updated based on the loss function by gradient descent or other methods. When the preset conditions are met, the first temperature layer training is completed, and the trained first temperature layer is obtained. The preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0077] In some embodiments, the first training sample may include sample parameters, sample pipeline information of the first pipeline segment, and sample environmental information. The first sample label may be the actual temperature impact and actual outlet data of the first pipeline segment under the sample parameters, the sample pipeline information of the first pipeline segment, and the sample environmental information. The actual temperature impact of the first pipeline segment is the difference between the measured outlet temperature and the measured inlet temperature of the first pipeline segment. In some embodiments, the first training sample and the first sample label may be obtained based on actual data.

[0078] In some embodiments, the Nth temperature layer can be obtained by training based on a large number of second training samples with second sample labels, using a training method similar to the first temperature layer, and the similarities are not repeated here. In some embodiments, the second training sample can include the sample outlet data and sample temperature influence of the previous segment, the sample pipeline information and sample environment information of the current segment. The second sample label can be the actual temperature influence and actual outlet data of the current segment under the sample outlet data and sample temperature influence of the previous segment, the sample pipeline information and sample environment information of the current segment. In some embodiments, the second training sample and the second sample label can be obtained based on actual data.

[0079] In some embodiments of the present specification, the temperature impact and outlet data of different pipeline segments can be obtained more accurately and quickly through the trained first model, so as to lay a foundation for the subsequent accurate determination of deformation assessment.

[0080] In some embodiments, since changes in gas pipeline diameter, pipeline bifurcations and pipeline intersections may affect the gas transmission rate, the cutting points of the pipeline segments may also include locations where the gas pipeline diameter changes, pipeline bifurcations and pipeline intersections.

[0081] In some embodiments, the temperature impact determination module 220 may determine the inlet rate of the current segment based on the outlet rate and pipe diameter of the previous segment. For example, the temperature impact determination module 220 may calculate the inlet rate of the current segment based on the outlet rate and pipe diameter of the previous segment by a fluid mechanics method.

[0082] In some embodiments of the present specification, the dividing points of the gas pipeline are determined based on the diameter change position, pipeline bifurcation position and pipeline intersection position of the gas pipeline, so that the diameter of each pipeline segment can be made the same as much as possible, which is conducive to further improving the prediction accuracy of the first model.

[0083] In some embodiments, the deformation assessment determination module 230 can determine the deformation assessment of the gas pipeline through the second model based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information and the basic sensing data. For more information about the deformation assessment of the gas pipeline, please refer to Figure 4 and its related description.

[0084] In some embodiments, the second model is a machine learning model. For example, the second model may include any one or a combination of various feasible models such as a recurrent neural network (RNN) model and a deep neural network (DNN) model.

[0085] In some embodiments, the second model may include multiple deformation layers, each deformation layer is configured to determine a deformation assessment of a pipeline segment among multiple pipeline segments. The deformation layer can be used to determine the deformation assessment of the pipeline segment. In some embodiments, one deformation layer corresponds to one pipeline segment, and one deformation layer can be used to determine the deformation assessment of a corresponding pipeline segment. In some embodiments, each deformation layer is a neural network (NN) model. In some embodiments, the input of each deformation layer may include candidate parameters, pipeline information of the corresponding pipeline segment, and environmental information, and the output of each deformation layer may include the deformation assessment of the corresponding pipeline segment.

[0086] Figure 6 is an exemplary schematic diagram of a second model according to some embodiments of this specification. Figure 6 As shown, for the first deformation layer (i.e., the deformation layer 610-1 corresponding to the first pipeline segment), its input may include the entry data of the first pipeline segment (i.e., the candidate parameter 330), the first pipeline information (310-1), and the first environmental information (321-1), and its output may include the first deformation evaluation (620-1) of the first pipeline segment. For the Nth deformation layer (i.e., the deformation layer 610-n corresponding to other pipeline segments), its input may include the N-1th deformation evaluation (620-(n-1)) of the previous segment, the Nth pipeline information (310-n) of the current segment, the Nth environmental information (321-n), and the Nth temperature impact (520-n), and its output may include the Nth deformation evaluation (620-n) of the current segment.

[0087] The subsequent segments are iteratively calculated between multiple deformation layers according to the gas flow direction. For example, the output of the deformation layer of the previous segment is used as the input of the deformation layer of the subsequent segment. However, when a sensing device (such as a temperature sensor or a flow meter) is provided at the entrance of the subsequent segment, the data obtained by the sensing device in real time (such as the gas transmission temperature and the gas transmission rate) needs to be used as the entrance data. Moreover, since the bellows compensator can compensate for the temperature difference deformation or other deformation between the equipment and the gas pipeline, it has a great influence on the deformation assessment of the gas pipeline. Therefore, the pipeline information in the input of the deformation layer also needs to include the setting information of the bellows compensator to further improve the prediction accuracy of the second model. For more information about the setting information of the bellows compensator, please refer to Figure 3 and its related description.

[0088] In some embodiments, the output of the second model may be a sequence of deformation evaluations output by all deformation layers, for example, (first deformation evaluation, second deformation evaluation, ..., Nth deformation evaluation).

[0089] In some embodiments, the first deformation layer can be obtained by training based on a large number of third training samples with third sample labels using a training method similar to that of the first temperature layer. For more information about the training method of the first temperature layer, see Figure 5 and its related description.

[0090] In some embodiments, the third training sample may include sample candidate parameters, sample pipeline information, and sample environmental information of the first pipeline segment. In some embodiments, the third sample label may be an actual deformation assessment of the first pipeline segment under the sample candidate parameters, sample pipeline information, and sample environmental information of the first pipeline segment. In some embodiments, the third training sample may be acquired based on historical data. The third sample label may be determined based on the actual deformation situation. For example, deformation is marked as 1, and no deformation is marked as 0. For another example, the actual deformation situation is a deformation variable of +1mm, a deformation variable of +5mm, a deformation variable of +0mm, etc. In some embodiments, the actual deformation situation may be acquired based on historical pipeline leakage information and historical deformation data. Among them, historical deformation data refers to pipeline deformation conditions discovered during pipeline maintenance and inspection. For more information about historical pipeline leakage information, please refer to Figure 7 and its related description.

[0091] In some embodiments, the Nth deformation layer can be obtained by training based on a large number of fourth training samples with fourth sample labels, using a training method similar to the first temperature layer. In some embodiments, the fourth training sample may include the sample deformation evaluation, sample pipeline information and sample environment information of the previous segment, as well as the sample temperature influence, sample pipeline information and sample environment information of the current segment. The fourth sample label may be the actual deformation evaluation of the current segment under the sample deformation evaluation, sample pipeline information and sample environment information of the previous segment and the sample temperature influence, sample pipeline information and sample environment information of the current segment. In some embodiments, the fourth sample label can be determined based on the influence of the pipeline information and environment information of the current segment and the pipeline information and environment information of the previous segment on the deformation evaluation. For example, if the pipe diameter of the previous segment is large and the pipe diameter of the current segment is small, the pressure of the current segment increases and the deformation increases. At this time, the deformation evaluation of the current segment is the sum of the deformation amount of the deformation evaluation of the previous segment and the amplitude of the deformation increase. For another example, if the temperature of the current segment drops (such as the temperature influence of the current segment or the external temperature of the current segment in the environmental information drops), the deformation of the current segment weakens. At this time, the deformation evaluation of the current segment is the difference between the deformation probability of the deformation evaluation of the previous segment and the probability of deformation weakening.

[0092] In some embodiments, the training of the second model may also include determining different training sample sets and their corresponding labels based on the distribution of delivery stations; and alternating training of different training sample sets according to size; wherein different training sample sets have different learning rates during the training process, and the learning rate is adjusted based on the characteristics of the training samples.

[0093] The distribution of the delivery stations can reflect the distribution of the delivery stations in the pipeline system. In some embodiments, different training sample sets can be divided according to the distribution of the delivery stations. For example, data of several delivery stations within a preset distance threshold can be used as a training sample set. The preset distance threshold can be manually set in advance.

[0094] Learning rate refers to the hyperparameter used to update weights during the gradient descent process in machine learning. The learning rate can control the convergence speed, ensure the stability and accuracy of the model, and affect the efficiency of model training. The lower the learning rate, the slower the loss function changes, and it is easy to overfit. If the learning rate is too high, gradient explosion is likely to occur, the loss oscillation amplitude is large, and the model is difficult to converge.

[0095] The training sample characteristics can reflect the characteristics of the training samples (including the third training sample and the fourth training sample) themselves. In some embodiments, the training sample characteristics may include the source of the training sample and the reliability of the training sample. Among them, the source of the training sample may include historical data of gas pipelines with different pressure levels. It can be understood that compared with gas pipelines with different pressure levels, gas pipelines with the same pressure level have a higher similarity. For example, the pipeline information, external temperature, pipeline maintenance status, etc. of high-pressure pipelines and low-pressure pipelines are usually different.

[0096] The reliability of training samples refers to the consistency of corresponding labels for the same or similar training samples. If multiple labels are consistent or mostly consistent, the reliability of the training samples is high and the training effect is good; if multiple labels are inconsistent (that is, for the same or similar training samples, the corresponding actual deformation assessments are different), the reliability of the training samples is low, resulting in poor training results. The similarity of training samples can be determined by a variety of methods, such as feature extraction and representation, similarity measurement, multimodal integration, clustering and classification.

[0097] In some embodiments, the deformation assessment determination module 230 can determine the learning rates of different training sample sets during the training process by vector matching based on the training sample features. For example, the first vector database may include multiple candidate vectors and their corresponding candidate learning rates. Among them, the candidate vectors can be constructed based on the training sample source and the training sample reliability. The candidate learning rate corresponding to the candidate vector can be determined based on the actual learning rate corresponding to the training sample source and the training sample reliability.

[0098] In some embodiments, the deformation evaluation determination module 230 can construct a vector to be matched based on the current training sample source and the training sample reliability, and respectively calculate the distance between the vector to be matched and multiple candidate vectors, determine the candidate vector whose distance to the vector to be matched is less than a distance threshold as the target vector, and determine the candidate learning rate corresponding to the target vector as the current learning rate. The distance threshold can be manually set in advance based on past experience.

[0099] In some embodiments of this specification, the deformation assessment of different pipeline segments can be obtained more accurately and quickly through the trained second model. Based on the distribution of transmission stations in the gas pipeline, different training sample sets and their corresponding labels are trained alternately according to the size, so that deformation assessment results can be obtained quickly and accurately. At the same time, the learning rate is determined according to the characteristics of the training samples, which can effectively reduce the training time and improve the stability of model training.

[0100] Figure 7 FIG. 1 is an exemplary schematic diagram of a method for adjusting the deformation assessment of each pipeline segment shown in some embodiments of the present specification. Figure 7As shown, the deformation assessment adjustment module 270 can determine the first confidence 730 of each pipeline segment for each pipeline segment in the plurality of pipeline segments based on the pipeline information 310, the environmental information 321, the transport parameters 370, the inlet data 710 of each pipeline segment, and the historical pipeline leakage information 720; and adjust the deformation assessment 740 of each pipeline segment based on the first confidence 730 of each pipeline segment. For more information about the pipeline information, the environmental information, the transport parameters, the pipeline segments, and the inlet data of the pipeline segments, please refer to the relevant description in the above text.

[0101] The historical pipeline leakage information refers to relevant information of pipeline leakage in the past. For example, the historical pipeline leakage information may include the location of historical pipeline leakage, etc. In some embodiments, the deformation assessment adjustment module 270 may obtain the historical pipeline leakage information based on historical data.

[0102] The first confidence level refers to one of the indicators used to evaluate the adjustment amplitude or adjustment range of the deformation assessment. In some embodiments, the deformation assessment adjustment module 270 can determine the first confidence level based on the similarity between the deformation assessment (such as deformation probability) of the current segment output by the second model and the target deformation probability. For example, the higher the similarity, the higher the first confidence level. In some embodiments, the target deformation probability can be determined in a variety of ways. For example, the deformation assessment adjustment module 270 can construct a second vector database based on historical data, and retrieve the corresponding target deformation probability based on the matching vector. Among them, the second vector database may include multiple reference vectors and their corresponding reference deformation probabilities. The reference vector can be constructed based on the transportation parameters, pipeline information and environmental information of the current segment, as well as historical pipeline leakage information. The reference deformation probability corresponding to the reference vector can be the actual deformation probability corresponding to the transportation parameters, pipeline information and environmental information of the current segment, as well as historical pipeline leakage information.

[0103] In some embodiments, the deformation assessment adjustment module 270 can construct a vector to be matched based on the inlet data (gas delivery temperature, gas delivery rate) of the current segment, pipeline information and environmental information, and respectively calculate the distance between the vector to be matched and multiple reference vectors, and determine the reference vector whose distance to the vector to be matched is less than the distance threshold as the target vector, and determine the reference deformation probability corresponding to the target vector as the current target deformation probability. Among them, the distance threshold can be set manually in advance based on past experience. In some embodiments, the reference deformation probability can be marked according to historical pipeline leakage information. For example, if the current segment leaks multiple times (such as more than 5 times), the reference deformation probability is 1, the leakage number is in the middle (such as no more than 3 times), the reference deformation probability is 0.5, and if there has been no leakage, the reference deformation probability is 0.

[0104] In some embodiments, the deformation assessment adjustment module 270 may determine a first magnification factor corresponding to each pipeline segment based on a first confidence level of each pipeline segment; and adjust the deformation assessment of each pipeline segment based on the first magnification factor.

[0105] The first magnification factor refers to a factor used to adjust the deformation assessment. The first magnification factor corresponding to each pipeline segment may be the same or different. In some embodiments, the first magnification factor corresponding to each pipeline segment may be negatively correlated with the first confidence level of each pipeline segment. For example, the smaller the first confidence level, the larger the corresponding first magnification factor.

[0106] In some embodiments, the deformation assessment adjustment module 270 can calculate the adjusted deformation assessment of each pipeline segment based on the first magnification coefficient corresponding to each pipeline segment, so as to adjust the deformation assessment of each pipeline segment. For example, the adjusted deformation assessment = the current deformation assessment × the corresponding first magnification coefficient. For more information on how to adjust the deformation assessment of each pipeline segment, please refer to Figure 8 and its related description.

[0107] In some embodiments of the present specification, a first confidence level of each pipeline segment is determined, and based on the deformation assessment output by the second model, the deformation assessment of each pipeline segment is adjusted to obtain a more accurate deformation probability.

[0108] In some embodiments, a plurality of checkpoints are provided in the gas pipeline. A checkpoint refers to a location point provided on the gas pipeline for temperature inspection. In some embodiments, the number of checkpoints may be a fixed value. In some embodiments, the location of the checkpoints may be randomly generated. It should be noted that the number and location of the checkpoints may also be determined in any other feasible manner. For more information on how to determine the number and location of the checkpoints, please refer to Fig.10 and its related description.

[0109] In some embodiments, the deformation assessment adjustment module 270 may adjust the deformation assessment of each pipeline segment through the steps described in the process 800 based on the first confidence level of each pipeline segment.

[0110] Figure 8 FIG. 1 is an exemplary flow chart of a method for adjusting the deformation assessment of each pipeline segment according to other embodiments of the present specification. Figure 8 As shown, the process 800 may include the following steps. In some embodiments, the process 800 may be executed by the deformation evaluation adjustment module 270 .

[0111] Step S810, obtaining actual temperatures of multiple inspection points.

[0112] In some embodiments, the deformation assessment and adjustment module 270 can obtain the actual temperature of multiple checkpoints through a sensor device. For example, the manager can set multiple temperature sensors at multiple checkpoints, and each temperature sensor can obtain the actual temperature of each checkpoint in real time and upload it to the deformation assessment and adjustment module 270. In some embodiments, the temperature sensor can be integrated into a mobile measuring instrument, and the deformation assessment and adjustment module 270 can control the mobile measuring instrument to move to each checkpoint in turn to obtain the actual temperature of multiple checkpoints.

[0113] Step S820: determining a second confidence level of each pipeline segment based on actual temperatures of multiple inspection points and historical predictions.

[0114] Historical prediction refers to the predicted value of the temperature impact corresponding to the transportation parameters reported by the transportation station before the current transportation plan is determined. In other words, it is the temperature impact of the first model output corresponding to the transportation parameters when the deformation assessment meets the preset conditions. For more information about the temperature impact of the first model output, please refer to Figure 5 and its related description.

[0115] The second confidence level refers to another indicator for evaluating the adjustment amplitude or adjustment range of the deformation assessment. In some embodiments, the second confidence level of each pipeline segment can be expressed based on the temperature difference of the checkpoints on each pipeline segment. In some embodiments, the deformation assessment adjustment module 270 can determine the second confidence level of each pipeline segment (i.e., the temperature difference of the checkpoints on each pipeline segment) based on the actual temperatures of multiple checkpoints and historical predictions in the following manner.

[0116] S821: Determine the pipeline segment where each checkpoint is located and its position on the corresponding pipeline segment. The position of each checkpoint in the pipeline segment can be represented by the distance between each checkpoint and the entrance or exit of the pipeline segment where it is located. In some embodiments, the pipeline segment where each checkpoint is located and its position on the corresponding pipeline segment can be obtained by input by a management personnel.

[0117] S822: Obtain the inlet temperature of the pipeline segment where each checkpoint is located and the outlet temperature of the corresponding pipeline segment output by the first model (hereinafter referred to as the predicted outlet temperature), and determine the estimated temperature of each checkpoint through linear difference fitting. The inlet temperature of the pipeline segment where each checkpoint is located is the outlet temperature of the previous pipeline segment of the corresponding pipeline segment, which is also obtained by the first model.

[0118] S823: Based on the actual temperature and the estimated temperature of each inspection point, a temperature difference of each inspection point is obtained by calculation, thereby obtaining a second confidence level of each pipeline segment. An exemplary calculation formula may include temperature difference=|actual temperature-estimated temperature|.

[0119] It should be noted that, for a pipeline segment without a checkpoint, its second confidence can be obtained based on the nearest checkpoint. For example, the second confidence of a pipeline segment without a checkpoint = the temperature difference from the nearest checkpoint / the distance between the checkpoint and the entrance or exit of the pipeline segment.

[0120] Step S830: adjusting the deformation assessment of each pipeline segment based on the first confidence level and the second confidence level.

[0121] In some embodiments, the deformation assessment adjustment module 270 may determine a second magnification factor corresponding to each pipeline segment based on the first confidence level and the second confidence level; and adjust the deformation assessment of each pipeline segment based on the second magnification factor.

[0122] In some embodiments, the second amplification factor corresponding to each pipeline segment may be negatively correlated with the weighted sum of the first confidence and the second confidence of each pipeline segment. For example, the smaller the weighted sum of the first confidence and the second confidence, the larger the corresponding second amplification factor. The weighted sum of the first confidence and the second confidence refers to the value obtained by weighted summing the first confidence and the second confidence. Since the second confidence is related to the actual temperature of the checkpoint on the pipeline segment, the reliability of the second confidence is higher than that of the first confidence. Therefore, when weighted summing, the weight coefficient of the second confidence is greater than the weight coefficient of the first confidence. For ease of calculation, the first confidence and the second confidence need to be normalized.

[0123] In some embodiments, the deformation assessment adjustment module 270 may calculate the adjusted deformation assessment of each pipeline segment based on the second magnification coefficient corresponding to each pipeline segment, so as to adjust the deformation assessment of each pipeline segment. For example, the adjusted deformation assessment = the current deformation assessment × the corresponding second magnification coefficient.

[0124] In some embodiments of the present specification, a second confidence level of each pipeline segment is determined based on actual temperatures and historical predictions of multiple checkpoints, and a magnification factor is determined based on a weighted sum of the first confidence level and the second confidence level to adjust the deformation assessment of each pipeline segment to obtain a more accurate deformation assessment.

[0125] Fig. 9 is an exemplary flow chart of a method for updating candidate parameters according to some embodiments of this specification. Fig. 9 As shown, the process 900 may include the following steps. In some embodiments, the process 900 may be executed by the candidate parameter updating module 240 .

[0126] Step S910: determining a first amplitude of the candidate parameter based on the deformation evaluation corresponding to the candidate parameter.

[0127] The deformation evaluation corresponding to the candidate parameter refers to the deformation evaluation of each pipeline segment corresponding to a certain candidate parameter as the transport parameter. In some embodiments, the deformation evaluation corresponding to a candidate parameter can be the same or different. For more information about candidate parameters and deformation evaluation, please refer to Figure 4 And related instructions. In some embodiments, the candidate parameter update module 240 can determine the deformation evaluation corresponding to the candidate parameter through a second preset table. Among them, the second preset table can be used to characterize the correlation between the candidate parameter and the deformation evaluation of the pipeline segment. Each candidate parameter has a corresponding deformation evaluation under different pipeline segments. In some embodiments, the second preset table can be constructed based on the historical candidate parameters and the historical deformation evaluation of each pipeline segment.

[0128] The first amplitude of the candidate parameter refers to the adjustment amplitude of the candidate parameter determined according to the deformation evaluation corresponding to the candidate parameter. In some embodiments, the candidate parameter updating module 240 can determine the first amplitude of the candidate parameter by the positive correlation principle based on the deformation evaluation corresponding to at least one candidate parameter. For example, the larger the deformation evaluation corresponding to the candidate parameter, the larger the first amplitude of the corresponding candidate parameter.

[0129] Step S920: determining a second amplitude of the candidate parameter based on the deformation evaluation and the consistency of the gas flow direction.

[0130] Gas flow direction refers to the direction in which gas flows in a gas pipeline. For example, the gas flow direction is from a starting point (such as a gas source) to an end point (such as a user end). The consistency of deformation assessment and gas flow direction refers to: for the same candidate parameter, the degree of consistency between the deformation degree of the pipeline segment caused by it and the distribution of the deformation degree along the gas flow direction. For example, if a change in a candidate parameter causes the same or similar deformation degree to appear in multiple pipeline segments on a gas pipeline, and these pipeline segments are close to or continuous in physical position, then it can be considered that the deformation assessment corresponding to the candidate parameter is highly consistent with the gas flow direction. In some embodiments, the candidate parameter update module 240 can determine the consistency of the deformation assessment and the gas flow direction through graphical analysis based on the deformation assessment and the gas flow direction corresponding to the candidate parameter. The specific steps are as follows: S921, establish a two-dimensional coordinate system, wherein the X-axis represents the number of the pipeline segment according to the gas flow direction, and the Y-axis represents the deformation assessment value corresponding to the pipeline segment. Each pipeline segment has a corresponding point in the two-dimensional coordinate system.

[0131] S922, draw a deformation curve. Draw the deformation evaluation values ​​of all pipeline segments corresponding to the candidate parameters in a two-dimensional coordinate system, and connect them in sequence to form a curve. This curve reflects the distribution of the deformation degree of each pipeline segment along the gas flow direction under the same candidate parameters.

[0132] S923, analyze the peak situation. When there is only one peak in the curve, and the pipeline segments corresponding to this peak are adjacent (such as pipeline segment 2, pipeline segment 3, pipeline segment 4), and their deformation evaluation values ​​are all the maximum value or close to the maximum value of the curve segment, then it can be considered that under this candidate parameter, the consistency between the deformation evaluation and the gas flow direction is high. This shows that the deformation is mainly concentrated on these adjacent pipeline segments. At this time, lowering the gas transmission temperature can solve the pipeline deformation problem. On the contrary, if there are multiple peaks in the curve (such as three peaks), and the pipeline segments corresponding to these peaks are far apart on the X-axis (such as pipeline segment 1, pipeline segment 50, pipeline segment 100), then it can be considered that under this candidate parameter, the consistency between the deformation evaluation and the gas flow direction is poor. This shows that there are more uncontrollable factors causing pipeline deformation. At this time, it is necessary to increase the reduction amplitude of the candidate parameters so that the deformation reduction effect can be achieved in multiple positions in the end.

[0133] In some embodiments, the candidate parameter updating module 240 can also screen out multiple pipeline segments with deformation evaluation values ​​ranked high (such as the top 10) based on the deformation evaluation and gas flow direction corresponding to the candidate parameters, and determine the consistency of the deformation evaluation and the gas flow direction by comparing the relative position relationship (such as the distance) between these pipeline segments. For example, if the total distance of multiple pipeline segments with deformation evaluation values ​​ranked high (such as the top 10) does not exceed the distance threshold, it can be considered that the consistency of the deformation evaluation and the gas flow direction is high. Among them, the distance threshold can be set manually in advance.

[0134] The second amplitude refers to the adjustment amplitude determined according to the consistency between the gas flow direction and the deformation evaluation corresponding to the candidate parameter. In some embodiments, the candidate parameter update module 240 can determine the second amplitude of the candidate parameter by the negative correlation principle based on the consistency between the deformation evaluation and the gas flow direction. For example, the higher the consistency between the deformation evaluation and the gas flow direction, the smaller the second amplitude of the candidate parameter. When analyzing the influence of a certain candidate parameter on pipeline deformation, if it is found that the deformation evaluation results show a high degree of consistency in the pipeline segments along the gas flow direction, it indicates that the influence of the candidate parameter on the pipeline deformation is local and concentrated. At this time, in order to control or reduce such deformation, it is necessary to adjust the candidate parameter by a smaller amplitude. On the contrary, if the deformation evaluation results show a lower consistency, it indicates that the influence of the candidate parameter on the pipeline deformation may be more complex and dispersed. At this time, it is necessary to expand the second amplitude to ensure that multiple deformation points can be effectively controlled.

[0135] Step S930: updating the candidate parameters based on the first amplitude and the second amplitude.

[0136] In some embodiments, the candidate parameter updating module 240 may calculate the maximum or average value of the first amplitude and the second amplitude and use it as the final adjustment amplitude (such as the temperature reduction amplitude), and then adjust or update the current candidate parameter based on the final adjustment amplitude. As an example only, the updated candidate parameter = the current candidate parameter - |final adjustment amplitude|.

[0137] In some embodiments of the present specification, the candidate parameter updating module can comprehensively consider the impact of the candidate parameter's own change on the pipeline deformation and the consistency between the change and the gas flow direction by determining the first amplitude and the second amplitude of the candidate parameter respectively. This dual consideration can make the adjustment more precise, and can more accurately reflect the actual situation, reducing unnecessary adjustment errors.

[0138] In some embodiments, the gas pipeline is provided with multiple checkpoints, and the multiple checkpoints correspond to at least one delivery station. A delivery station refers to a station in the gas pipeline that is responsible for multiple functions such as gas transmission, pressurization, distribution, metering, and monitoring. In some embodiments, one or more delivery stations may be provided on the gas pipeline, wherein one delivery station may correspond to one or more checkpoints. For more information about the delivery station, please refer to the previous article (such as Figure 3 ) in the relevant description. In some embodiments, the number of checkpoints is related to the deformation assessment output by the second model. The government safety supervision management platform can quantify and identify those pipeline segments whose deformation degree exceeds the preset safety threshold by performing statistical analysis on the deformation assessment results output by the second model. In some embodiments, there is a significant positive correlation between the number of checkpoints and the number of pipeline segments that exceed the safety threshold. That is, the more pipeline segments that exceed the safety threshold, the more checkpoints there are. For more information about the second model and deformation assessment, please refer to the previous text (such as Figure 4 , Figure 6 ) in the relevant description.

[0139] In some embodiments of this specification, the deformation assessment of the gas pipeline is determined based on the second model, and the weak links or high-risk pipeline sections that may exist in the gas pipeline can be identified. Based on these assessment results, the system can dynamically adjust the number of checkpoints, increase the number of checkpoints in pipeline sections with large deformation or high risk, and appropriately reduce the number of checkpoints in relatively stable pipeline sections. This can not only ensure close monitoring of key pipeline sections, but also effectively avoid waste of resources and improve the pertinence and monitoring effect of monitoring.

[0140] In some embodiments, the number of checkpoints is also related to the temperature impact of the gas pipeline. In some embodiments, there is a significant positive correlation between the number of checkpoints and the temperature impact value. That is, the larger the temperature impact value, the more checkpoints there are.

[0141] In some embodiments of this specification, by combining the number of checkpoints with the temperature impact of the gas pipeline, the monitoring system can more accurately capture the changes in the gas pipeline under different temperature conditions. Adding checkpoints in areas with large temperature fluctuations can more frequently monitor the impact of temperature on pipeline materials, stress states, etc., thereby discovering potential safety hazards earlier.

[0142] In some embodiments, the number of checkpoints is also related to the number of pipeline segments. In some embodiments, the more gas pipeline segments there are, the more checkpoints should be set to ensure the accuracy and comprehensiveness of the inspection.

[0143] In some embodiments of this specification, by dividing the gas pipeline into multiple pipeline segments and determining the distribution of inspection points according to the number of pipeline segments, it can be ensured that each pipeline segment can be fully monitored. This helps to timely discover possible problems in each segment, such as leakage, deformation, etc., so as to take targeted measures to deal with them and prevent the problem from spreading.

[0144] In some embodiments, the location of the checkpoint is related to the temperature impact of the gas pipeline. In some embodiments, the location of the checkpoint needs to cover at least the pipeline segments with higher temperature impact values. For example, in order to determine the best location for the checkpoint, the government safety supervision management platform can sort the pipeline segments based on temperature impact, specifically involving temperature impact parameters m11, m12, ..., m21, etc. Select the pipeline segments with the highest temperature impact ranking (such as the top 5%) among these pipeline segments, and set the checkpoints on these pipeline segments. If it is not feasible to set them directly on the pipeline segments, select the checkpoint location closest to these pipeline segments to ensure that the checkpoints can cover the minimum number of pipeline segments and can effectively monitor the pipeline segments with the most significant temperature impact.

[0145] In some embodiments of this specification, by setting the location of the checkpoint in an area that is greatly affected by temperature, such as a location with a large temperature gradient, a pipe joint, an elbow, etc., the impact of temperature on pipe performance can be captured more accurately. This helps to promptly detect potential problems such as pipe deformation and leakage caused by temperature changes, and improves the accuracy of monitoring. For more information about the checkpoint, please refer to the previous article (such as Figure 8 ) in the relevant description.

[0146] Fig.10 is an exemplary flow chart of a method for adjusting a delivery parameter according to some embodiments of this specification. Fig.10 As shown, the process 1000 may include the following steps. In some embodiments, the process 1000 may be performed by the delivery parameter adjustment module 280 .

[0147] Step S1010, obtaining the actual temperatures of multiple checkpoints. For detailed instructions on how to obtain the actual temperatures of multiple checkpoints, please refer to Figure 8 and related descriptions.

[0148] Step S1020, based on the actual temperatures of the multiple checkpoints and the historical predictions, determine the confidence level of at least one delivery station corresponding to the multiple checkpoints.

[0149] The confidence of the delivery station is used to evaluate the adjustment amplitude or adjustment range of the delivery parameters of the delivery station. In some embodiments, the confidence of the delivery station can be represented by a vector composed of the temperature difference of one or more checkpoints corresponding to the delivery station. As mentioned above, a delivery station can control one or more pipeline segments, so when the delivery station only controls one pipeline segment, the confidence of the delivery station is the same as the second confidence of the pipeline segment it controls. For more information on the actual temperature of multiple checkpoints and historical predictions, and the calculation method of the temperature difference of the checkpoints, please refer to Figure 8 and related descriptions.

[0150] Step S1030: Adjust the delivery parameters based on the confidence level of at least one delivery station. For more information about the delivery parameters, please refer to the previous article (such as Figure 3-Figure 4 ) in the relevant description.

[0151] In some embodiments, the delivery parameter adjustment module 280 can determine multiple checkpoints and their corresponding delivery stations whose temperature difference values ​​in the above confidence values ​​are within the temperature difference range based on the confidence value of at least one delivery station, and adjust the delivery parameters (such as gas delivery temperature) of the delivery stations corresponding to the above multiple checkpoints in the following manner. For example only, the adjusted gas delivery temperature = current gas delivery temperature + temperature difference Adjustment coefficient. Wherein, the adjustment coefficient is less than 1. In some embodiments, the temperature difference range and the adjustment coefficient can be determined based on past experience, historical data, simulation experiments, etc.

[0152] In some embodiments of this specification, by obtaining the actual temperature of multiple checkpoints in real time and comparing them with historical prediction data, the pipeline section with abnormal temperature can be quickly determined. Based on this abnormal information, the system can immediately calculate the confidence level of the corresponding delivery station and adjust the delivery parameters accordingly. This rapid response mechanism helps to correct deviations in pipeline operation in a timely manner and prevent problems from expanding.

[0153] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

Claims

1. A smart gas pipeline temperature control method based on the Internet of Things, implemented based on a smart gas pipeline temperature control system, characterized in that: The method comprises: Based on the gas company management platform, obtain the pipeline information of the gas pipeline and the basic perception data collected and uploaded by the gas equipment object platform, and obtain candidate parameters from the government safety supervision management platform, and determine the transmission parameters through at least one round of iterative interaction with the government safety supervision management platform; and Based on the delivery parameters, controlling the air compressor of the gas equipment object platform to adjust the gas delivery temperature; Among them, one round of iterative interaction includes: The gas company management platform determines the temperature impact of the gas pipeline through a first model based on the candidate parameters, the pipeline information and the environmental information; the first model is a machine learning model; Sending the temperature impact of the gas pipeline to the government safety supervision management platform; The government safety supervision management platform determines a deformation assessment of the gas pipeline based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information and the basic sensing data; updating the candidate parameters based on the deformation assessment of the gas pipeline to obtain updated candidate parameters; and, In response to the deformation assessment of the gas pipeline satisfying a preset deformation condition, the updated candidate parameter is determined as the delivery parameter.

2. The smart gas pipeline temperature control method based on the Internet of Things as claimed in claim 1 is characterized in that: The gas pipeline includes a plurality of pipeline segments, the first model includes a plurality of temperature layers, each of the temperature layers is configured to determine temperature impact and outlet data for a pipeline segment of the plurality of pipeline segments.

3. The smart gas pipeline temperature control method based on the Internet of Things as claimed in claim 2 is characterized in that: The cutting points of the pipeline segmentation include the position where the diameter of the gas pipeline changes, the pipeline bifurcation position and the pipeline intersection position; The method further comprises: The inlet velocity of the current segment is determined based on the outlet velocity and pipe diameter of the previous segment.

4. The smart gas pipeline temperature control method based on the Internet of Things as claimed in claim 1 is characterized in that: The updating of the candidate parameters based on the deformation assessment of the gas pipeline includes: determining a first magnitude of the candidate parameter based on a deformation assessment corresponding to the candidate parameter; determining a second magnitude of the candidate parameter based on the deformation assessment and consistency of the gas flow direction; and, The candidate parameters are updated based on the first amplitude and the second amplitude.

5. The smart gas pipeline temperature control method based on the Internet of Things as claimed in claim 1 is characterized in that: The gas pipeline is provided with a plurality of inspection points, and the plurality of inspection points correspond to at least one delivery station; The method further comprises: obtaining actual temperatures of the plurality of checkpoints; Determining the confidence of at least one delivery station corresponding to the plurality of checkpoints based on the actual temperatures of the plurality of checkpoints and historical predictions; and The delivery parameters are adjusted based on the confidence level of the at least one delivery station.

6. The smart gas pipeline temperature control method based on the Internet of Things as claimed in claim 5 is characterized in that: The number of the inspection points is related to the temperature influence of the gas pipeline.

7. A smart gas pipeline temperature control system based on the Internet of Things, characterized in that: The system includes a management platform, a sensor network platform and a gas equipment object platform respectively configured on different servers; The management platform includes a gas company management platform and a government safety supervision management platform, and data is exchanged between the gas company management platform and the government safety supervision management platform through a sensor network platform; the sensor network platform includes a gas company sensor network platform and a government safety supervision sensor network platform, and the sensor network platform operates based on data communication equipment; The gas company management platform is configured as follows: Obtaining pipeline information of the gas pipeline and basic perception data collected and uploaded by the gas equipment object platform, and obtaining candidate parameters from the government safety supervision and management platform, and determining the transmission parameters through at least one round of iterative interaction with the government safety supervision and management platform; as well as, Based on the delivery parameters, controlling the air compressor of the gas equipment object platform to adjust the gas delivery temperature; Among them, one round of iterative interaction includes: Based on the candidate parameters, the pipeline information and the environmental information, determining the temperature impact of the gas pipeline by a first model; the first model is a machine learning model; Sending the temperature impact of the gas pipeline to the government safety supervision management platform; The government security supervision management platform is configured as follows: Determining a deformation assessment of the gas pipeline based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information, and the basic sensing data; updating the candidate parameters based on the deformation assessment of the gas pipeline to obtain updated candidate parameters; and, In response to the deformation assessment of the gas pipeline satisfying a preset deformation condition, the updated candidate parameter is determined as the transport parameter.

8. The smart gas pipeline temperature control system based on the Internet of Things as claimed in claim 7 is characterized in that: The management platform is configured with: A parameter acquisition module, configured to acquire the basic perception data, the candidate parameters and the pipeline information; a temperature impact determination module, configured to determine the temperature impact of the gas pipeline through the first model based on the candidate parameters, the pipeline information and the environmental information; a deformation assessment determination module, configured to determine a deformation assessment of the gas pipeline based on the temperature impact of the gas pipeline, the candidate parameters, the pipeline information and the basic sensing data; A candidate parameter updating module, configured to update the candidate parameters based on the deformation evaluation of the gas pipeline to obtain updated candidate parameters; a transport parameter determination module, configured to determine the updated candidate parameter as the transport parameter in response to the deformation assessment of the gas pipeline satisfying a preset deformation condition; and The delivery temperature adjustment module is configured to control the air compressor of the gas equipment object platform to adjust the gas delivery temperature based on the delivery parameters.

9. The smart gas pipeline temperature control system based on the Internet of Things as claimed in claim 8, characterized in that: The gas pipeline includes a plurality of pipeline sections; the government safety supervision management platform is further configured with: The deformation assessment and adjustment module is configured as follows: For each pipeline segment in the plurality of pipeline segments, Determining a first confidence level for each of the pipeline segments based on the pipeline information, the environmental information, the transport parameters, the inlet data of each of the pipeline segments, and historical pipeline leakage information; and, A deformation assessment of each of the pipe segments is adjusted based on the first confidence level of each of the pipe segments.

10. The smart gas pipeline temperature control system based on the Internet of Things as claimed in claim 8, characterized in that: The gas pipeline is provided with a plurality of inspection points, and the plurality of inspection points correspond to at least one delivery station; The government security supervision management platform is further configured with: The transport parameter adjustment module is configured as follows: obtaining actual temperatures of the plurality of checkpoints; Determining the confidence of at least one delivery station corresponding to the plurality of checkpoints based on the actual temperatures of the plurality of checkpoints and historical predictions; and The delivery parameters are adjusted based on the confidence level of the at least one delivery station.

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