Intelligent gas-based pipeline support assembly regulation internet of things system, method and storage medium

Abstract

The application provides a pipeline support assembly regulation Internet of Things system and method based on smart gas, and a storage medium, and belongs to the field of gas pipeline maintenance.The system comprises a smart gas company sensing network platform, a smart gas equipment object platform and a smart gas company management platform.The method comprises the following steps: in response to the current time point being a judgment time point, obtaining sensing data of the gas pipeline network within a preset time period; in response to the gas pipeline network being at a target time point in a target deformation cycle, determining at least one of a height variable sequence and a damping variable sequence; then generating a height electric control instruction to adjust the height of the support assembly in the gas pipeline network before the next judgment time point; and generating a damping electric control instruction to adjust the damping of the compensation assembly in the gas pipeline network before the next judgment time point.The application can reduce the deformation of the pipeline in the gas pipeline network, avoid pipeline damage caused by long-term deformation, and better ensure the service life of the gas pipeline.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline maintenance, and in particular to an IoT system, method, and storage medium for regulating pipeline support components based on smart gas. Background Technology

[0002] With the acceleration of urbanization, urban gas pipeline networks are becoming increasingly complex, making pipeline safety and stability crucial for urban energy supply. However, gas pipelines are subject to various external forces and internal stresses during operation, leading to deformation and stress concentration. This not only affects normal pipeline operation but may also cause safety accidents. While support frames can be used to reduce pipeline deformation and stress concentration, they are clearly insufficient in dealing with the complex and variable stress environment of pipelines.

[0003] Therefore, it is desirable to provide an IoT system, method, and storage medium for regulating pipeline support components based on smart gas, so as to adjust the support frame more flexibly, thereby effectively reducing pipeline deformation and stress concentration, and improving pipeline safety and stability. Summary of the Invention

[0004] One embodiment of the present invention provides a method for regulating pipeline support components based on smart gas. The method is executed by a smart gas company management platform of a smart gas pipeline support component regulation IoT system. The method includes: determining whether the current time point is a judgment time point; in response to the current time point being the judgment time point, obtaining sensor data of the gas pipeline network within a preset time period from a smart gas equipment object platform; determining the current state of the gas pipeline network based on the current time period corresponding to the current time point, the sensor data, and the gas delivery parameters of the gas pipeline network; determining whether the gas pipeline network is at a target time point in a target deformation cycle based on the current state; in response to the gas pipeline network being at the target time point in the target deformation cycle, determining at least one of a height variable sequence and a damping variable sequence based on the target time point; generating a height electronic control command based on the height variable sequence to adjust the height of the support components in the gas pipeline network according to the height variable sequence before the next judgment time point; and generating a damping electronic control command based on the damping variable sequence to adjust the damping of the compensation components in the gas pipeline network according to the damping variable sequence before the next judgment time point.

[0005] One or more embodiments of the present invention provide an IoT system for regulating pipeline support components based on smart gas, including a smart gas company sensor network platform, a smart gas equipment object platform, and a smart gas company management platform; the smart gas company management platform is communicatively connected to the smart gas company sensor network platform and the smart gas equipment object platform; the smart gas equipment object platform includes at least one of a support component and a compensation component; the support component includes at least one electrically controlled support frame disposed in the gas pipeline network, the electrically controlled support frame including a support plate disposed below the gas pipeline, and a left support column and a right support column below the support plate; the compensation component includes at least one electrically controlled compensator disposed in the gas pipeline network; the smart gas company management platform is configured to execute the aforementioned method for regulating pipeline support components based on smart gas.

[0006] One or more embodiments of the present invention provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for regulating pipeline support components based on smart gas.

[0007] This invention can reduce the stress caused by environmental changes, reduce the deformation of pipelines in the gas pipeline network, avoid pipeline damage that may be caused by long-term deformation, thereby achieving better safety and better guaranteeing the service life of gas pipelines. Attached Figure Description

[0008] The present invention will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is a schematic diagram of the platform structure of an IoT system for regulating pipeline support components based on smart gas, according to some embodiments of the present invention.

[0010] Figure 2 This is a flowchart illustrating a method for regulating pipeline support components based on smart gas according to some embodiments of the present invention;

[0011] Figure 3 This is an exemplary schematic diagram illustrating the determination of the current state of a gas pipeline network according to some embodiments of the present invention;

[0012] Figure 4 This is a schematic diagram illustrating the adjustment of an electrical control support frame and / or an electrical control compensator by an Internet of Things system according to some embodiments of the present invention. Detailed Implementation

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of the present invention. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0014] Unless the context clearly indicates otherwise, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the term "comprising" only indicates that explicitly identified steps and elements are included, and these steps and elements do not constitute an exclusive list; the method or apparatus may also include other steps or elements.

[0015] This invention uses flowcharts to illustrate the operations performed by the system according to embodiments of the invention. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0016] Figure 1 This is a schematic diagram of the platform structure of an IoT system for regulating pipeline support components based on smart gas, according to some embodiments of the present invention. Figure 1 As shown, the smart gas pipeline support component control Internet of Things system 100 (also referred to as "Internet of Things system 100" or "Internet of Things system") may include a smart gas company management platform 110, a smart gas company sensor network platform 120, and a smart gas equipment object platform 130.

[0017] The Smart Gas Company Management Platform 110 (also known as "Company Management Platform 110" or "Company Management Platform") refers to a comprehensive management platform for gas companies to process and monitor information.

[0018] In some embodiments, the company management platform 110 is configured to execute a smart gas-based pipeline support component control method, the details of which can be found in the following description of the invention.

[0019] In some embodiments, the company management platform 110 may be configured on a processor and / or server used by the gas company, which may process data and / or information obtained from other platforms and execute program instructions based on such data, information and / or processing results to perform one or more functions described in this invention.

[0020] In some embodiments, the company management platform 110 can interact with the smart gas equipment object platform 130 through the smart gas company sensor network platform 120.

[0021] The Smart Gas Company Sensor Network Platform 120 (also referred to as "Sensor Network Platform 120" or "Sensor Network Platform") refers to the functional platform that manages the sensor communication of the gas company.

[0022] In some embodiments, the sensor network platform 120 can be configured as a communication network or gateway, etc., and can realize the functions of sensing communication of perception information and sensing communication of control information.

[0023] In some embodiments, the sensor network platform 120 can interact upwards with the company management platform 110 and downwards with the device object platform 130.

[0024] The intelligent gas equipment object platform 130 (also known as "equipment object platform 130" or "equipment object platform") refers to the functional platform for gas companies to generate sensing information and execute control information.

[0025] In some embodiments, the device object platform 130 may include support components, compensation components 132, sensors, pipeline robots, etc.

[0026] Support components are used to provide support for gas pipelines in order to reduce the deformation of the gas pipelines or reduce the stress on the gas appliance pipelines.

[0027] In some embodiments, the support assembly includes at least one electrically controlled support frame disposed in the gas pipeline network. The electrically controlled support frame includes a support plate disposed under the gas pipeline, and a left support column and a right support column below the support plate.

[0028] In some embodiments, the electrically controlled support frame is further provided with a shock-absorbing mechanism. In some embodiments, the shock-absorbing mechanism may include a left support column, a right support column, a spring, and a lifting block. Springs are provided at the bottom of the left and right support columns, and the top ends of the springs are slidably connected to the lifting block.

[0029] Compensation components are used to adjust the damping of gas pipelines.

[0030] In some embodiments, the compensation component includes at least one electrically controlled compensator connected to an adjacent gas pipeline in a gas pipeline network.

[0031] Sensors can be used to acquire sensing data in gas pipeline networks. For a detailed description of the sensing data, please refer to this invention. Figure 2 The relevant description in the document.

[0032] Pipeline robots can receive instructions from the company's management platform 110 and enter the gas pipeline network to collect data or perform other operations.

[0033] In some embodiments of the present invention, the information operation in the smart gas pipeline support component control Internet of Things system 100 can form a closed loop between various functional platforms, and operate in a coordinated and regular manner under the unified management of the smart gas company management platform, thereby realizing the informatization and intelligentization of smart gas pipeline support component control.

[0034] It should be noted that the above description of the IoT system 100 and its platform is for ease of description only and should not be construed as limiting the invention to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of this IoT system, may arbitrarily combine the various platforms or construct subsystems to connect with other platforms without departing from these principles.

[0035] Figure 2 This is a flowchart illustrating a method for regulating pipeline support components based on smart gas, according to some embodiments of the present invention. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by the company management platform 110.

[0036] Step 210: Determine whether the current time point is the analysis time point. If the current time point is the analysis time point, obtain the sensing data of the gas pipeline network within the preset time period from the smart gas equipment object platform.

[0037] The assessment time point refers to the point in time when it is necessary to assess the deformation and / or stress condition of the gas pipeline network.

[0038] In some embodiments, the company management platform 110 can determine the current time point through the system clock of the Internet of Things system 100, and determine whether the current time point is the right time point through various means.

[0039] In some embodiments, the company management platform 110 can pre-set several time points with the same interval as judgment time points. If the current time point matches a judgment time point, then the current time point is determined as the judgment time point. The interval can be determined based on prior experience and / or actual needs. Matching the current time point with the judgment time point means that the difference between the current time point and the judgment time point is less than a pre-set time threshold.

[0040] The preset time period refers to a historical period before the current time that is set in advance.

[0041] In some embodiments, the preset time period can be set based on prior experience and / or actual needs.

[0042] Sensor data is data that reflects the characteristics of the gas pipeline itself and its surrounding environment. In some embodiments, sensor data may include at least one of pipeline data and environmental data.

[0043] Pipeline data may include, but is not limited to, temperature data from multiple sampling points inside the pipeline. In some embodiments, pipeline data may be collected based on temperature sensors, which may be deployed at multiple sampling points on the gas pipeline wall and / or in the internal cavity of the gas pipeline. In some embodiments, the sampling points may be preset.

[0044] Environmental data may include at least one of ambient temperature data and ambient humidity data. In some embodiments, environmental data may be acquired based on temperature sensors and / or humidity sensors installed outside the gas appliance piping.

[0045] In some embodiments, the company management platform 110 can control the aforementioned sensors to acquire sensing data according to acquisition parameters within a preset time period. The acquisition parameters include at least one of the following: the data acquisition frequency and the acquisition accuracy of the sensors.

[0046] In some embodiments, the company management platform 110 may set the collection parameters based on prior experience and / or actual needs.

[0047] In some embodiments, the acquired parameters are also related to the environmental complexity of the gas pipeline network. Environmental complexity reflects the degree of complexity of the environment in which the gas pipeline network is located. The higher the environmental complexity, the more varied and complex the environment in which the gas pipeline network is located. In this case, the data acquisition frequency and accuracy of the sensors should be higher.

[0048] In some embodiments, the company management platform 110 can determine the environmental complexity based on environmental data of the environment in which the gas pipeline network is located within a preset time period. For example, the company management platform 110 can determine the range of environmental temperature data and environmental humidity data within a preset time period based on the environmental data, and determine the mean of the two ranges as the environmental complexity.

[0049] Step 220: Determine the current status of the gas pipeline network based on the current time period, sensor data, and gas delivery parameters of the gas pipeline network.

[0050] The current time period refers to the time period in which the current point in time is located.

[0051] In some embodiments, the company management platform 110 can divide the time period into multiple time periods according to preset standards and number them according to the order of the time periods, such as dividing by week, dividing by month, etc. The preset standards can be determined based on actual needs.

[0052] In some embodiments, the company management platform 110 can determine the current time period corresponding to the current time point by using the system clock set in the Internet of Things system 100.

[0053] Gas delivery parameters are parameters that characterize the features of gas delivery. In some embodiments, gas delivery parameters may include the gas flow rate, delivery temperature, or other gas delivery-related parameters for a period of time after the current point in time, which may be determined based on actual needs.

[0054] In some embodiments, the company management platform 110 can determine gas delivery parameters by obtaining user input or reading gas delivery plans stored in the Internet of Things system 100.

[0055] In some embodiments, the current state of the gas pipeline network may include the deformation cycle of the gas pipeline network and the time point within the deformation cycle corresponding to the current time point.

[0056] The deformation cycle refers to the length of time it takes for a gas pipeline to return to the same state after undergoing multiple deformations, starting from a certain state.

[0057] At least one gas pipeline in a gas pipeline network will undergo multiple deformation cycles due to the periodic changes in its environment. For example, periodic changes in light, temperature, and pressure will cause the gas pipeline to deform repeatedly.

[0058] The current time point within the deformation period is obtained by converting the current time point using the deformation period as a reference. For example, the current time point... The corresponding time point within the deformation period can be the first The first deformation cycle within the first deformation period Second.

[0059] In some embodiments, the company management platform 110 can determine the current status of the gas pipeline network in a variety of ways.

[0060] In some embodiments, the company management platform 110 can determine the current status of the gas pipeline network by querying a reference status table based on the current time period, sensor data, and gas delivery parameters of the gas pipeline network. The reference status table includes the correspondence between the current time period, sensor data, gas delivery parameters, and reference status. In some embodiments, the reference status table can be preset based on prior experience.

[0061] In some embodiments, the company management platform 110 can construct a sensor data map based on sensor data and the gas pipeline network, process the sensor data map using a stress prediction model to determine the stress map sequence of the gas pipeline network, and determine the current state of the gas pipeline network based on the stress map sequence. For more detailed descriptions, please refer to this invention. Figure 3 The relevant description in the document.

[0062] Step 230: Based on the current state, determine whether the gas pipeline network is at the target time point in the target deformation cycle. In response to the gas pipeline network being at the target time point in the target deformation cycle, determine at least one of the height variable sequence and the damping variable sequence based on the target time point.

[0063] The target deformation cycle refers to the deformation cycle during which the support components and / or compensation components need to be adjusted.

[0064] The target time point refers to the point in time within the target deformation period at which the control of the support components and / or compensation components begins.

[0065] In some embodiments, the company management platform 110 may pre-set the target deformation cycle and target time point based on prior experience.

[0066] In some embodiments, the company management platform can determine the target deformation period and target time point based on the deformation data corresponding to at least one deformation period.

[0067] Deformation data reflects the periodic characteristics of gas pipeline networks. For example, deformation data may include the time period corresponding to the deformation characteristics, the deformation distribution and stress distribution of the gas pipeline network within that deformation period, etc.

[0068] In some embodiments, the company management platform 110 may determine the deformation period in which the average deformation value is greater than the first deformation threshold as the target deformation period, and determine the time point in the target deformation period when the deformation value first exceeds the second deformation threshold as the target time point.

[0069] In some embodiments, the first deformation threshold and the second deformation threshold can be determined based on prior experience, and the first deformation threshold is less than the second deformation threshold.

[0070] The height variable sequence is a sequence of data reflecting the height adjustment values ​​of the electrically controlled support frame at at least one point in time. The number of elements in the height variable sequence represents the frequency of height adjustment of the electrically controlled support frame, and the value of the element represents the adjustment value of the height of the electrically controlled support frame.

[0071] The damping variable sequence is a sequence of data reflecting the damping adjustment values ​​of the electronically controlled compensator at at least one point in time. The number of elements in the damping variable sequence represents the frequency of damping adjustments to the electronically controlled compensator, and the value of each element represents the adjustment value of the damping to the electronically controlled compensator.

[0072] In some embodiments, the company management platform 110 can adjust the height of the electric control support frame and / or the damping of the electric control compensator at at least one preset time point based on at least one element in the height variable sequence and / or damping variable sequence.

[0073] In some embodiments, the company management platform 110 can query a reference sequence table based on the target deformation period and target time point to determine the height variable sequence and damping variable sequence. The reference sequence table includes the correspondence between the reference deformation period, reference time point, and variable sequence, wherein the aforementioned variable sequence includes the height variable sequence and damping variable sequence.

[0074] In some embodiments, the company management platform 110 can construct a reference sequence table based on historical data. For example, the company management platform 110 can filter historical variable sequences corresponding to historical deformation cycles and historical target time points based on selection criteria, and construct a reference sequence table based on historical variable sequences that meet the selection criteria. The selection criteria can be that after adjusting the support components and compensation components based on the historical variable sequences, the stress distribution within the gas pipeline network is most uniform.

[0075] Step 240: Generate height control instructions based on the height variable sequence, so as to adjust the height of the support components in the gas pipeline network according to the height variable sequence before the next assessment time point.

[0076] A height control command is an instruction used to instruct the electrically controlled support frame to perform damping adjustments. In some embodiments, the company management platform 110 can determine the height control command based on a sequence of height variables. For example, the height control command may be to adjust the electrically controlled support frame included in the support components of the gas pipeline network at at least one preset time point, sequentially according to the adjustment value corresponding to at least one element in the height variable sequence.

[0077] In some embodiments, the company management platform 110 can send height control commands to the equipment object platform 130 to control the electrically controlled support frame in the support assembly to adjust the height according to the adjustment value in the height variable sequence before the next judgment time point.

[0078] In some embodiments, in response to the current time being the judgment time point, the company management platform 110 can determine the next judgment time point based on the current time being the same as the time point of the preset time interval.

[0079] In some embodiments, the company management platform 110 can adjust the next assessment time point based on the target time point. For example, the company management platform 110 can determine the current deformation cycle based on the target time point, determine the first assessment time point in the next deformation cycle based on the current deformation cycle, and use this assessment time point as the next assessment time point.

[0080] For a detailed explanation of the target time and its determination, please refer to this invention. Figure 1 The relevant description in step 230.

[0081] Step 250: Generate damping electronic control commands based on the damping variable sequence, so as to adjust the damping of the compensation components in the gas pipeline network according to the damping variable sequence before the next judgment time point.

[0082] The damping electronic control command is an instruction used to direct the electronically controlled compensator to perform damping adjustment. In some embodiments, the company management platform 110 can determine the damping electronic control command based on the damping variable sequence, and adjust the damping of the electronically controlled compensator in the compensation component based on the damping electronic control command. The process is similar to the process of determining the height electronic control command and adjusting the height of the electronically controlled support frame, as described above.

[0083] In some embodiments of the present invention, by analyzing the deformation cycle of the gas pipeline network and judging the deformation situation at the current time point, the deformation and stress situation of the pipeline in the gas pipeline network can be updated in real time, and the adjustment parameters of the support components and compensation components can be determined to reduce the stress caused by environmental changes, reduce the deformation of the pipeline in the gas pipeline network, avoid pipeline damage that may be caused by long-term deformation, thereby obtaining better safety and better guaranteeing the service life of the gas pipeline.

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

[0085] Figure 3 This is an exemplary schematic diagram illustrating the determination of the current state of a gas pipeline network according to some embodiments of the present invention. Figure 3 As shown, the process of determining the current state of the gas pipeline network includes the following. In some embodiments, the process of determining the current state of the gas pipeline network may be performed by a smart gas company management platform.

[0086] In some embodiments, the smart gas company management platform can determine the stress spectrum sequence 330 of the gas pipeline network based on the sensor data spectrum 310 and the stress prediction model 320; and determine the current state 340 of the gas pipeline network based on the stress spectrum sequence 330.

[0087] Sensor data map 310 is a graph-structured data representation of the distribution characteristics of sensor data in a gas pipeline network. The sensor data map consists of nodes and edges.

[0088] In some embodiments, the node corresponds to a collection point in the gas pipeline network.

[0089] In some embodiments, node characteristics may include sensor data from a point in time.

[0090] In some embodiments, edges represent the connection relationships between collection points. When two collection points are located on the same gas pipeline or the gas pipelines they are on are directly connected, there is an edge between the nodes corresponding to the two collection points, pointing from upstream to downstream.

[0091] In some embodiments, edge features may include pipe distances between nodes.

[0092] In some embodiments, sensor data maps can be constructed based on sensor data. For a detailed description of sensor data, please refer to this invention. Figure 1 The relevant description in the document.

[0093] Stress spectrum sequence 330 is a sequence of data reflecting the stress distribution characteristics of a gas pipeline network at at least one time point. One element in the stress spectrum sequence corresponds to the stress spectrum of the gas pipeline network at a given time point.

[0094] In some embodiments, the stress map sequence may include stress maps at least one time point within the current assessment period. The current assessment period refers to the time interval between the current time point and the next assessment time point.

[0095] Stress maps are graphical data reflecting the stress distribution characteristics in gas pipeline networks. A stress map consists of nodes and edges, and its structure is similar to that of sensor data maps; please refer to the relevant description of sensor data maps. The difference is that the node features in a stress map can include the predicted stress values ​​of the data collection points in the gas pipeline network at a single point in time.

[0096] In some embodiments, the company management platform 110 can determine the stress spectrum sequence based on multiple sensor data spectra at different time points using a stress prediction model.

[0097] Stress prediction model 320 is a model used to predict stress map sequences. In some embodiments, the stress prediction model can be a machine learning model, such as a graph neural network (GNN), or other trained machine learning models. In some embodiments, the input to the stress prediction model can be sensor data maps, and the output is a stress map sequence corresponding to the gas pipeline network.

[0098] In some embodiments, the stress prediction model can be trained based on a first training sample with a first label. In some embodiments, the first training sample may include multiple sample sensor data maps, where the node features in different sensor data maps correspond to historical sensor data of the collection points at different historical time points in the gas pipeline map. The first label may be a stress map sequence corresponding to the first training sample. In some embodiments, the first training sample may be obtained based on historical data, and the first label may be constructed based on the actual stress values ​​of each collection point in the historical gas pipeline network corresponding to the first training sample at multiple subsequent time points, where one element in the first label corresponds to the historical actual stress value of the collection point in the gas pipeline network at a subsequent historical time point.

[0099] In some embodiments, the company management platform 110 can input multiple first training samples with first labels into the initial stress prediction model, construct a loss function based on the first labels and the results of the initial stress prediction model, and iteratively update the parameters of the initial stress prediction model using various methods based on the loss function. For example, updates can be performed using gradient descent. When the loss function of the initial stress prediction model meets preset conditions, model training is complete, and a trained stress prediction model is obtained. The preset conditions may include loss function convergence, the number of iterations reaching a threshold, etc.

[0100] In some embodiments, the smart gas company management platform can determine the current state of the gas pipeline network in various ways based on the stress spectrum sequence.

[0101] In some embodiments, the company management platform 110 can determine the stress prediction value sequence corresponding to each of multiple collection points in the gas pipeline network based on the stress spectrum sequence, and match the stress prediction value sequence corresponding to the collection point with at least one historical stress distribution corresponding to at least one historical deformation cycle. For example, the company management platform can determine at least one historical stress sequence corresponding to the collection point based on at least one historical stress distribution, determine the historical stress distribution corresponding to the historical stress sequence with the highest matching degree with the stress prediction value sequence as the target stress distribution, and take the historical deformation cycle corresponding to the target stress distribution as the current deformation cycle of the gas pipeline network. Based on the matching of the current stress distribution of the gas pipeline network with the target stress distribution, the time point of the gas pipeline network within the current deformation cycle is determined. For example, the company management platform can determine the element in the target stress distribution that is most similar to the current stress distribution, and determine the time point of the gas pipeline network within the current deformation cycle based on the time point corresponding to the element. If there are multiple most similar elements, the stress distribution of the candidate time point and the current time point is rematched with the target stress distribution to determine the element group that matches the stress distribution of the candidate time point and the current time point, and the time point of the gas pipeline network within the current deformation cycle is determined based on the latest time in the element group. Among them, the alternative time point refers to at least one historical time point that is closest to the current time point. The more times a match has occurred, the more alternative time points there are.

[0102] In some embodiments, the company management platform 110 can compare the stress at a sampling point in the target stress distribution with the stress at the corresponding sampling point in the current stress distribution. If the difference between the two is not greater than a preset stress threshold, the two are considered to be matched. By comparing the stress at each sampling point in the target stress distribution and the current stress distribution, the similarity between the current stress distribution and the target stress distribution is determined based on the ratio of the number of matching sampling points to the total number of sampling points.

[0103] In some embodiments, the company management platform 110 can analyze the historical deformation distribution of the gas pipeline network based on preset rules, determine at least one historical deformation cycle, and obtain the historical stress distribution in the gas pipeline network within at least one historical deformation cycle. The preset rules can be determined by technical personnel based on prior experience, and the company management platform 110 can obtain the preset rules uploaded by technical personnel through a user terminal.

[0104] Historical deformation distribution characterizes the deformation distribution of a gas pipeline network over multiple historical periods. These historical periods can be preset. Deformation distribution refers to the deformation locations of the gas pipeline network at multiple points in time and the corresponding deformation amount at each location.

[0105] In some embodiments, the company management platform 110 can determine the historical deformation distribution based on historical data collected by multiple sensors deployed in the gas pipeline network.

[0106] Historical stress distribution characterizes the stress distribution of a gas pipeline network over multiple historical periods. Stress distribution refers to the stress at various sampling points within the gas pipeline network at multiple time points.

[0107] In some embodiments, the company management platform 110 can determine the historical stress distribution based on historical data collected by multiple sensors deployed at collection points in the gas pipeline network.

[0108] In some embodiments, the company management platform 110 can determine the current state 340 of the gas pipeline network based on the stress spectrum sequence 330 and the state prediction model.

[0109] A state prediction model is a model used to predict the current state of a gas pipeline network. In some embodiments, the state prediction model can be a machine learning model, such as a graph neural network (DNN) model, or other trained machine learning models.

[0110] In some embodiments, the input to the state prediction model can be a stress spectrum sequence of the gas pipeline network, and the output can be the current state of the gas pipeline network.

[0111] In some embodiments, the company management platform 110 can iteratively train the initial model based on a large number of labeled training samples to determine the state prediction model.

[0112] In some embodiments, training samples may include a sequence of stress maps from historical data of the gas pipeline network. Labels may include the actual deformation period of the gas pipeline network and a time point within that deformation period.

[0113] In some embodiments, training samples can be obtained based on historical data, and the labels corresponding to the training samples can be obtained through manual annotation.

[0114] The training process for the state prediction model is similar to that for the stress prediction model; please refer to the relevant description of the stress prediction model.

[0115] In some embodiments, the process of iteratively training the initial model includes at least one training cycle. In response to the completion of a training cycle, the company management platform 110 can adjust the learning rate of the iterative training based on a decay factor. For example, each time the state prediction model completes a training cycle, the smart gas company management platform can multiply the learning rate of the state prediction model by a decay factor. In some embodiments, the decay factor takes a value between 0 and 1 and can be set empirically.

[0116] In some embodiments, a training cycle includes a preset number of iterations, the preset number being related to the layout information of the support components in the gas pipeline network. In some embodiments, the larger the standard deviation of the initial height values ​​of each electrically controlled support frame in the layout information of the support components, the larger the preset number can be. For more information on the layout information of the support components, see [link to documentation]. Figure 2 And its related descriptions.

[0117] In some embodiments of the present invention, when the height distribution of the gas pipeline network itself is not uniform, by increasing the preset number of rounds, the state prediction model can learn the data features of the training set, and the learning rate of the model can be adjusted according to the height distribution of the gas pipeline network itself, which can help the state prediction model converge to the optimal solution better and avoid the situation of not being able to converge during the training process.

[0118] In some embodiments of the present invention, the operating status of gas pipeline networks can be comprehensively and accurately assessed through multi-dimensional stress spectrum sequences and state prediction models, potential risks and abnormal situations can be identified, and managers can take preventive measures.

[0119] In some embodiments of the present invention, by collecting sensor data in real time and combining it with a stress prediction model, the future stress change trend can be predicted before the current time point, helping managers to fully understand the stress state of the pipeline network. Based on the stress spectrum sequence, the current state of the gas pipeline network can be comprehensively evaluated from multiple dimensions, ensuring the accuracy and comprehensiveness of the evaluation results.

[0120] In some embodiments, the company management platform 110 can control one or more pipeline robots equipped with stress sensors to collect measured stress values ​​at points of interest in the gas pipeline network; and update the stress map sequence based on the measured stress values.

[0121] Pipeline robots are automated devices used to perform tasks inside gas pipelines.

[0122] Stress sensors are devices used to measure stress at points of interest in a gas pipeline network, such as ultrasonic sensors.

[0123] Points of interest (POIs) refer to key locations within a gas pipeline network that require focused attention. For example, POIs may include valves, regulating stations, intersections, and joints within the gas pipeline network. In some embodiments, POIs may be a non-empty subset of the data collection points pre-defined by technicians.

[0124] In some embodiments, the company management platform 110 can update the node features of the nodes corresponding to the points of interest in the sensing data map based on the measured stress values ​​of the points of interest, such as by adding the measured stress values ​​to the node features, to obtain the updated sensing data map; and input the updated sensing data map into the stress prediction model to obtain the updated stress map sequence.

[0125] In some embodiments of the present invention, updating the stress spectrum sequence based on the measured stress values ​​collected by the pipeline robot can keep the stress spectrum up-to-date and better reflect the stress change trend in the gas pipeline network, thereby more accurately determining the adjustment values ​​of the support components and / or compensation components.

[0126] In some embodiments, the company management platform 110 can determine the reference layout density of the support components in the gas pipeline network based on the stress spectrum sequence; and adjust the layout parameters of the support components based on the measured stress value of the point of interest and the reference layout density.

[0127] Gas pipeline density refers to the number of supporting components per unit area in a gas pipeline network. The reference density refers to a recommended value for the density.

[0128] In some embodiments, the company management platform 110 can determine the reference layout density of support components in the gas pipeline network through cluster analysis based on the stress spectrum sequence.

[0129] In some embodiments, the company management platform 110 can construct a spectrum feature vector based on the stress spectrum sequence. The spectrum feature vector includes spectrum features corresponding to the stress spectrum of the gas pipeline network at at least one time point. The spectrum features may include the number of edges, the number of nodes, an edge feature sequence, and a node feature sequence. The elements in the edge feature sequence and the node feature sequence are arranged in a user-defined order.

[0130] In some embodiments, the company management platform 110 can determine at least one reference vector and its corresponding vector label based on historical data. Elements in the reference vector may include historical stress patterns of the gas pipeline network at at least one historical time point, and the corresponding label may be the historical deployment density of the gas pipeline network's support components at that historical time point. The aforementioned at least one historical time point is a historical time point in which no failure occurred in subsequent periods.

[0131] In some embodiments, historical data can be obtained by the company management platform 110 from the government regulatory comprehensive database through the smart gas government safety supervision sensor network platform.

[0132] In some embodiments, the company management platform 110 can determine the spectral feature vector and at least one reference vector as clustering objects, cluster the clustering objects based on clustering indices to obtain multiple clusters, and take the cluster containing the spectral feature vector as the target cluster; the company management platform 110 can take the average value of the vector labels corresponding to each reference vector in the target cluster as the reference deployment density of the supporting components in the gas pipeline network. In some embodiments, the clustering indices can be spectral features.

[0133] Layout parameters are parameters used to guide the layout of support components in a gas pipeline network. In some embodiments, layout parameters may include at least the layout density, and may also include other parameters related to the layout of the support components.

[0134] In some embodiments, the company management platform 110 can adjust the layout parameters of the support components based on the measured stress values ​​of the points of interest and the reference layout density. For example, the company management platform 110 can divide the gas pipeline network into at least one sub-region based on the points of interest, each sub-region including a preset number of points of interest; determine the sub-region where the layout density of the support components is less than the reference layout density and the range of the measured stress values ​​is greater than the range threshold as the region to be adjusted; increase the number of support components in the region to be adjusted so that the layout density of the support components in that region is not less than the reference layout density.

[0135] In some embodiments, the preset quantity and range threshold can be set based on prior experience.

[0136] In some embodiments of the present invention, by analyzing stress spectrum sequences, a comprehensive understanding of the stress distribution in different regions of the gas pipeline network can be achieved. By assessing the requirements of each region for support components, a reasonable reference layout density can be determined. By analyzing the measured stress values ​​at points of interest, local optimization of support components in specific regions can be performed, effectively reducing stress concentration in the gas pipeline network and lowering the probability of accidents such as pipeline rupture and leakage.

[0137] Figure 4 This is a schematic diagram illustrating the adjustment of the electrical control support frame and / or electrical control compensator by an Internet of Things system according to some embodiments of the present invention. Figure 4 As shown, the process of adjusting the electrical control support frame and / or the electrical control compensator includes the following. In some embodiments, the process of adjusting the electrical control support frame and / or the electrical control compensator may be performed by the company management platform 110.

[0138] In some embodiments, the company management platform 110 may adjust the number of elements 430 in the height variable sequence 410 and / or the damping variable sequence 420 according to the stress spectrum sequence 330; determine the adjustment frequency 440 based on the number of elements 430; and control the electric control support frame to adjust the height according to the adjustment frequency during the current assessment cycle, and / or control the electric control compensator to adjust the damping according to the adjustment frequency.

[0139] In some embodiments, the company management platform 110 can determine the number of elements in the height variable sequence and / or damping variable sequence by querying an element quantity table based on the stress spectrum sequence. The element quantity table may include a reference mean sequence of stress fluctuations at each sampling point in the gas pipeline network and the reference number of elements in the corresponding height variable sequence and / or damping variable sequence, wherein the stress fluctuation values ​​in the reference mean sequence are arranged in a pre-set order. The element quantity table can be constructed based on prior experience.

[0140] In some embodiments, the company management platform 110 can determine the mean stress fluctuation of each of the multiple collection points in the gas pipeline network based on the stress spectrum sequence, and organize the mean stress fluctuation of each of the multiple collection points into a fluctuation mean sequence according to a pre-set order (such as based on a pre-set numbering order). Based on the fluctuation mean sequence, the platform can query the element quantity table and determine the reference quantity corresponding to the reference mean sequence with the highest similarity as the number of elements in the height variable sequence and / or damping variable sequence.

[0141] Stress fluctuation refers to the numerical change in stress between the current time point and the previous time point; it only reflects the numerical value of stress change. The company's management platform 110 can determine the stress fluctuation at two time points and calculate the average stress fluctuation based on the average of the stress fluctuations at the two time points.

[0142] The adjustment frequency includes the height adjustment frequency and the damping adjustment frequency. In some embodiments, the adjustment frequency can be determined based on the number of elements in the height variable sequence and / or the damping variable sequence. For example, if the height variable sequence includes n elements, the company management platform needs to control the electrically controlled support frame to complete n height adjustments in the current assessment cycle.

[0143] In some embodiments, the company management platform 110 can, during the current assessment period, control the drive device to drive the electrically controlled support frame to perform at least one activity according to the element values ​​in the height variable sequence based on the adjustment frequency, so as to adjust the height of the electrically controlled support frame.

[0144] In some embodiments, the company management platform 110 can, in the current assessment cycle, control the current of the electromagnetic device in the electronically controlled compensator based on the adjustment frequency, so as to adjust the damping of the spring in the electronically controlled compensator according to the element values ​​in the damping variable sequence, thereby adjusting the stress that the electronically controlled compensator can compensate for.

[0145] For detailed explanations regarding height adjustment of the electronically controlled support frame based on a height variable sequence and damping adjustment based on a damping variable sequence, please refer to this invention. Figure 2 The relevant description in the document.

[0146] In some embodiments of the present invention, the number of elements in the height variable sequence and / or damping variable sequence is adjusted based on the stress spectrum sequence to determine the adjustment frequency, and the height and damping are adjusted based on the adjustment frequency. This is beneficial for rapid response to stress changes, reducing stress concentration, and improving the safety of the gas pipeline network structure.

[0147] In some embodiments, the company management platform 110 can also determine the vibration reduction characteristic sequence 461 of the gas pipeline network based on the height sequence 451 of the electric control support frame, the damping sequence 453 of the electric control compensator, the first layout information 452 of the support components, and the second layout information 454 of the compensation components in the current assessment period; determine the probability distribution sequence 480 of the external vibration excitation in the future period based on the external vibration excitation 471 at the current time point, the sensor data 472 of the preset time period, the measured stress value 473 of the point of interest, and the gas data 474 of the point of interest; determine the predicted amplitude sequence 490 based on the vibration reduction characteristic sequence 461 and the vibration excitation sequence 462; adjust the height variable sequence 410 and the damping variable sequence 420 in response to the predicted amplitude sequence 490 not meeting the preset conditions; and adjust the height of the electric control support frame and the damping of the electric control compensator based on the adjusted height variable sequence and damping variable sequence.

[0148] The height sequence is a sequence of height values ​​of each electrically controlled support frame at at least one time point in the current assessment cycle.

[0149] In some embodiments, the company management platform 110 can determine the height sequence based on the initial height and height variable sequence of the electrically controlled support frame.

[0150] The damping sequence refers to the sequence of damping values ​​of each electronically controlled compensator at at least one time point in the current assessment cycle.

[0151] In some embodiments, the smart gas company management platform can determine the damping sequence based on the initial damping and damping variable sequence of the electronically controlled compensator.

[0152] For detailed information on the height variable series and damping variable series, please refer to this invention. Figure 2 The relevant description in the document.

[0153] The first layout information refers to the layout information of the support components. The first layout information may include the position information and initial height of each electrical control support frame.

[0154] The second layout information refers to the layout information of the compensation components. This second layout information may include the location information and initial damping of each electronically controlled compensator.

[0155] In some embodiments, the first deployment information and the second deployment information can be obtained by the company management platform 110 through the device object platform 130. The first deployment information and the second deployment information can also be obtained based on input from technical personnel.

[0156] Vibration reduction characteristic sequence refers to data reflecting the magnitude of vibration reduction of a gas pipeline at at least one point in time.

[0157] In some embodiments, the company management platform 110 can query a reference vibration reduction characteristic table based on the height sequence of the electrically controlled support frame, the damping sequence of the electrically controlled compensator, the first layout information of the support components, and the second layout information of the compensation components in the current assessment period, to determine the vibration reduction characteristic sequence of the gas pipeline network. The reference vibration reduction characteristic table includes the reference height value of the electrically controlled support frame, the reference damping value of the electrically controlled compensator, and the correspondence between the reference layout information of the support components and the compensation components and the reference vibration reduction characteristic sequence. In some embodiments, the reference vibration reduction characteristic table can be preset based on prior experience.

[0158] A probability distribution sequence refers to the probability distribution of external vibration excitations at various times within the current assessment period. Here, probability distribution refers to the probability of occurrence of external vibration excitations with different directions and / or intensities. Each element in the probability distribution sequence corresponds to the probability distribution of external vibration excitations at a specific point in time.

[0159] External vibration excitation refers to vibrations caused by changes in the environment surrounding gas pipelines, including but not limited to vibrations caused by construction or subway operation.

[0160] In some embodiments, the company management platform 110 can be excited by a microwave sensor installed on the outer wall of the gas pipeline or by external vibration.

[0161] In some embodiments, the company management platform 110 can determine the probability distribution sequence of external vibration excitation in future periods based on the external vibration excitation at the current time point, the sensor data for a preset time period, the measured stress value of the point of interest, and the gas data of the point of interest.

[0162] For detailed information on obtaining sensor data and measured stress values, please refer to this invention. Figure 2 The relevant description in the document.

[0163] Gas data is data that reflects the characteristics of gas and its transmission. For example, gas data may include, but is not limited to, at least one of gas temperature, gas humidity, gas transmission rate, and gas transmission pressure.

[0164] In some embodiments, the company management platform 110 may acquire gas data through sensors deployed in the gas pipeline network and / or pipeline robots equipped with sensors.

[0165] In some embodiments, the company management platform 110 can determine the probability distribution sequence of external vibration excitation in the future period through various methods. For example, the company management platform 110 can construct a target vector based on external vibration excitation, sensor data, measured stress values ​​of points of interest, and gas data of points of interest, calculate the similarity between the target vector and feature vectors in the vector database, and use the label corresponding to the feature vector with the highest similarity as the probability distribution sequence of external vibration excitation in the future period.

[0166] The vector database can include feature vectors and their corresponding labels. Feature vectors can be constructed based on historical external vibration excitations, historical sensor data, historical measured stress values ​​of points of interest, and historical gas data of points of interest from historical data.

[0167] In some embodiments, the label may be a reference distribution sequence, which includes a reference value for the probability distribution of external vibration excitation at at least one time point.

[0168] In some cases, the same external vibration excitation may occur multiple times at different points in time. When the sensor data, measured stress values, and gas data are the same, their corresponding feature vectors are identical, meaning the feature vector can correspond to multiple external vibration excitations. The company's management platform 110 can use a preset algorithm to obtain the probability distribution sequences corresponding to each of the multiple external vibration excitations in the historical data, and determine the probability distribution sequence with the highest average probability as the label of the feature vector. Here, "highest average probability" means that the average value of each probability in the probability distribution sequence is the largest. The preset algorithm can be a time series algorithm, a neural network, an ensemble learning method, or other methods that can determine the external vibration conditions and their probabilities.

[0169] In some embodiments, a predicted amplitude sequence 490 is determined based on the damping characteristic sequence 461 and the vibration excitation sequence 462. In response to the predicted amplitude sequence 490 not meeting preset conditions, the company management platform 110 can adjust the height variable sequence 410 and the damping variable sequence 420 based on the stress spectrum sequence 330. Based on the number of elements in the adjusted height and damping variable sequences, an adjustment frequency is determined. During the current assessment cycle, based on the adjustment frequency, the height of the electrically controlled support frame is adjusted, and the damping of the electrically controlled compensator is adjusted. For detailed explanations regarding controlling the height adjustment of the electrically controlled support frame and the damping adjustment of the electrically controlled compensator, please refer to this invention. Figure 2 The relevant description in the document.

[0170] The vibration excitation sequence is a sequence of external vibration excitations with the highest probability of occurrence. In some embodiments, the company management platform 110 can filter external vibration excitations 471 based on the probability distribution sequence 480, and construct a vibration excitation sequence 462 based on external vibration excitations with a probability of occurrence greater than a probability threshold.

[0171] The predicted amplitude sequence refers to the sequence of predicted amplitude values ​​of the gas pipeline. One element of the predicted amplitude sequence corresponds to the predicted amplitude of the gas pipeline at a certain point in time.

[0172] In some embodiments, the predicted amplitude can be determined based on the product of the damping characteristics at a given time point and the standard amplitude corresponding to the external vibration excitation at that time point. The aforementioned standard amplitude can be determined based on prior experience. For details on determining the damping characteristics and the external vibration excitation, please refer to the relevant descriptions above.

[0173] The preset condition can be that the average amplitude of the future time period at each time point is not greater than a preset threshold.

[0174] In some embodiments, in response to the predicted amplitude sequence not meeting preset conditions, the company management platform 110 can adjust the height variable sequence and the damping variable sequence based on preset adjustment rules.

[0175] The preset threshold can be set in advance by technicians based on experience.

[0176] In some embodiments, the preset threshold is related to the structural complexity of the current gas pipeline network. The greater the structural complexity of the current gas pipeline network, the smaller the preset threshold.

[0177] By determining the probability distribution sequence based on external vibration excitation and sensor data, and then adjusting the height and damping variable sequences, the height and damping can be improved. This helps to enhance the real-time response of the electrically controlled support frame and the electrically controlled compensator to different vibration and environmental conditions, improve the response effect to external vibration excitation, reduce stress transmission, and thus better protect the gas pipeline network.

[0178] The basic concepts have been described above. It is clear that the detailed disclosure above is merely illustrative and does not constitute a limitation of the present invention. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to the present invention by those skilled in the art. Such modifications, improvements, and corrections are suggested in this invention and therefore remain within the spirit and scope of the exemplary embodiments of the present invention.

[0179] Furthermore, this invention uses specific terms to describe embodiments of the invention. For example, "some embodiments" refers to a particular feature, structure, or characteristic associated with at least one embodiment of the invention. Additionally, certain features, structures, or characteristics in one or more embodiments of the invention can be appropriately combined.

[0180] Finally, it should be understood that the embodiments described in this invention are merely illustrative of the principles of the invention. Other modifications may also fall within the scope of this invention. Therefore, alternative configurations of the embodiments of this invention are considered as examples and not limitations, and are regarded as consistent with the teachings of this invention. Accordingly, the embodiments of this invention are not limited to those explicitly described and illustrated herein.

Claims

1. A smart gas pipeline support component control IoT system, characterized in that, The system includes a smart gas company sensor network platform, a smart gas equipment object platform, and a smart gas company management platform. The smart gas company management platform is communicatively connected to the smart gas company sensor network platform and the smart gas equipment object platform. The intelligent gas equipment platform includes at least one of a support component and a compensation component; the support component includes at least one electrically controlled support frame disposed in the gas pipeline network, the electrically controlled support frame includes a support plate disposed below the gas pipeline, a left support column and a right support column below the support plate, and a shock absorption mechanism; the compensation component includes at least one electrically controlled compensator disposed in the gas pipeline network. The smart gas company management platform is configured as follows: Determine whether the current time point is the assessment time point. If the current time point is the assessment time point, obtain the sensor data of the gas pipeline network within a preset time period from the smart gas equipment object platform. The current state of the gas pipeline network is determined based on the current time period corresponding to the current time point, the sensor data, and the gas delivery parameters of the gas pipeline network. Based on the sensor data map, the stress map sequence of the gas pipeline network is determined through a stress prediction model; the sensor data map is constructed based on the sensor data. The stress prediction model is a machine learning model; The current state of the gas pipeline network is determined based on the stress spectrum sequence; wherein, The stress spectrum sequence includes stress spectrums at least one time point in the current assessment period, and the stress spectrum includes the predicted stress value of at least one collection point in the gas pipeline network; the current assessment period refers to the time period between the current time point and the next assessment time point. Based on the current state, determine whether the gas pipeline network is at a target time point in the target deformation cycle. In response to the gas pipeline network being at the target time point in the target deformation cycle, determine at least one of a height variable sequence and a damping variable sequence based on the target time point. Based on the height variable sequence, a height control command is generated to adjust the height of the support components in the gas pipeline network according to the height variable sequence before the next assessment time point; Based on the damping variable sequence, a damping electronic control command is generated to adjust the damping of the compensation components in the gas pipeline network according to the damping variable sequence before the next judgment time point; Based on the height sequence of the electric control support frame, the damping sequence of the electric control compensator, the first layout information of the support components, and the second layout information of the compensation components in the current assessment period, the vibration reduction characteristic sequence of the gas pipeline network is determined. Based on the external vibration excitation at the current time point, the sensing data during the preset time period, the measured stress value at the point of interest, and the gas data at the point of interest, determine the probability distribution sequence of the external vibration excitation in the future time period; The predicted amplitude sequence is determined based on the vibration reduction characteristic sequence and the probability distribution sequence. In response to the predicted amplitude sequence not meeting the preset conditions, the height variable sequence and the damping variable sequence are adjusted. Based on the adjusted height variable sequence and the damping variable sequence, the height of the electric control support frame and the damping of the electric control compensator are adjusted.

2. The system as described in claim 1, characterized in that, The smart gas company management platform is also configured as follows: Adjust the number of elements in the height variable sequence and / or the damping variable sequence according to the stress spectrum sequence; The adjustment frequency is determined based on the number of the elements. During the current assessment cycle, the height of the electrically controlled support frame is adjusted according to the adjustment frequency, and / or the damping of the electrically controlled compensator is adjusted according to the adjustment frequency.

3. The system as described in claim 1, characterized in that, The smart gas company management platform is further configured as follows: One or more pipeline robots equipped with stress sensors are controlled to collect measured stress values ​​at points of interest in the gas pipeline network; the points of interest are a non-empty subset of the collected points. The stress spectrum sequence is updated based on the measured stress values.

4. A method for regulating pipeline support components based on smart gas supply, characterized in that, The method is executed by a smart gas company management platform based on a smart gas pipeline support component control IoT system; The smart gas pipeline support component control IoT system includes a smart gas company sensor network platform, a smart gas equipment object platform, and a smart gas company management platform. The smart gas company management platform is communicatively connected to the smart gas company sensor network platform and the smart gas equipment object platform. The intelligent gas equipment platform includes at least one of a support component and a compensation component; the support component includes at least one electrically controlled support frame installed in the gas pipeline network, the electrically controlled support frame includes a support plate installed below the gas pipeline, a left support column and a right support column below the support plate, and a shock absorption mechanism; The compensation component includes at least one electrically controlled compensator disposed in the gas pipeline network; The method includes: Determine whether the current time point is the assessment time point. If the current time point is the assessment time point, obtain the sensor data of the gas pipeline network within a preset time period from the smart gas equipment object platform. The current state of the gas pipeline network is determined based on the current time period corresponding to the current time point, the sensor data, and the gas delivery parameters of the gas pipeline network. Based on the sensor data map, the stress map sequence of the gas pipeline network is determined through a stress prediction model; the sensor data map is constructed based on the sensor data; the stress prediction model is a machine learning model. The current state of the gas pipeline network is determined based on the stress spectrum sequence; wherein, The stress spectrum sequence includes stress spectrums at least one time point in the current assessment period, and the stress spectrum includes the predicted stress value of at least one collection point in the gas pipeline network; the current assessment period refers to the time period between the current time point and the next assessment time point. Based on the current state, determine whether the gas pipeline network is at a target time point in the target deformation cycle. In response to the gas pipeline network being at the target time point in the target deformation cycle, determine at least one of a height variable sequence and a damping variable sequence based on the target time point. Based on the height variable sequence, a height control command is generated to adjust the height of the support components in the gas pipeline network according to the height variable sequence before the next assessment time point; Based on the damping variable sequence, a damping electronic control command is generated to adjust the damping of the compensation components in the gas pipeline network according to the damping variable sequence before the next judgment time point; Based on the height sequence of the electric control support frame, the damping sequence of the electric control compensator, the first layout information of the support components, and the second layout information of the compensation components in the current assessment period, the vibration reduction characteristic sequence of the gas pipeline network is determined. Based on the external vibration excitation at the current time point and the sensing data during the preset time period, determine the probability distribution sequence of the external vibration excitation in the future time period; In response to the fact that the damping characteristic sequence and the probability distribution sequence do not meet the preset conditions, the height variable sequence and the damping variable sequence are adjusted; Based on the adjusted height variable sequence and the damping variable sequence, the height of the electric control support frame and the damping of the electric control compensator are adjusted.

5. The method as described in claim 4, characterized in that, The method further includes: One or more pipeline robots equipped with stress sensors are controlled to collect measured stress values ​​at points of interest in the gas pipeline network; the points of interest are a non-empty subset of the collected points. The stress spectrum sequence is updated based on the measured stress values.

6. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the pipeline support component control method based on smart gas as described in claim 4.

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