Intelligent Gas Pipeline Network Transformation Method, System and Medium Based on Supervisory Internet of Things

Abstract

The present invention provides a method, system and medium for the intelligent transformation of a gas pipeline network based on a supervision Internet of Things, which relates to the field of pipeline network transformation. This method is executed by the gas company management platform of the intelligent gas pipeline network transformation system based on the supervision Internet of Things. The method includes: obtaining gas monitoring data from the intelligent gas equipment object platform through the gas company sensing network platform; obtaining historical fault data through the gas database, and determining transformation strategy parameters based on the historical fault data and the gas monitoring data; generating a control instruction based on the transformation strategy parameters, and sending the control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to adjust the monitoring parameters of the gas monitoring equipment of the intelligent gas equipment object platform. The present invention can monitor the usage status of gas pipelines in real time and accurately, so as to timely discover and effectively handle the pipelines that need to be transformed, thereby significantly improving the overall safety performance of gas pipelines.

Description

Technical Field

[0001] The present invention relates to the field of pipe network transformation, and particularly to a smart gas pipe network transformation method, system and medium based on a supervision Internet of Things. Background Art

[0002] With the increase in the service life of gas pipe networks, in some old communities with long-term use of gas pipe networks, some pipelines in the pipe networks have problems such as aging facilities and frequent failures, which may lead to safety hazards. When transforming old pipelines, it is necessary to determine the location and transformation method of the pipeline segments to be transformed in the pipe network, so as to realize the pipe network transformation on the premise of ensuring the stable operation of the whole pipe network.

[0003] Therefore, it is hoped to propose a smart gas pipe network transformation method, system and medium based on a supervision Internet of Things to monitor the usage status of gas pipelines in real time and accurately, so as to be able to timely discover and effectively handle the pipelines that need to be transformed, and then significantly improve the overall safety performance of gas pipelines. Summary of the Invention

[0004] The summary of the invention includes a smart gas pipe network transformation method based on the Internet of Things. This method is executed by the gas company management platform of the smart gas pipe network transformation system based on the supervision Internet of Things, and includes: obtaining gas monitoring data from the smart gas equipment object platform through the gas company sensing network platform, and storing the gas monitoring data in the gas database; obtaining historical fault data through the gas database, determining transformation strategy parameters based on the historical fault data and the gas monitoring data, and uploading the transformation strategy parameters to the smart gas government safety supervision management platform through the smart gas government safety supervision sensing network platform. The transformation strategy parameters include at least one of the data of the pipeline to be transformed and the construction parameters of the pipeline to be transformed; and generating a control instruction based on the transformation strategy parameters, and sending the control instruction to the smart gas equipment object platform through the gas company sensing network platform to adjust the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

[0005] The invention content includes a smart gas pipeline network transformation system based on the supervision Internet of Things. The system includes: a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a gas company sensor network platform, and a smart gas equipment object platform. The smart gas government safety supervision object platform includes a gas company management platform. The gas company management platform is configured to: obtain gas monitoring data from the smart gas equipment object platform through the gas company sensor network platform and store the gas monitoring data in a gas database; obtain historical fault data through the gas database, determine transformation strategy parameters based on the historical fault data and the gas monitoring data, and upload the transformation strategy parameters to the smart gas government safety supervision management platform through the smart gas government safety supervision sensor network platform. The transformation strategy parameters include at least one of the data of the pipeline to be transformed and the construction parameters of the pipeline to be transformed; and generate a control instruction based on the transformation strategy parameters and send the control instruction to the smart gas equipment object platform through the gas company sensor network platform to adjust the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

[0006] The invention content includes a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the smart gas pipeline network transformation method based on the Internet of Things according to any one of the above.

[0007] The beneficial effects of the present invention include but are not limited to: (1) Through the smart gas pipeline network transformation system based on the supervision Internet of Things, an information operation closed-loop can be formed among various functional platforms, and they can operate coordinately and regularly, realizing the informatization and intelligence of the smart gas pipeline network transformation. (2) By monitoring the states such as temperature and humidity inside and outside the gas pipeline, the fault conditions of the gas pipeline can be monitored in real time and accurately, so that the gas pipeline that needs to be transformed can be discovered in time, and the appropriate transformation method can be judged according to the gas database to ensure the safety of gas transmission. (3) Through the evaluation parameters of the gas pipeline, the pipeline to be transformed in the gas pipeline network and the corresponding construction parameters can be quickly determined, which can ensure the accuracy and smooth progress of the gas pipeline network transformation. The leakage of the gas pipeline that may leak can be monitored by a crawling robot, and the leakage situation of the gas pipeline can be responded to in time. Brief Description of the Drawings

[0008] The present invention will be further described in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0009] Figure 1 is a schematic diagram of the platform structure of the smart gas pipeline network transformation system based on the supervision Internet of Things shown in some embodiments of this specification;

[0010] Figure 2 is an exemplary flowchart of a method for transforming an intelligent gas pipeline network based on the Internet of Things as shown in some embodiments of this specification;

[0011] Figure 3 is an exemplary schematic diagram of an evaluation model as shown in some embodiments of this specification;

[0012] Figure 4 is an exemplary schematic diagram of an impact estimation model as shown in some embodiments of this specification. Detailed implementation manners

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for description in the embodiments will be briefly introduced below. The accompanying drawings do not represent all implementation manners.

[0014] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the said words can be replaced by other expressions.

[0015] Unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0016] In the embodiments of the present invention, when the operations performed are described step by step, unless otherwise specified, the order of the steps can be adjusted, the steps can be omitted, and other steps can also be included during the operation process.

[0017] Figure 1 is a schematic diagram of the platform structure of an intelligent gas pipeline network transformation system based on the Internet of Things for supervision as shown in some embodiments of this specification.

[0018] As Figure 1 shown, the intelligent gas pipeline network transformation system 100 based on the Internet of Things for supervision may include an intelligent gas government safety supervision management platform 110, an intelligent gas government safety supervision sensing network platform 120, an intelligent gas government safety supervision object platform 130, a gas company sensing network platform 140, an intelligent gas equipment object platform 150, and a gas user object platform 160.

[0019] The intelligent gas government safety supervision management platform 110 refers to a comprehensive management platform for government management information.

[0020] In some embodiments, the intelligent gas government safety supervision management platform 110 may interact with the intelligent gas government safety supervision sensing network platform 120.

[0021] The intelligent gas government safety supervision sensing network platform 120 refers to a platform for comprehensive management of government sensing information. The intelligent gas government safety supervision sensing network platform 120 may be configured as a communication network or a gateway, etc.

[0022] In some embodiments, the intelligent gas government safety supervision sensing network platform 120 may interact with the gas company management platform 131. For example, the intelligent gas government safety supervision sensing network platform 120 may obtain the transformation strategy parameters uploaded by the gas company management platform 131.

[0023] The intelligent gas government safety supervision object platform 130 refers to a platform for generating government supervision information and executing control information. In some embodiments, the intelligent gas government safety supervision object platform 130 may include the gas company management platform 131.

[0024] The gas company management platform 131 refers to a comprehensive management platform for gas company information.

[0025] In some embodiments, the gas company management platform 131 may be configured to obtain gas monitoring data, store the gas monitoring data in the gas database; determine transformation strategy parameters based on historical fault data and gas monitoring data; generate a regulation instruction based on the transformation strategy parameters, and send the regulation instruction to the intelligent gas equipment object platform through the gas company sensing network platform to regulate the monitoring parameters of the gas monitoring equipment of the intelligent gas equipment object platform.

[0026] The gas database is used to store information and / or data related to the intelligent gas pipeline network transformation system 100 based on the supervision Internet of Things. For example, the gas database may store gas monitoring data, historical fault data, and historical monitoring data, etc.

[0027] In some embodiments, the gas company management platform 131 may also be configured to generate and send leakage monitoring instructions; determine estimated monitoring data, estimated fault data, and estimated evaluation parameters; generate and send fault monitoring instructions; generate and indirectly regulate instructions; generate and send monitoring expansion instructions.

[0028] The gas company sensing network platform 140 refers to a platform for comprehensively managing the sensing information of the gas company. In some embodiments, the gas company sensing network platform 140 may be configured as a communication network or a gateway, etc. The gas company sensing network platform 140 may interact with the gas company management platform 131.

[0029] The intelligent gas equipment object platform 150 refers to a functional platform for generating sensing information and executing control information. In some embodiments, the intelligent gas equipment object platform 150 can interact with the gas company sensing network platform 140.

[0030] In some embodiments, the intelligent gas equipment object platform may include gas monitoring devices and crawling robots. The intelligent gas equipment object platform can communicate and / or be physically connected to the gas monitoring devices and crawling robots, and can control the gas monitoring devices and crawling robots while acquiring data. The gas monitoring device refers to a device for collecting gas monitoring data. For example, the gas monitoring device may include at least one of a gas flow rate measuring instrument, a digital pressure gauge, a gas analyzer, a temperature sensor, a humidity sensor, etc. arranged inside the gas pipeline, and at least one of a temperature sensor, a humidity sensor, etc. arranged outside the gas pipeline. The equipment management platform can be wirelessly connected to the crawling robot.

[0031] In some embodiments, the intelligent gas pipeline network transformation system 100 based on the supervision Internet of Things may further include a server. Each platform can be set on the server and connected through network communication. The server can process information and / or data related to the intelligent gas pipeline network transformation system 100 based on the supervision Internet of Things to perform one or more functions described in this application. In some embodiments, the server may include a processor, a memory, a storage device, and a network. The storage device can store a gas database. The gas database refers to a database management system that supports high-concurrency access.

[0032] For the foregoing detailed description, reference may be made to Figures 2 to 4 the relevant description.

[0033] In some embodiments of this specification, through the intelligent gas pipeline network transformation system 100 based on the supervision Internet of Things, an information operation closed-loop can be formed between each functional platform, coordinated and regularly operated, to achieve the informatization and intelligence of the intelligent gas pipeline network transformation.

[0034] Figure 2 is an exemplary flowchart of an intelligent gas pipeline network transformation method based on the Internet of Things according to some embodiments of this specification. In some embodiments, the process 200 of the intelligent gas pipeline network transformation method based on the Internet of Things is executed by the gas company management platform (hereinafter referred to as the company management platform) of the intelligent gas pipeline network transformation system based on the supervision Internet of Things. As Figure 2 shown, the process 200 of the intelligent gas pipeline network transformation method based on the Internet of Things includes the following steps:

[0035] In some embodiments, the company management platform can obtain gas monitoring data from the intelligent gas equipment object platform through the gas company sensing network platform, and store the gas monitoring data in the gas database. The company management platform can obtain historical fault data through the gas database, determine the renovation strategy parameters based on the historical fault data and the gas monitoring data, and upload the renovation strategy parameters to the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensing network platform. The company management platform can generate a control instruction based on the renovation strategy parameters, and send the control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to adjust the monitoring parameters of the gas monitoring equipment of the intelligent gas equipment object platform.

[0036] For the relevant content of each platform of the intelligent gas pipeline renovation system based on the supervision Internet of Things, reference can be made to Figure 1 the corresponding description.

[0037] Step 210: Obtain gas monitoring data from the intelligent gas equipment object platform through the gas company sensing network platform, and store the gas monitoring data in the gas database.

[0038] Gas monitoring data refers to the data obtained by monitoring the inside and / or outside of the gas pipeline. In some embodiments, the gas monitoring data may include internal monitoring data and external monitoring data. The internal monitoring data may include at least one of gas flow rate, gas temperature, gas pressure, gas composition, etc. The external monitoring data may include at least one of ambient temperature, ambient humidity, etc.

[0039] In some embodiments, the gas monitoring data can be collected by multiple gas monitoring devices arranged inside and / or outside the gas pipeline and sent to the intelligent gas equipment object platform. The company management platform can obtain the gas monitoring data from the intelligent gas equipment object platform and store the gas monitoring data in the gas database. For the description of the gas monitoring devices, reference can be made to Figure 1 and its related description.

[0040] In some embodiments, the company management platform can obtain gas monitoring data from the intelligent gas equipment object platform through the gas company sensing network platform.

[0041] Step 220: Obtain historical fault data through the gas database, determine the renovation strategy parameters based on the historical fault data and the gas monitoring data, and upload the renovation strategy parameters to the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensing network platform.

[0042] Renovation strategy parameters refer to the parameters used to guide pipeline renovation. In some embodiments, the renovation strategy parameters may include data of the pipeline to be renovated and construction parameters, etc.

[0043] The pipeline data to be modified refers to the data related to the pipeline to be renovated. For example, aging pipelines and pipelines with frequent failures, etc. In some embodiments, the company management platform can number all gas pipelines, and the pipeline data to be modified includes the numbers of the pipelines to be renovated.

[0044] The construction parameters refer to the parameters for the renovation construction of the pipeline to be renovated. In some embodiments, the construction parameters can include the renovation methods for the pipeline to be renovated. The renovation methods can include replacing the pipeline, repairing the pipeline, and replacing accessories, etc.

[0045] Replacing the pipeline means excavating the original pipeline by trenching and replacing it with a new one. Pipeline repair means repairing the old pipeline without excavation. Accessory replacement means replacing only the accessories without excavating the old pipeline.

[0046] The historical failure data refers to the failure data of the gas pipeline within a historical time period. In some embodiments, the failure data can include the failure location, failure type, failure time, etc. The failure types can include leakage and corrosion, etc.

[0047] In some embodiments, the company management platform can obtain the historical failure data through the gas database. When a gas pipeline fails, the intelligent gas equipment object platform can upload the failure data to the gas database.

[0048] In some embodiments, the company management platform can determine the renovation strategy parameters based on the historical failure data and gas monitoring data.

[0049] In some embodiments, the company management platform can determine the pipeline to be renovated in various ways based on the historical failure data and gas monitoring data. For example, the company management platform can count the number of times and / or frequency of failures of the gas pipeline within a historical time period based on the historical failure data of the gas pipeline, and determine whether the number of times and / or frequency of failures meets the failure conditions. In response to the historical number of failures and / or frequency meeting the failure conditions, the gas pipeline is determined as the pipeline to be renovated. Among them, the preset failure conditions include that the number and / or frequency is greater than the failure threshold. The failure threshold can include the thresholds corresponding to the number and frequency, and the failure threshold can be set in advance based on historical experience.

[0050] For another example, the company management platform can, based on the changes in the gas component data in the gas monitoring data, calculate the change range of the gas components in the gas pipeline within a preset time period, and determine whether the change range of the gas component data meets the preset fault condition. In response to the change range of the gas components meeting the preset fault condition, the gas pipeline is determined as a pipeline to be renovated. Among them, the preset fault condition includes that the change range of the gas component data is greater than the change threshold, and the change threshold and the preset time period can be set in advance based on historical experience. The gas components can be collected by a gas analyzer in the gas monitoring device.

[0051] In some embodiments, the company management platform can determine construction parameters based on historical fault data and gas monitoring data in various ways. For example, the company management platform can, based on the historical fault data of the gas pipeline, calculate the number of times and / or frequency of faults that occurred in the gas pipeline within a historical time period, query the first construction parameters corresponding to the number of times and / or frequency in the first preset table, and determine the first construction parameters as the construction parameters.

[0052] In some embodiments, the first preset table can be set in advance based on historical experience, including multiple groups of the number of times and / or frequency and the first construction parameters corresponding to each group of the number of times and / or frequency. Among them, the higher a group of the number of times and / or frequency, the more the corresponding first construction parameter tends to be set as replacing the pipeline.

[0053] For another example, the company management platform can, based on the gas monitoring data, determine the change range of the gas components in the gas monitoring data, query the second construction parameters corresponding to the change range in the second preset table, and determine the second construction parameters as the construction parameters.

[0054] In some embodiments, the second preset table can be set in advance based on historical experience, including multiple groups of historical change ranges of gas components in historical data and the second construction parameters corresponding to each group of historical change ranges. Among them, the larger a group of historical change ranges, the more the corresponding second construction parameter tends to be set as replacing the pipeline.

[0055] In some embodiments, the company management platform can determine construction parameters based on the pipeline to be renovated and the evaluation parameter sequence. For more content on this part, reference can be made to Figure 3 the corresponding description.

[0056] Step 230: Generate a regulation instruction based on the renovation strategy parameters, and send the regulation instruction to the intelligent gas equipment object platform through the gas company sensing network platform to regulate the monitoring parameters of the gas monitoring equipment of the intelligent gas equipment object platform.

[0057] The regulation instruction refers to an instruction used to regulate the monitoring parameters of the gas monitoring equipment.

[0058] The monitoring parameters refer to the parameters related to the operation of the gas monitoring equipment. In some embodiments, the detection parameters may include the monitoring frequency and monitoring accuracy of the gas monitoring equipment.

[0059] In some embodiments, the company management platform may increase the monitoring frequency and monitoring accuracy of the gas monitoring equipment for the pipeline to be renovated based on the renovation strategy parameters, generate a control instruction based on the gas monitoring equipment and the updated monitoring frequency and monitoring accuracy, and send the control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the control instruction.

[0060] It can be understood that since accidents may occur in the pipeline to be renovated, it is necessary to increase the monitoring frequency and monitoring accuracy of the gas monitoring equipment to avoid the deterioration of the gas pipeline situation before renovation and the inability to respond in a timely manner, which is conducive to adjusting the construction parameters in a timely manner when the situation deteriorates.

[0061] In some embodiments, the management platform may generate an indirect control instruction based on the monitoring intensity and send the indirect control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the affected area. For details of this part, please refer to Figure 4 and its related descriptions.

[0062] In some embodiments of this specification, by monitoring the states such as temperature and humidity inside and outside the gas pipeline, the fault condition of the gas pipeline can be monitored in real time and accurately, so that the gas pipeline that needs to be renovated can be detected in time, and the suitable renovation method can be determined according to the gas database to ensure the safety of gas transmission.

[0063] It should be noted that the description of the process 200 of the method for renovating the intelligent gas pipeline network based on the Internet of Things above is only for illustration and explanation, and does not limit the scope of application of the present invention. For those skilled in the art, various corrections and changes can be made to the process 200 of the method for renovating the intelligent gas pipeline network based on the Internet of Things under the guidance of the present invention. However, these corrections and changes are still within the scope of the present invention.

[0064] Figure 3 is an exemplary schematic diagram of the evaluation model shown in some embodiments of this specification.

[0065] In some embodiments, the company management platform can obtain historical monitoring data 311 through the gas database, and determine an evaluation parameter sequence 330 through an evaluation model 320 based on the historical monitoring data 311, historical fault data 312, pipeline data 313, and gas monitoring data 314. In response to the evaluation parameter sequence 330 meeting a preset condition, the company management platform can determine the pipeline to be renovated. The company management platform can determine construction parameters based on the pipeline to be renovated and the evaluation parameter sequence. In response to the pipeline health score of the pipeline to be renovated meeting a first monitoring condition, the company management platform can generate a leakage monitoring instruction based on the pipeline health score, and send the leakage monitoring instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the crawler robot of the intelligent gas equipment object platform to move to the pipeline to be renovated and perform leakage monitoring. For the description of historical fault data and gas monitoring data, reference can be made to Figure 2 and its related descriptions.

[0066] Historical monitoring data refers to the gas monitoring data collected by gas monitoring equipment within a historical time period. In some embodiments, the company management platform can obtain historical monitoring data through the gas database.

[0067] The evaluation parameter sequence refers to a sequence composed of multiple evaluation sequences. The evaluation parameter sequence can include multiple historical evaluation parameters and a current evaluation parameter. An evaluation parameter refers to a parameter for evaluating the operating state of a pipeline.

[0068] In some embodiments, the evaluation parameters can include the delivery fluctuation degree, corrosion degree, potential risk, and confidence level of the potential risk, etc. Among them, the delivery fluctuation degree can characterize the fluctuation degree of the gas flow delivered by a gas pipeline. The delivery fluctuation degree, corrosion degree, and confidence level of the potential risk can all be expressed in percentages or other ways. The larger the percentage representing the delivery fluctuation degree, the greater the fluctuation of the gas flow delivered by the gas pipeline.

[0069] In some embodiments, the potential risk can include at least one of leakage risk, stress risk, geological risk, interface risk, and fracture risk, etc. Each risk corresponds to a confidence level. The leakage risk refers to the possible occurrence of gas leakage. The stress risk refers to the risk that may occur due to stress changes caused by factors such as temperature changes and pressure fluctuations. The geological risk refers to the risk that may occur due to changes in the geological conditions of the area where the pipeline is located. The interface risk refers to the risk of the sealing and stability at the pipeline interface. The fracture risk refers to the risk that the pipeline may fracture due to pipeline aging, corrosion, etc.

[0070] In some embodiments, the company management platform may determine an evaluation parameter sequence based on historical monitoring data, historical fault data, pipeline data, and gas monitoring data through an evaluation model. Among them, the company management platform may use multiple evaluation parameters determined by the evaluation model for historical time as multiple historical evaluation parameters, and form an evaluation parameter sequence with the current evaluation parameters.

[0071] Pipeline data refers to data related to gas pipelines. In some embodiments, pipeline data may include pipeline parameter data, pipeline interface data, and geological parameter data. If the gas pipeline is on the ground surface, the pipeline data does not include geological parameter data. In some embodiments, the pipeline data may be uploaded by the gas company to the company management platform.

[0072] Pipeline parameter data refers to data related to the gas pipeline itself. In some embodiments, the pipeline parameter data may include at least one of pipeline material, pipeline size, pipeline length, and lining parameters, etc.

[0073] Pipeline interface data refers to data related to the interfaces of gas pipelines. In some embodiments, the pipeline interface data may include the types and statuses of pipeline interfaces.

[0074] Geological parameter data refers to data related to the geology of the area where the gas pipeline is located. In some embodiments, the geological parameter data may include at least one of pipeline burial depth, soil data, geological structure parameters, ground vibration frequency, etc.

[0075] The evaluation model refers to a model used to determine evaluation parameters. In some embodiments, the evaluation model may be a machine learning model. For example, the evaluation model may include any one or combination of a Recurrent Neural Network (RNN) model or other custom model structures, etc.

[0076] In some embodiments, the company management platform may train the evaluation model based on a large number of first training samples with first labels through methods such as gradient descent. The first training samples may include sample historical monitoring data, sample historical fault data, sample pipeline data, and sample gas monitoring data, and the first labels of the first training samples may be actual evaluation parameters.

[0077] In some embodiments, the first training samples may be obtained based on historical data. The first labels may be determined based on manual annotation. For example, the conveying fluctuation degree and corrosion degree in the first labels can be measured by manual use of measuring instruments such as flow meters and corrosion detectors for gas pipelines, and the potential risks and the confidence levels of potential risks can be evaluated through manual on-site inspections.

[0078] In some embodiments, the evaluation model can be trained as follows: input a plurality of first training samples with first labels into the initial evaluation model, construct a loss function based on the first labels and the prediction results of the initial evaluation model, iteratively update the initial evaluation model based on the loss function, and when the loss function of the initial evaluation model meets the preset loss condition, the evaluation model training is completed. Among them, the preset loss condition can be that the loss function converges, the number of iterations reaches a set value, etc.

[0079] In some embodiments, in response to the evaluation parameter sequence meeting the preset condition, the pipeline to be renovated is determined.

[0080] The preset condition refers to the condition used to determine whether a gas pipeline needs to be renovated. In some embodiments, the preset condition can be set in advance based on historical experience and includes multiple parameter thresholds. Among them, each parameter threshold corresponds to one item of data in the evaluation parameters.

[0081] In some embodiments, the company management platform can determine the pipeline to be renovated based on the current evaluation parameters. For example, if any one of the delivery fluctuation degree, corrosion degree, and confidence level of potential risks in the evaluation parameters exceeds the corresponding parameter threshold in the preset condition, the company management platform can determine the gas pipeline as the pipeline to be renovated.

[0082] In some embodiments, the preset condition can also include a health score threshold. The company management platform can determine the pipeline health score based on the evaluation parameters, and in response to the pipeline health score being lower than the health score threshold in the preset condition, determine the gas pipeline as the pipeline to be renovated. The health score threshold can be set in advance based on historical experience.

[0083] The pipeline health score can characterize the degree to which a gas pipeline may malfunction. The lower the pipeline health score, the more likely the gas pipeline is to malfunction.

[0084] In some embodiments, the pipeline health score can be related to the delivery fluctuation degree, corrosion degree, potential risks, and their confidence levels. For example, the pipeline health score can be negatively correlated with the delivery fluctuation degree, corrosion degree, potential risks, and their confidence levels. Only as an example, the company management platform can determine the pipeline health score through the following formula (1):

[0085] H = 100 - (a1×S1 + a2×S2 + a3×S3 + a4×S4 + a5×S5 + a6×S6 + a7×S7) (1)

[0086] Among them, H is the pipeline health score, S1 is the transportation fluctuation degree, S2 is the corrosion degree, S3 is the leakage risk confidence level, S4 is the stress risk confidence level, S5 is the geological risk confidence level, S6 is the interface risk confidence level, S7 is the fracture risk confidence level, and a1 - a7 are coefficients. Among them, a1 - a7 can be preset based on historical experience.

[0087] In some embodiments, the company management platform can determine the pipeline to be renovated based on the evaluation parameter sequence. For example, the company management platform can determine multiple historical pipeline health scores based on multiple historical evaluation parameters in the evaluation parameter sequence. In response to the average change range of the multiple historical pipeline health scores being greater than the preset range threshold, the gas pipeline is determined as the pipeline to be renovated. Among them, the change range can be characterized by the difference between the historical pipeline health score at the previous moment and the historical pipeline health score at the subsequent moment. The preset range threshold can be preset based on historical experience.

[0088] In some embodiments, the company management platform can query the historical evaluation parameters identical to the evaluation parameters in the preset parameter table based on the pipeline to be renovated and the evaluation parameters in the evaluation parameter sequence, and determine the reference construction parameters corresponding to the historical evaluation parameters as the construction parameters of the pipeline to be renovated. Among them, the preset parameter table can be preset based on historical data, including multiple historical evaluation parameters and the reference construction parameters corresponding to each historical evaluation parameter. The reference construction parameters can be the construction parameters actually used for renovation at historical times.

[0089] The leakage monitoring instruction refers to an instruction used to indicate whether a gas pipeline has leaked.

[0090] In some embodiments, in response to the pipeline health score of the pipeline to be renovated satisfying the first monitoring condition, the company management platform can generate a leakage monitoring instruction based on the pipeline health score. Among them, the first monitoring condition can include that the pipeline health score is lower than the leakage monitoring threshold. The leakage monitoring threshold can be preset based on historical experience.

[0091] In some embodiments, the leakage monitoring instruction is sent to the intelligent gas equipment object platform through the gas company sensing network platform to control the crawling robot of the intelligent gas equipment object platform to move to the pipeline to be renovated and perform leakage monitoring. The crawling robot can be equipped with a leakage monitoring device and crawl outside the pipeline for leakage monitoring.

[0092] The leakage monitoring device refers to a device used to monitor the external gas content of the pipeline. For example, a gas leak detector and a gas detector, etc.

[0093] In some embodiments of this specification, based on the evaluation parameters of gas pipelines, the pipelines to be renovated in the gas pipeline network and the corresponding construction parameters can be quickly determined, which can ensure the accurate and smooth progress of the renovation of the gas pipeline network. The leakage of gas pipelines that may occur can be monitored by a crawling robot, and the leakage situation of the gas pipeline can be responded to in a timely manner.

[0094] In some embodiments, the evaluation parameter sequence may further include estimated evaluation parameters, and the evaluation model may further include a prediction layer and an evaluation layer. The company management platform can construct a pipeline map based on historical monitoring data, historical fault data, gas monitoring data, future weather data, the evaluation parameter sequence, and pipeline data; determine estimated monitoring data and estimated fault data through the prediction layer based on the pipeline map; and determine estimated evaluation parameters through the evaluation layer based on the estimated monitoring data, estimated fault data, historical monitoring data, historical fault data, gas monitoring data, and pipeline data.

[0095] In some embodiments, the prediction layer and the evaluation layer can be trained separately.

[0096] The pipeline map refers to a graph structure that represents the association relationship between gas monitoring devices and pipeline interfaces, etc. The graph structure is a data structure composed of nodes and edges. The edges connect the nodes, and the nodes and edges can have features.

[0097] In some embodiments, the company management platform can construct a pipeline map based on the connection relationship between gas monitoring devices and pipeline interfaces. The nodes of the pipeline map can include interface nodes and device nodes. Among them, the interface node refers to the node representing the position of the pipeline interface. The device node refers to the node representing the position of the gas monitoring device.

[0098] In some embodiments, the node features can include interface node features and device node features. The interface node features include pipeline data and historical fault data; the device node features include gas monitoring data, historical monitoring data, etc.

[0099] The edges of the pipeline map can represent the connectivity between nodes. In some embodiments, the edges of the pipeline map can correspond to the gas pipelines connecting between nodes. The edge features corresponding to the edges include the historical fault data of the pipelines corresponding to the edges, future weather data, the evaluation parameter sequence, etc. The direction of the edge is the direction of gas flow.

[0100] Future weather data refers to the weather conditions at future times. For example, the company management platform can obtain future weather data through external meteorological platforms and weather forecasts, etc.

[0101] In some embodiments, the prediction layer refers to a model used to determine the estimated monitoring data and the estimated fault data. In some embodiments, the prediction layer can be a machine learning model. For example, the prediction layer can be any one or a combination of a Graph Neural Networks (GNN) model or other custom model structures, etc.

[0102] In some embodiments, the output of the prediction layer can include the estimated monitoring data output by the device nodes and the estimated fault data output by the edges.

[0103] The estimated monitoring data refers to the gas monitoring data at estimated future times. In some embodiments, the estimated monitoring data can include the gas monitoring data at multiple future time points. Among them, the multiple future time points can be preset based on historical experience.

[0104] The estimated fault data refers to the types of faults that may occur at estimated future times. In some embodiments, the estimated fault data can include the types of faults that may occur at multiple future time points. If it is estimated that no fault will occur in the gas pipeline at a future time point, the estimated fault data is empty.

[0105] In some embodiments, the company management platform can train the prediction layer based on a large number of second training samples with second labels through methods such as gradient descent. The second training samples can include sample pipeline maps. The second labels can be the gas monitoring data and fault data actually obtained at historical time points. The second training samples and the second labels can be obtained based on historical data. For the description of the fault data, reference can be made to Figure 2 and its related descriptions. The historical time point refers to the time point corresponding to the estimated monitoring data and the estimated fault data output by the prediction layer at a historical time. For example, if the time point corresponding to the estimated monitoring data and the estimated fault data output by the prediction layer at 10 o'clock is 15 o'clock, then the historical time point is 15 o'clock.

[0106] In some embodiments, the training process of the prediction layer is similar to the training process of the above evaluation model, and the implementation method can refer to the training method of the above evaluation model.

[0107] In some embodiments, in response to the estimated fault data satisfying the second monitoring condition, the company management platform can generate a fault monitoring instruction based on the estimated fault data, and send the fault monitoring instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the crawling robot of the intelligent gas equipment object platform to move to the pipeline to be monitored and perform mobile patrol.

[0108] The pipeline to be monitored refers to the gas pipeline corresponding to the estimated fault data that satisfies the second monitoring condition.

[0109] A fault monitoring instruction refers to an instruction used to indicate mobile patrol. In some embodiments, the fault monitoring instruction may be configured to cause a crawling robot in a pipeline to be monitored to perform a reciprocating mobile patrol centered on its own location with a preset length, and to monitor whether a fault occurs in the pipeline to be monitored during the mobile patrol.

[0110] In some embodiments, the preset length may be related to the detection sensitivity of the crawling robot and the distribution of fault points. For example, the preset length may be negatively correlated with the detection sensitivity of the crawling robot and the density of the distribution of fault points. The lower the detection sensitivity and the lower the density of the distribution of fault points, the longer the preset length.

[0111] The detection sensitivity refers to the ability of the crawling robot to detect the smallest fault signal. The distribution of fault points refers to the distribution of the fault positions of the gas pipeline in historical time. In some embodiments, the company management platform may generate the distribution of fault points based on the fault positions in the historical fault data. Among them, the shorter the distance between the fault positions, the higher the density of the distribution of fault points. The detection sensitivity may be uploaded by the gas company to the company management platform.

[0112] In some embodiments of this specification, based on the detection sensitivity of the crawling robot and the distribution of fault points, the patrol length can be reasonably arranged. On the premise of ensuring the monitoring effectiveness, the unnecessary patrol length can be shortened. Even if the detection sensitivity of the crawling robot is low, a longer patrol length can help the crawling robot cover more areas and increase the probability of detecting potential faults.

[0113] In some embodiments, in response to the estimated fault data meeting the second monitoring condition, the company management platform may generate a fault monitoring instruction based on the estimated fault data. Among them, the second monitoring condition may include that the estimated fault data is not empty.

[0114] In some embodiments, the company management platform may send the fault monitoring instruction to the intelligent gas equipment object platform to control the crawling robot of the intelligent gas equipment object platform to move to the pipeline to be monitored and perform a mobile patrol.

[0115] In some embodiments of this specification, since gas leakage is often dynamic, if the crawling robot is fixed at one position for detection, the situation of untimely detection and slow response may occur. By analyzing the estimated fault data and generating a fault monitoring instruction, the movement of the crawling robot to the pipeline to be monitored and the execution of a mobile patrol can be timely controlled, thereby expanding the monitoring range, reducing the monitoring blind area, and thus discovering potential faults in advance.

[0116] The evaluation layer refers to a model used to determine the estimated evaluation parameters. In some embodiments, the evaluation layer can be a machine learning model. For example, the evaluation layer can include any one or combination of a Recurrent Neural Network (RNN) model or other custom model structures, etc.

[0117] In some embodiments, the input of the evaluation layer includes historical monitoring data, historical fault data, and pipeline data, and the output of the evaluation layer includes estimated evaluation parameters. Among them, the historical monitoring data includes the current gas monitoring data and the estimated monitoring data output by the prediction layer. The historical fault data includes the estimated fault data output by the prediction layer.

[0118] The estimated evaluation parameter refers to the evaluation parameter at a future time point estimated. In some embodiments, the estimated evaluation parameter can include the evaluation parameters at multiple future time points.

[0119] In some embodiments, the company management platform can sequentially determine the estimated evaluation parameters for each of the multiple future time points through the evaluation layer. Among them, when determining a future time point, the historical monitoring data and historical fault data input to the evaluation layer can include the estimated monitoring data and estimated fault data corresponding to this future time point, as well as the estimated monitoring data and estimated fault data of the future time points before this future time point.

[0120] Exemplarily, when the company management platform determines the estimated evaluation parameter for the second future time point through the evaluation layer, the historical monitoring data and historical fault data input to the evaluation layer can also include the estimated monitoring data and estimated fault data of the second future time point, as well as the estimated monitoring data and estimated fault data of the first future time point. Among them, the second future time point is later than the first future time point.

[0121] In some embodiments, the training method and process of the evaluation layer are similar to those of the above evaluation model, and its implementation method and process can refer to the training method and process of the above evaluation model. The difference is that the company management platform can use the third training sample and the third label. The third training sample can include sample historical monitoring data, sample historical fault data, sample pipeline data, sample gas monitoring data, sample estimated monitoring data, and sample estimated fault data. The third label can include the actual evaluation parameters at the future time point. Among them, the sample estimated monitoring data and sample estimated fault data can be obtained through the prediction layer. The description of obtaining the sample historical monitoring data, sample historical fault data, sample pipeline data, sample gas monitoring data, and the actual evaluation parameters can refer to the relevant description above.

[0122] In some embodiments, for gas pipelines that are not determined to be pipelines to be renovated, the company management platform may determine an estimated pipeline health score corresponding to the estimated evaluation parameters based on the estimated evaluation parameters, and in response to the estimated pipeline health score being lower than the health score threshold, determine the gas pipeline as a pipeline to be renovated. For more information about the pipeline health score and the health score threshold, reference may be made to the relevant descriptions above.

[0123] In some embodiments, in response to the average decrease amplitude of the estimated pipeline health score being greater than a preset amplitude threshold, the company management platform may determine the gas pipeline as a pipeline to be renovated. By way of example only, the company management platform may calculate the difference between the current pipeline health score and the estimated pipeline health score corresponding to the first future time point to obtain a first difference, and calculate the difference between the estimated pipeline health score corresponding to the second future time point and the estimated pipeline health score corresponding to the first future time point to obtain a second difference, and determine the average of the first difference and the second difference as the average decrease amplitude. For more information about the preset amplitude threshold, reference may be made to the relevant descriptions above.

[0124] In some embodiments, the renovation strategy parameters may further include the renovation time point of the pipeline to be renovated. The renovation time point refers to the time point for renovating the pipeline to be renovated.

[0125] In some embodiments, the company management platform may determine the time point when the estimated pipeline health score is first lower than the health score threshold as the renovation time point based on the estimated pipeline health scores corresponding to multiple future time points.

[0126] In some embodiments, the company management platform may query the historical evaluation parameters identical to the estimated evaluation parameters in the preset parameter table based on the estimated evaluation parameters of the renovation time point and the pipeline to be renovated, and determine the reference construction parameters corresponding to the historical evaluation parameters as the construction parameters of the pipeline to be renovated. For the description of the preset parameter table, reference may be made to Figure 2 and its related descriptions.

[0127] In some embodiments of this specification, by constructing a pipeline map, historical monitoring data, historical fault data, gas monitoring data, future weather data, evaluation parameter sequences, and pipeline data can be effectively organized. At the same time, using an evaluation model with a hierarchical structure can quickly and accurately determine the estimated evaluation parameters, reduce the difficulty of training the evaluation model, improve the evaluation of the evaluation model, and is easy to adjust and maintain.

[0128] Figure 4 is an exemplary schematic diagram of an impact estimation model shown according to some embodiments of this specification.

[0129] In some embodiments, the transformation strategy parameters may further include the affected degree of the affected area, and the regulation instructions may further include indirect regulation instructions. The company management platform may construct a transformation map 420 based on the gas monitoring data 314, the pipeline to be modified data 412, and the construction parameters 413; and determine the affected degree 440 through the impact estimation model 430 based on the transformation map 420. The company management platform may also determine the monitoring intensity corresponding to the affected area based on the affected degree 440; generate an indirect regulation instruction based on the monitoring intensity, and send the indirect regulation instruction to the intelligent gas device object platform through the gas company sensing network platform to regulate the monitoring parameters of the gas monitoring devices corresponding to the affected area. For the description of the gas monitoring data, the pipeline to be modified data, and the construction parameters, reference can be made to Figure 2 and Figure 3 its related descriptions.

[0130] An indirect regulation instruction refers to an instruction used to regulate the monitoring parameters of gas monitoring devices.

[0131] The affected degree of the affected area is used to characterize the degree of impact on the affected area when construction is carried out on the pipeline to be modified. The affected area refers to the area affected due to the transformation of the gas pipeline network. In some embodiments, the affected area may include the pipeline to be modified and the area where the gas pipelines that may be affected are located. For example, the affected area may include the pipeline to be modified and the pipelines around the pipeline to be modified.

[0132] A transformation map refers to a graph structure representing the association relationship of different gas monitoring devices.

[0133] In some embodiments, the company management platform may construct a transformation map based on the connection relationships of multiple gas monitoring devices. The nodes of the transformation map may include monitoring device nodes (such as node 421), and the monitoring device nodes represent the locations of gas monitoring devices. The node features of the monitoring device nodes may include historical monitoring data and gas monitoring data corresponding to the gas monitoring devices. For the description of the historical monitoring data, reference can be made to Figure 2 its related descriptions.

[0134] The edges of the transformation map may represent the connectivity between nodes and are directed edges, and the direction of the edges is the direction of gas flow. In some embodiments, the edges of the transformation map may include transformation edges and general edges, both of which are gas pipelines connecting between nodes. Among them, the transformation edge represents the pipeline to be modified, and the general edge represents a gas pipeline that is not the pipeline to be modified.

[0135] In some embodiments, the characteristics of the modified edge may include pipeline parameter data, geological parameter data, historical fault data, and construction parameters. The characteristics of the general edge may include pipeline parameter data, geological parameter data, historical fault data, and an evaluation parameter sequence. For the pipeline parameter data, geological parameter data, historical fault data, and the evaluation parameter sequence, reference can be made to Figure 2 , Figure 3 and its related descriptions.

[0136] In some embodiments, the characteristics of the modified edge and the characteristics of the general edge may further include the evaluation parameter sequence of the gas pipeline corresponding to the edge.

[0137] In some embodiments of this specification, since the evaluation parameters can reflect the actual condition of the gas pipeline, introducing the evaluation parameters in the renovation atlas can more precisely estimate which areas will be affected by the renovation and the degree of influence, and can avoid underestimating the affected degree of the affected areas and causing damage to the affected areas.

[0138] The impact estimation model refers to a model used to determine the degree of influence. In some embodiments, the impact estimation model can be a machine learning model. For example, the impact estimation model can include any one or a combination of a Graph Neural Network (GNN) model or other custom model structures, etc.

[0139] In some embodiments, the input of the impact estimation model can include the renovation atlas, and the output can include the affected area and the degree of influence of the affected area.

[0140] In some embodiments, the impact estimation model can be trained based on a training sample set. The training sample set includes multiple training samples and the label corresponding to each training sample. Each training sample includes a sample renovation atlas. The label corresponding to each training sample is the actual degree of influence of the affected area.

[0141] In some embodiments, the company management platform can train the impact estimation model based on the training sample set through methods such as gradient descent. The training method of the impact estimation model is similar to that of the evaluation model, and its implementation method can refer to the training method of the evaluation model.

[0142] In some embodiments, the training sample set can be obtained based on historical data. For example, the company management platform can construct a sample renovation atlas based on historical gas monitoring data, historical pipeline to be renovated data, and historical construction parameters in the historical data, determine the area actually affected when renovating the pipeline to be renovated as the affected area, and determine the actual degree of influence of the affected area as the label.

[0143] In some embodiments, the label corresponding to each training sample can be determined based on the data change amplitude of the gas monitoring data and / or the vibration amplitude during the renovation process. For example, the company management platform can obtain the gas monitoring data of the general edges in the sample renovation map before and after the renovation edge construction, calculate the data change amplitude of the gas monitoring data before and after the construction, determine the affected area in the label for the general edges with a data change amplitude greater than the change threshold, and determine the actual degree of influence of the affected area through a preset correspondence relationship. Among them, the change threshold can be preset based on historical experience.

[0144] The preset correspondence relationship can be preset based on historical experience. In some embodiments, the preset correspondence relationship can include that the degree of influence is positively correlated with the data change amplitude, and the greater the data change amplitude, the higher the degree of influence.

[0145] In some embodiments, the company management platform can also obtain the average vibration amplitude of the general edges before and after the renovation edge construction through vibration sensors deployed on the gas pipelines corresponding to the general edges, determine the affected area in the label for the general edges with an average vibration amplitude greater than the vibration threshold, and determine the actual degree of influence of the affected area through a preset correspondence relationship. Among them, the preset correspondence relationship here can include that the degree of influence is positively correlated with the average vibration amplitude, and the greater the average vibration amplitude, the higher the degree of influence. The vibration threshold can be preset based on historical experience.

[0146] The vibration sensor is used to collect the vibration amplitude of the pipeline based on the collection frequency. The collection frequency can be preset based on historical experience. The average vibration amplitude refers to the mean value of the vibration amplitudes collected based on the collection frequency.

[0147] In some embodiments, the company management platform can also perform a weighted sum of the average vibration amplitude and the data change amplitude, and determine the obtained sum value as the degree of influence. The weights for the weighted sum can be preset based on historical experience.

[0148] In some embodiments, the number of training samples corresponding to each renovation method in the training sample set satisfies a preset number condition.

[0149] The preset number refers to the condition for restricting the number of training samples corresponding to different renovation methods in the training sample set. In some embodiments, the preset number condition can include that the number of training samples corresponding to each renovation method is not less than the corresponding number threshold. Among them, the number threshold corresponding to accessory replacement is less than the number threshold corresponding to pipeline repair, and the number threshold corresponding to pipeline repair is less than the number threshold corresponding to pipeline replacement. For the description of the renovation methods, please refer to Figure 2 and its related descriptions.

[0150] In some embodiments, the magnitude of each quantity threshold can be preset. For example, the magnitude of the quantity threshold can be positively correlated with the average retrofit cost of the method corresponding to the quantity threshold. The average retrofit cost of the retrofit method refers to the mean value of the costs of using this retrofit method multiple times over a historical period.

[0151] In some embodiments of this specification, by setting the number of training samples corresponding to each retrofit method in the training sample set to meet the preset quantity condition, it is possible to enable the impact estimation model to maintain good generalization ability when facing different types of retrofit methods. By installing vibration sensors on the pipelines around the pipeline to be retrofitted and combining the data change range of the gas monitoring data to determine the degree of influence, potential safety hazards can be discovered more promptly.

[0152] The monitoring intensity refers to the data characterizing the intensity of monitoring by the gas monitoring equipment within the affected area. In some embodiments, the monitoring intensity can include the acquisition frequency of the gas monitoring equipment.

[0153] In some embodiments, the company management platform can determine the monitoring intensity according to a preset intensity relationship based on the degree of influence and the regional importance of the affected area. Among them, the preset intensity relationship can include that the monitoring intensity is positively correlated with the degree of influence and the regional importance.

[0154] The regional importance can characterize the degree to which the affected area needs to be monitored.

[0155] In some embodiments, the company management platform can determine the pipeline importance of the gas pipelines within the affected area as the regional importance. If the affected area includes multiple gas pipelines, the regional importance can be the mean value of the pipeline importances of the multiple gas pipelines.

[0156] The pipeline importance can characterize the degree to which the gas pipeline needs to be monitored. In some embodiments, the company management platform can calculate the weighted average of the gas pipeline's average flow rate and the population density of the area where the gas pipeline is located to obtain the pipeline importance. Among them, the weights of the two can be preset based on historical experience. The average flow rate can be obtained from the intelligent gas equipment object platform. The population density can be obtained by means such as user input.

[0157] In some embodiments, the company management platform can generate an indirect control instruction based on the monitoring intensity, and send the indirect control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to adjust the monitoring parameters of the gas monitoring equipment corresponding to the affected area to be consistent with the monitoring parameters in the monitoring intensity.

[0158] In some embodiments, in response to the monitoring intensity and / or the affected degree meeting the third monitoring condition, a monitoring expansion instruction is generated based on the monitoring intensity and / or the affected degree; the monitoring expansion instruction is sent to the intelligent gas equipment object platform through the gas company sensing network platform to control the crawling robot of the intelligent gas equipment object platform to move to the affected area and add gas monitoring equipment and / or vibration sensors.

[0159] The monitoring expansion instruction refers to an instruction for indicating the addition of gas monitoring equipment and / or vibration sensors. In some embodiments, in response to the monitoring intensity and / or the affected degree meeting the third monitoring condition, the company management platform can generate a monitoring expansion instruction based on the monitoring intensity and / or the affected degree. Among them, the third monitoring condition may include that the monitoring intensity is greater than the monitoring limit threshold and / or the affected degree is greater than the impact threshold.

[0160] In some embodiments, the monitoring limit threshold may be the highest acquisition frequency that the gas monitoring equipment can reach. In some embodiments, the impact threshold may be positively correlated with the importance of the affected area, and the higher the importance, the greater the impact threshold.

[0161] In some embodiments, in response to the affected degree meeting the third monitoring condition, the company management platform can determine the number of vibration sensors to be added based on the length of the pipeline in the affected area through the corresponding relationship between the length and the installation quantity, and evenly distribute the installation positions on the pipeline, and generate a monitoring expansion instruction based on the number and positions of the added vibration sensors. Among them, the corresponding relationship between the length and the installation quantity may include that the installation quantity is positively correlated with the length.

[0162] In some embodiments, in response to the monitoring intensity meeting the third monitoring condition, the company management platform can add gas monitoring equipment with a quantity that can meet the monitoring intensity at the positions of the original gas monitoring equipment, and generate a monitoring expansion instruction based on the positions, quantities of the added gas monitoring equipment, and the time points of obtaining data.

[0163] Exemplarily, the monitoring intensity is 2 s / time, but the monitoring upper limit threshold is 4 s / time, and the time points for the original gas monitoring equipment to obtain data are 0 s, 4 s, 8 s, …, then one gas monitoring equipment is added at the position of the original gas monitoring equipment, and the time points for the added gas monitoring equipment to obtain data are 2 s, 6 s, 10 s, …, and through cross-monitoring acquisition, a monitoring intensity of 2 s / time is achieved.

[0164] In some embodiments, the company management platform may send a monitoring expansion instruction to the intelligent gas device object platform through the gas company sensing network platform to control the crawling robot of the intelligent gas device object platform to move to the affected area and add gas monitoring devices and / or vibration sensors. Among them, based on the monitoring expansion instruction, the crawling robot may fix the installed gas monitoring devices and / or vibration sensors at the positions where they need to be installed. The crawling robot may also carry the gas monitoring devices and / or vibration sensors and wait at the positions where they need to be installed, so as to subsequently remove the installed gas monitoring devices and / or vibration sensors.

[0165] In some embodiments of this specification, by generating a monitoring expansion instruction and controlling the crawling robot to move to the affected area and add gas monitoring devices and / or vibration sensors, when the current monitoring intensity of the affected area is insufficient, devices for monitoring can be added in a timely manner to ensure effective monitoring of the affected area.

[0166] In some embodiments of this specification, by constructing a transformation map, the miscellaneous gas monitoring devices and related data can be effectively organized, and the affected degree of the affected area can be quickly and accurately determined through an impact estimation model. Furthermore, based on the affected intensity, the monitoring intensity of the gas monitoring devices in the affected area can be determined to ensure effective monitoring of the affected area.

[0167] The present invention also provides a computer-readable storage medium storing computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method described in any one of the above embodiments.

[0168] In addition, certain features, structures, or characteristics in one or more embodiments of the present invention may be appropriately combined.

[0169] In some embodiments, the numerical parameters used in the specification and claims are all approximate values, and these approximate values may change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining general digits. Although the numerical ranges and parameters used to confirm the scope breadth in some embodiments of the present invention are approximate values, in specific embodiments, such numerical settings are made as precise as possible within a feasible range.

[0170] If there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the materials cited in the present invention and the content described in the present invention, the descriptions, definitions, and / or uses of terms in the present invention shall prevail.

Claims

1. A method for transforming a smart gas pipeline network based on the supervision Internet of Things, characterized in that The method is executed by the gas company management platform of the intelligent gas pipeline network transformation system based on the supervision Internet of Things. The method includes: Obtaining gas monitoring data from the intelligent gas equipment object platform through the gas company sensing network platform, and storing the gas monitoring data in the gas database; Obtaining historical fault data through the gas database, determining transformation strategy parameters based on the historical fault data and the gas monitoring data, and uploading the transformation strategy parameters to the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensing network platform. The transformation strategy parameters include at least one of the data of the pipeline to be transformed, construction parameters, and the degree of influence of the affected area; and Generating a control instruction based on the transformation strategy parameters, and sending the control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to adjust the monitoring parameters of the gas monitoring equipment of the intelligent gas equipment object platform. The control instruction includes an indirect control instruction; Constructing a transformation map based on the gas monitoring data, the data of the pipeline to be transformed, and the construction parameters. The transformation map is a graph structure representing the association relationship of multiple gas monitoring devices; Determining the degree of influence through an influence estimation model based on the transformation map. The influence estimation model is a machine learning model; Determining the monitoring intensity corresponding to the affected area based on the degree of influence; and Generating the indirect control instruction based on the monitoring intensity, and sending the indirect control instruction to the intelligent gas equipment object platform through the gas company sensing network platform to adjust the monitoring parameters of the gas monitoring equipment corresponding to the affected area.

2. The method according to claim 1, wherein The method further includes: Obtaining historical monitoring data through the gas database, and determining an evaluation parameter sequence through an evaluation model based on the historical monitoring data, the historical fault data, pipeline data, and the gas monitoring data. The evaluation model is a machine learning model; Determining the pipeline to be transformed in response to the evaluation parameter sequence meeting a preset condition; Determining the construction parameters based on the pipeline to be transformed and the evaluation parameter sequence; and In response to the pipeline health score of the pipeline to be transformed meeting a first monitoring condition, generating a leakage monitoring instruction based on the pipeline health score, and sending the leakage monitoring instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the crawler robot of the intelligent gas equipment object platform to move to the pipeline to be transformed and perform leakage monitoring.

3. The method according to claim 2, characterized in that, The evaluation parameter sequence includes predicted evaluation parameters. The evaluation model includes a prediction layer and an evaluation layer. The step of obtaining historical monitoring data through the gas database and determining an evaluation parameter sequence through an evaluation model based on the historical monitoring data, the historical fault data, pipeline data, and the gas monitoring data includes: Constructing a pipeline map based on the historical monitoring data, the historical fault data, the gas monitoring data, future weather data, the evaluation parameter sequence, and the pipeline data; Based on the pipeline map, determine the estimated monitoring data and the estimated fault data through the prediction layer; and Based on the estimated monitoring data, the estimated fault data, the historical monitoring data, the historical fault data, the gas monitoring data, and the pipeline data, determine the estimated evaluation parameters through the evaluation layer.

4. The method according to claim 3, characterized in that, The method further includes: In response to the estimated fault data satisfying the second monitoring condition, generate a fault monitoring instruction based on the estimated fault data, and send the fault monitoring instruction to the intelligent gas device object platform through the gas company sensing network platform, so as to control the crawling robot of the intelligent gas device object platform to move to the pipeline to be monitored and perform mobile patrol.

5. A smart gas pipeline network transformation system based on the supervision Internet of Things, characterized in that The system includes an intelligent gas government safety supervision and management platform, an intelligent gas government safety supervision sensing network platform, an intelligent gas government safety supervision object platform, a gas company sensing network platform, and an intelligent gas device object platform. The intelligent gas government safety supervision object platform includes a gas company management platform; the gas company management platform is configured to: Through the gas company sensing network platform, obtain gas monitoring data from the intelligent gas device object platform, and store the gas monitoring data in the gas database; Obtain historical fault data through the gas database, determine retrofit strategy parameters based on the historical fault data and the gas monitoring data, and upload the retrofit strategy parameters to the intelligent gas government safety supervision and management platform through the intelligent gas government safety supervision sensing network platform. The retrofit strategy parameters include at least one of the pipeline data to be retrofitted of the pipeline to be retrofitted, construction parameters, and the affected degree of the affected area; and Based on the retrofit strategy parameters, generate a regulation instruction, and send the regulation instruction to the intelligent gas device object platform through the gas company sensing network platform to regulate the monitoring parameters of the gas monitoring device associated with the intelligent gas device object platform. The regulation instruction includes an indirect regulation instruction; Based on the gas monitoring data, the pipeline data to be retrofitted, and the construction parameters, construct a retrofit map, where the retrofit map is a graph structure representing the association relationship of multiple gas monitoring devices; Based on the retrofit map, determine the affected degree through an impact estimation model, where the impact estimation model is a machine learning model; Based on the affected degree, determine the monitoring intensity corresponding to the affected area; And Generate the indirect regulation instruction based on the monitoring intensity, and send the indirect regulation instruction to the intelligent gas device object platform through the gas company sensing network platform to regulate the monitoring parameters of the gas monitoring device corresponding to the affected area.

6. The system according to claim 5, characterized in that The gas company management platform is further configured to: Obtain historical monitoring data through the gas database, and determine an evaluation parameter sequence through an evaluation model based on the historical monitoring data, the historical fault data, the pipeline data, and the gas monitoring data. The evaluation model is a machine learning model; In response to the evaluation parameter sequence satisfying a preset condition, determine the pipeline to be retrofitted; Determine the construction parameters based on the pipeline to be renovated and the sequence of evaluation parameters; and In response to the pipeline health score of the pipeline to be renovated meeting the first monitoring condition, generate a leakage monitoring instruction based on the pipeline health score, and send the leakage monitoring instruction to the intelligent gas equipment object platform through the gas company sensing network platform to control the crawler robot of the intelligent gas equipment object platform to move to the pipeline to be renovated and perform leakage monitoring.

7. The system according to claim 6, wherein The sequence of evaluation parameters includes estimated evaluation parameters, the evaluation model includes a prediction layer and an evaluation layer, and the gas company management platform is further configured to: Construct a pipeline atlas based on the historical monitoring data, the historical fault data, the gas monitoring data, the future weather data, the sequence of evaluation parameters, and the pipeline data; Determine the estimated monitoring data and the estimated fault data through the prediction layer based on the pipeline atlas; and Determine the estimated evaluation parameters through the evaluation layer based on the estimated monitoring data, the estimated fault data, the historical monitoring data, the historical fault data, the gas monitoring data, and the pipeline data.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the method according to claim 1.

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