Intelligent gas pipe network transformation method and system based on supervision Internet of Things and medium

Abstract

The invention provides an intelligent gas pipe network transformation method and system based on the supervision Internet of Things and a medium, and relates to the field of pipe network transformation, the method is executed by a gas company management platform of the intelligent gas pipe network transformation system based on the supervision Internet of Things, and the method comprises the following steps: through a gas company sensing network platform, from an intelligent gas equipment object platform, a gas supply network is established; acquiring gas monitoring data; acquiring historical fault data through the gas database, and determining transformation strategy parameters based on the historical fault data and the gas monitoring data; and on the basis of the transformation strategy parameters, generating a regulation and control instruction, and sending the regulation and control instruction to an intelligent gas equipment object platform through a gas company sensing network platform so as to regulate and control monitoring parameters of gas monitoring equipment of the intelligent gas equipment object platform. The use state of the gas pipeline can be accurately monitored in real time, so that the pipeline needing to be transformed can be found and effectively treated in time, and the overall safety performance of the gas pipeline is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of pipeline network reconstruction, and in particular to a smart gas pipeline network reconstruction method, system and medium based on a supervisory Internet of Things. Background Art

[0002] As the age of gas pipelines increases, some old residential areas with long-term use of gas pipelines have problems such as aging facilities and frequent failures, which lead to safety hazards. When renovating old pipelines, it is necessary to determine the location of the pipeline section to be renovated in the pipeline network and the renovation method, so as to achieve pipeline renovation under the premise of ensuring the overall stable operation of the pipeline network.

[0003] Therefore, we hope to propose a smart gas pipeline network transformation method, system and medium based on the supervision of the Internet of Things to monitor the usage status of the gas pipeline in real time and accurately, so as to promptly discover and effectively deal with the pipelines that need to be transformed, thereby significantly improving the overall safety performance of the gas pipeline. Summary of the invention

[0004] The invention content includes a smart gas pipeline network transformation method based on the Internet of Things, which is executed by a gas company management platform of a smart gas pipeline network transformation system based on the supervision of the Internet of Things, including: obtaining gas monitoring data from a smart gas equipment object platform through a gas company sensor network platform, and storing the gas monitoring data in a 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 a smart gas government safety supervision management platform through a smart gas government safety supervision sensor network platform, wherein the transformation strategy parameters include at least one of the pipeline data to be modified and construction parameters of the pipeline to be modified; and generating control instructions based on the transformation strategy parameters, and sending the control instructions to the smart gas equipment object platform through the gas company sensor network platform to control 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 a regulatory Internet of Things, the system including: 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 the gas database; obtain historical fault data through the gas database, determine the 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 pipeline data to be modified and the construction parameters of the pipeline to be modified; and generate control instructions based on the transformation strategy parameters, and send the control instructions to the smart gas equipment object platform through the gas company sensor network platform to control 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, which stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes any one of the methods for smart gas pipeline network transformation based on the Internet of Things.

[0007] The beneficial effects of the present invention include but are not limited to: (1) Through the intelligent gas pipeline network transformation system based on the supervision of the Internet of Things, an information operation closed loop can be formed between the various functional platforms, and coordinated and regular operation can be achieved to realize the informatization and intelligence of the intelligent gas pipeline network transformation. (2) By monitoring the temperature, humidity and other conditions 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 determined according to the gas database to ensure the safety of gas transportation. (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. By using a crawling robot to monitor the leakage of gas pipelines that may leak, it is possible to respond to the leakage of the gas pipeline in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in the form of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a schematic diagram of the platform structure of a smart gas pipe network transformation system based on the supervision Internet of Things shown in some embodiments of this specification; Figure 2 is an exemplary flow chart of a smart gas network transformation method based on the Internet of Things according to some embodiments of this specification; Figure 3 is an exemplary schematic diagram of an evaluation model according to some embodiments of this specification; Figure 4 is an exemplary schematic diagram of an impact estimation model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings do not represent all implementation methods.

[0010] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the words can be replaced by other expressions.

[0011] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0012] When the operations performed in the embodiments of the present invention are described in steps, unless otherwise specified, the order of the steps is interchangeable, steps may be omitted, and other steps may be included in the operation process.

[0013] Figure 1 It is a schematic diagram of the platform structure of a smart gas pipe network transformation system based on the supervision of the Internet of Things as shown in some embodiments of this specification.

[0014] like Figure 1 As shown, the smart gas pipeline network transformation system 100 based on the regulatory Internet of Things may include a smart gas government safety supervision management platform 110, a smart gas government safety supervision sensor network platform 120, a smart gas government safety supervision object platform 130, a gas company sensor network platform 140, a smart gas equipment object platform 150 and a gas user object platform 160.

[0015] Smart gas government safety supervision management platform 110 refers to a comprehensive management platform for government management information.

[0016] In some embodiments, the smart gas government safety supervision management platform 110 can interact with the smart gas government safety supervision sensor network platform 120.

[0017] The smart gas government safety supervision sensor network platform 120 refers to a platform for comprehensive management of government sensor information. The smart gas government safety supervision sensor network platform 120 can be configured as a communication network or a gateway, etc.

[0018] In some embodiments, the smart gas government safety supervision sensor network platform 120 may interact with the gas company management platform 131. For example, the smart gas government safety supervision sensor network platform 120 may obtain the transformation strategy parameters uploaded by the gas company management platform 131.

[0019] The smart gas government safety supervision object platform 130 refers to a platform for generating government supervision information and controlling information execution. In some embodiments, the smart gas government safety supervision object platform 130 may include a gas company management platform 131.

[0020] The gas company management platform 131 refers to a comprehensive management platform for gas company information.

[0021] In some embodiments, the gas company management platform 131 can be configured to obtain gas monitoring data and store the gas monitoring data in a gas database; determine transformation strategy parameters based on historical fault data and gas monitoring data; generate control instructions based on the transformation strategy parameters, and send the control instructions to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

[0022] The gas database is used to store information and / or data related to the smart gas pipe network transformation system 100 based on the supervision Internet of Things. For example, the gas database can store gas monitoring data, historical fault data, and historical monitoring data.

[0023] In some embodiments, the gas company management platform 131 can 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 control instructions; generate and send monitoring expansion instructions.

[0024] The gas company sensor network platform 140 is a platform for comprehensively managing the sensor information of the gas company. In some embodiments, the gas company sensor network platform 140 can be configured as a communication network or a gateway, etc. The gas company sensor network platform 140 can interact with the gas company management platform 131.

[0025] The smart gas equipment object platform 150 refers to a functional platform for sensing information generation and controlling information execution. In some embodiments, the smart gas equipment object platform 150 can interact with the gas company sensor network platform 140 .

[0026] In some embodiments, the smart gas equipment object platform may include a gas monitoring device and a crawling robot. The smart gas equipment object platform may communicate and / or be physically connected with the gas monitoring device and the crawling robot, and may also control the gas monitoring device and the crawling robot 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 meter, 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 device management platform may be wirelessly connected to the crawling robot.

[0027] In some embodiments, the smart gas network transformation system 100 based on the regulatory Internet of Things may also include a server. Each platform may be set on the server and connected via network communication. The server may process information and / or data related to the smart gas network transformation system 100 based on the regulatory 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 may store a gas database. A gas database refers to a database management system that supports high concurrent access.

[0028] For detailed description of the above, please refer to Figures 2 to 4 Related description.

[0029] In some embodiments of the present specification, through the smart gas pipe network transformation system 100 based on the supervision of the Internet of Things, an information operation closed loop can be formed between various functional platforms, and coordinated and regular operations can be performed to realize the informatization and intelligence of the smart gas pipe network transformation.

[0030] Figure 2 This is an exemplary flow chart of a smart gas network transformation method based on the Internet of Things according to some embodiments of this specification. In some embodiments, the process 200 of the smart gas network transformation method based on the Internet of Things is executed by a gas company management platform (hereinafter referred to as the company management platform) based on the smart gas network transformation system for supervising the Internet of Things. Figure 2 As shown, the process 200 of the smart gas pipeline network transformation method based on the Internet of Things includes the following steps: In some embodiments, the company management platform can 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 the gas database. The company management platform can obtain historical fault data through the gas database, determine the transformation strategy parameters based on the historical fault data and 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 company management platform can generate control instructions based on the transformation strategy parameters, and send the control instructions to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

[0031] For relevant content about each platform of the smart gas pipeline network transformation system based on the supervision of the Internet of Things, please refer to Figure 1 The corresponding description.

[0032] Step 210, obtain gas monitoring data from the smart gas equipment object platform through the gas company's sensor network platform, and store the gas monitoring data in the gas database.

[0033] Gas monitoring data refers to 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.

[0034] In some embodiments, gas monitoring data can be collected by multiple gas monitoring devices arranged inside and / or outside the gas pipeline and sent to the smart gas device object platform. The company management platform can obtain gas monitoring data from the smart gas device object platform and store the gas monitoring data in the gas database. For a description of gas monitoring devices, see Figure 1 and its related description.

[0035] In some embodiments, the company management platform can obtain gas monitoring data from the smart gas equipment object platform through the gas company sensor network platform.

[0036] Step 220, obtain historical fault data through the gas database, determine the transformation strategy parameters based on the historical fault data and 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.

[0037] The transformation strategy parameters refer to parameters used to guide pipeline transformation. In some embodiments, the transformation strategy parameters may include pipeline data to be transformed and construction parameters.

[0038] The pipeline data to be modified refers to data related to the pipeline to be modified, such as aging pipelines and pipelines with frequent failures. 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 modified.

[0039] The construction parameters refer to the parameters for the transformation construction of the pipeline to be transformed. In some embodiments, the construction parameters may include the transformation method of the pipeline to be transformed. The transformation method may include replacing the pipeline, repairing the pipeline, and replacing accessories.

[0040] Pipeline replacement means digging out the original pipeline and replacing it with a new one. Pipeline repair means repairing the old pipeline without digging it out. Accessory replacement means replacing the accessories without digging out the old pipe.

[0041] Historical fault data refers to the fault data of the gas pipeline in the historical time period. In some embodiments, the fault data may include the fault location, fault type, fault time, etc. The fault type may include leakage and corrosion, etc.

[0042] In some embodiments, the company management platform can obtain historical fault data through the gas database. When a gas pipeline fails, the smart gas equipment object platform can upload the fault data to the gas database.

[0043] In some embodiments, the company management platform may determine transformation strategy parameters based on historical fault data and gas monitoring data.

[0044] In some embodiments, the company management platform can determine the pipeline to be modified in a variety of ways based on historical fault data and gas monitoring data. For example, based on the historical fault data of the gas pipeline, the company management platform can count the number and / or frequency of failures of the gas pipeline in a historical time period, and determine whether the number and / or frequency of failures meet the fault condition, and in response to the number and / or frequency of historical failures meeting the fault condition, determine the gas pipeline as a pipeline to be modified. The preset fault condition includes the number and / or frequency being greater than the fault threshold. The fault threshold may include thresholds corresponding to the number and frequency, and the fault threshold may be preset based on historical experience.

[0045] For another example, the company management platform can count the change range of the gas composition of the gas pipeline within a preset period of time based on the change of the gas composition data in the gas monitoring data, and determine whether the change range of the gas composition data meets the preset fault condition. In response to the change range of the gas composition meeting the preset fault condition, the gas pipeline is determined to be a pipeline to be modified. Among them, the preset fault condition includes that the change range of the gas composition data is greater than the change threshold, and the change threshold and the preset period of time can be preset based on historical experience. The gas composition can be collected and obtained by the gas analyzer in the gas monitoring equipment.

[0046] In some embodiments, the company management platform can determine the construction parameters in a variety of ways based on historical fault data and gas monitoring data. For example, the company management platform can count the number and / or frequency of gas pipeline failures in a historical time period based on historical fault data of the gas pipeline, query the first construction parameter corresponding to the number and / or frequency in the first preset table, and determine the first construction parameter as the construction parameter.

[0047] In some embodiments, the first preset table can be pre-set based on historical experience, including multiple groups of times and / or frequencies and the first construction parameters corresponding to each group of times and / or frequencies. The higher the number and / or frequency of a group, the more inclined the corresponding first construction parameter is to be set to replace the pipeline.

[0048] For another example, the company management platform can determine the change range of the gas composition in the gas monitoring data based on the gas monitoring data, query the second construction parameter corresponding to the change range in the second preset table, and determine the second construction parameter as the construction parameter.

[0049] In some embodiments, the second preset table can be preset based on historical experience, including the historical change amplitudes of multiple groups of gas components in the historical data and the number of second construction parameters corresponding to each group of historical change amplitudes. The larger the historical change amplitude of a group, the more inclined its corresponding second construction parameter is to be set to replace the pipeline.

[0050] In some embodiments, the company management platform can determine the construction parameters based on the pipeline to be modified and the evaluation parameter sequence. For more information about this section, please refer to Figure 3 The corresponding description.

[0051] Step 230, based on the transformation strategy parameters, generates a control instruction, and sends the control instruction to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

[0052] Control instructions refer to instructions used to control the monitoring parameters of gas monitoring equipment.

[0053] Monitoring parameters refer to parameters related to the operation of the gas monitoring device. In some embodiments, the detection parameters may include the monitoring frequency and monitoring accuracy of the gas monitoring device.

[0054] In some embodiments, the company management platform can increase the monitoring frequency and monitoring accuracy of the gas monitoring equipment of the pipeline to be modified based on the transformation strategy parameters, and generate control instructions based on the gas monitoring equipment and the updated monitoring frequency and monitoring accuracy, and send the control instructions to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the control instructions.

[0055] It is understandable that since accidents may occur in the pipeline to be renovated, it is necessary to improve the monitoring frequency and accuracy of the gas monitoring equipment to avoid the deterioration of the gas pipeline before the renovation and the inability to respond in time, which will facilitate the timely adjustment of construction parameters when the situation deteriorates.

[0056] In some embodiments, the management platform can generate indirect control instructions based on the monitoring intensity, and send the indirect control instructions to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the affected area. Figure 4 and its related description.

[0057] In some embodiments of the present specification, by monitoring the temperature, humidity and other conditions 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 modified can be discovered in time, and the appropriate modification method can be determined based on the gas database to ensure the safety of gas transportation.

[0058] It should be noted that the above description of the process 200 of the method for renovating a smart gas network based on the Internet of Things is only for example and explanation, and does not limit the scope of application of the present invention. For those skilled in the art, various modifications and changes can be made to the process 200 of the method for renovating a smart gas network based on the Internet of Things under the guidance of the present invention. However, these modifications and changes are still within the scope of the present invention.

[0059] Figure 3 is an exemplary schematic diagram of an evaluation model according to some embodiments of the present specification.

[0060] In some embodiments, the company management platform can obtain historical monitoring data 311 through the gas database, and determine the evaluation parameter sequence 330 through the 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 satisfying the preset conditions, the company management platform can determine the pipeline to be modified. The company management platform can determine the construction parameters based on the pipeline to be modified and the evaluation parameter sequence. In response to the pipeline health score of the pipeline to be modified satisfying the 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 smart gas equipment object platform through the gas company sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the pipeline to be modified and perform leakage monitoring. For instructions on historical fault data and gas monitoring data, please refer to Figure 2 and its related description.

[0061] Historical monitoring data refers to gas monitoring data collected by gas monitoring equipment in historical time. In some embodiments, the company management platform can obtain historical monitoring data through a gas database.

[0062] An evaluation parameter sequence refers to a sequence consisting of multiple evaluation sequences. An evaluation parameter sequence may include multiple historical evaluation parameters and current evaluation parameters. An evaluation parameter refers to a parameter for evaluating the operation status of a pipeline.

[0063] In some embodiments, the evaluation parameters may include transmission fluctuation, corrosion degree, potential risk, and confidence level of potential risk. The transmission fluctuation may characterize the fluctuation of the flow rate of gas transported by the gas pipeline. The transmission fluctuation, corrosion degree, and confidence level of potential risk may all be expressed in percentages or the like. The larger the percentage of the transmission fluctuation, the greater the fluctuation of the flow rate of gas transported by the gas pipeline.

[0064] In some embodiments, potential risks may include at least one of leakage risk, stress risk, geological risk, interface risk and fracture risk. Each risk corresponds to a confidence level. Leakage risk refers to the possibility of gas leakage. Stress risk refers to the risk caused by stress changes caused by factors such as temperature changes and pressure fluctuations. Geological risk refers to the risk caused by changes in geological conditions in the area where the pipeline is located. Interface risk refers to the risk of sealing and stability at the pipeline interface. Fracture risk refers to the risk of pipeline fracture due to pipeline aging, corrosion and other reasons.

[0065] In some embodiments, the company management platform can determine the evaluation parameter sequence through the evaluation model based on historical monitoring data, historical fault data, pipeline data, and gas monitoring data. The company management platform can use multiple evaluation parameters determined by the evaluation model at historical time as multiple historical evaluation parameters, and form an evaluation parameter sequence with the current evaluation parameters.

[0066] Pipeline data refers to data related to the gas pipeline. In some embodiments, the pipeline data may include pipeline parameter data, pipeline interface data, and geological parameter data. If the gas pipeline is on the 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.

[0067] The 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.

[0068] The pipeline interface data refers to data related to the interface of the gas pipeline. In some embodiments, the pipeline interface data may include the type and status of the pipeline interface.

[0069] The 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.

[0070] The evaluation model refers to a model used to determine the 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.

[0071] In some embodiments, the company management platform may train the evaluation model based on a large number of first training samples with first labels by gradient descent method, etc. 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 label of the first training sample may be an actual evaluation parameter.

[0072] In some embodiments, the first training sample can be obtained based on historical data. The first label can be determined based on manual annotation. For example, the transmission fluctuation and corrosion degree in the first label can be obtained by manually measuring the gas pipeline using flow meters, corrosion testers and other measuring instruments, and the potential risks and the confidence level of the potential risks can be obtained by manual field investigation and evaluation.

[0073] In some embodiments, the evaluation model can be trained in the following manner: input a plurality of first training samples with first labels into an initial evaluation model, construct a loss function through the first labels and the prediction results of the initial evaluation model, update the initial evaluation model based on the iteration of the loss function, and complete the evaluation model training when the loss function of the initial evaluation model satisfies a preset loss condition. The preset loss condition may be that the loss function converges, the number of iterations reaches a set value, etc.

[0074] In some embodiments, in response to the evaluation parameter sequence satisfying a preset condition, the pipeline to be modified is determined.

[0075] The preset condition refers to a condition used to determine whether the gas pipeline needs to be modified. In some embodiments, the preset condition can be pre-set based on historical experience, including multiple parameter thresholds. Each parameter threshold corresponds to a data in the evaluation parameter.

[0076] In some embodiments, the company management platform can determine the pipeline to be modified based on the current evaluation parameters. For example, if any of the evaluation parameters of the transmission fluctuation, corrosion degree, and confidence level of potential risk exceeds the corresponding parameter threshold in the preset conditions, the company management platform can determine the gas pipeline as a pipeline to be modified.

[0077] In some embodiments, the preset conditions may further include a health score threshold. The company management platform may 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 conditions, determine the gas pipeline as a pipeline to be modified. The health score threshold may be preset based on historical experience.

[0078] The pipeline health score can characterize the extent to which a gas pipeline may fail. The lower the pipeline health score, the more likely the gas pipeline is to fail.

[0079] In some embodiments, the pipeline health score may be related to the transmission fluctuation, the degree of corrosion, the potential risk and its confidence. For example, the pipeline health score may be negatively correlated to the transmission fluctuation, the degree of corrosion, the potential risk and its confidence. As an example only, the company management platform may determine the pipeline health score by the following formula (1): H=100-(a1×S1+a2×S2+a3×S3+a4×S4+a5×S5+a6×S6+a7×S7) (1)

[0080] Among them, H is the pipeline health score, S1 is the transmission fluctuation, S2 is the corrosion degree, S3 is the leakage risk confidence, S4 is the stress risk confidence, S5 is the geological risk confidence, S6 is the interface risk confidence, S7 is the fracture risk confidence, and a1-a7 are coefficients. Among them, a1-a7 can be pre-set based on historical experience.

[0081] In some embodiments, the company management platform may determine the pipeline to be modified based on the evaluation parameter sequence. For example, the company management platform may determine multiple historical pipeline health scores based on multiple historical evaluation parameters in the evaluation parameter sequence, and determine the gas pipeline as a pipeline to be modified in response to the average change amplitude of the multiple historical pipeline health scores being greater than a preset amplitude threshold. The change amplitude may be represented by the difference between the historical pipeline health score at the previous moment and the historical pipeline health score at the next moment. The preset amplitude threshold may be preset based on historical experience.

[0082] In some embodiments, the company management platform can query the historical evaluation parameters that are the same as the evaluation parameters in the preset parameter table based on the pipeline to be transformed 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 transformed. The preset parameter table can be pre-set based on historical data, including multiple historical evaluation parameters and reference construction parameters corresponding to each historical evaluation parameter. The reference construction parameters can be construction parameters actually used for transformation in historical time.

[0083] The leakage monitoring instruction refers to an instruction used to instruct to monitor whether a gas pipeline leaks.

[0084] In some embodiments, in response to the pipeline health score of the pipeline to be modified meeting the first monitoring condition, the company management platform can generate a leakage monitoring instruction based on the pipeline health score. 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.

[0085] In some embodiments, the gas company sensor network platform sends a leakage monitoring instruction to the smart gas equipment object platform to control the crawling robot of the smart gas equipment object platform to move to the pipeline to be modified and perform leakage monitoring. The crawling robot can be equipped with leakage monitoring equipment and crawl outside the pipeline to perform leakage monitoring.

[0086] Leakage monitoring equipment refers to equipment used to monitor the gas content outside the pipeline, such as gas leak detectors and gas detectors.

[0087] In some embodiments of this specification, based on the evaluation parameters of the gas pipeline, the pipelines to be modified in the gas pipeline network and the corresponding construction parameters are quickly determined, which can ensure the accuracy and smooth progress of the gas pipeline network modification. By monitoring the leakage of the gas pipeline that may leak through the crawling robot, the leakage of the gas pipeline can be responded to in time.

[0088] In some embodiments, the evaluation parameter sequence may further include an estimated evaluation parameter, and the evaluation model may further include a prediction layer and an evaluation layer. The company management platform may 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; based on the pipeline map, determine the estimated monitoring data and estimated fault data through the prediction layer; and based on the estimated monitoring data, estimated fault data, historical monitoring data, historical fault data, gas monitoring data, and pipeline data, determine the estimated evaluation parameter through the evaluation layer.

[0089] In some embodiments, the prediction layer and the evaluation layer can be trained separately.

[0090] The pipeline graph refers to a graph structure that represents the relationship between gas monitoring equipment and pipeline interfaces, etc. The graph structure is a data structure composed of nodes and edges, where edges connect nodes and nodes and edges can have features.

[0091] In some embodiments, the company management platform can construct a pipeline map based on the connection relationship between the gas monitoring equipment and the pipeline interface. The nodes of the pipeline map can include interface nodes and device nodes. Among them, the interface node refers to the node indicating the location of the pipeline interface. The device node refers to the node indicating the location of the gas monitoring equipment.

[0092] In some embodiments, the node features may 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.

[0093] The edges of the pipeline graph can represent the connectivity between nodes. In some embodiments, the edges of the pipeline graph can correspond to the gas pipelines connected between the nodes. The edge features corresponding to the edges include historical fault data, future weather data, evaluation parameter sequences, etc. of the pipelines corresponding to the edges. The direction of the edge is the direction of gas flow.

[0094] Future weather data refers to the weather conditions in the future. For example, the company management platform can obtain future weather data through external meteorological platforms and weather forecasts.

[0095] 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 combination of a Graph Neural Networks (GNN) model or other custom model structures.

[0096] In some embodiments, the output of the prediction layer may include estimated monitoring data output by device nodes and estimated fault data output by edges.

[0097] The estimated monitoring data refers to the gas monitoring data estimated at a future time. In some embodiments, the estimated monitoring data may include gas monitoring data at multiple future time points. The multiple future time points may be preset based on historical experience.

[0098] The estimated fault data refers to the type of fault that is estimated to occur in the future. In some embodiments, the estimated fault data may include multiple types of faults that may occur at multiple future time points. If it is estimated that the gas pipeline will not fail at a future time point, the estimated fault data is empty.

[0099] In some embodiments, the company management platform can obtain a prediction layer based on a large number of second training samples with second labels through training such as gradient descent. The second training sample can include a sample pipeline map. The second label can be gas monitoring data and fault data actually obtained at a historical time point. The second training sample and the second label can be obtained based on historical data. For an explanation of fault data, see Figure 2 and its related descriptions. The historical time point refers to the time point corresponding to the estimated monitoring data and estimated fault data output by the prediction layer at the historical time. For example, if the estimated monitoring data and estimated fault data output by the prediction layer at 10 o'clock correspond to 15 o'clock, then the historical time point is 15 o'clock.

[0100] In some embodiments, the training process of the prediction layer is similar to the training process of the above-mentioned evaluation model, and its implementation method can refer to the training method of the above-mentioned evaluation model.

[0101] 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 smart gas equipment object platform through the gas company sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the pipeline to be monitored and perform mobile patrol.

[0102] The pipeline to be monitored refers to the gas pipeline corresponding to the estimated fault data that meets the second monitoring condition.

[0103] The fault monitoring instruction is an instruction for instructing to perform mobile patrol. In some embodiments, the fault monitoring instruction can be configured to make the crawling robot in the pipeline to be monitored perform reciprocating patrol with a preset length centered on its own position, and monitor whether the pipeline to be monitored has a fault during the mobile patrol.

[0104] 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 to the detection sensitivity of the crawling robot and the density of the fault point distribution. The lower the detection sensitivity, the lower the density of the fault point distribution, and the longer the preset length.

[0105] Detection sensitivity refers to the ability of the crawler robot to detect the smallest fault signal. Fault point distribution refers to the distribution of fault locations of gas pipelines in historical time. In some embodiments, the company management platform can generate a fault point distribution based on the fault locations in historical fault data. The shorter the distance between the fault locations, the higher the density of the fault point distribution. The detection sensitivity can be uploaded by the gas company to the company management platform.

[0106] In some embodiments of the present specification, the patrol length can be reasonably arranged through the detection sensitivity of the crawling robot and the distribution of fault points. Under the premise of ensuring the effectiveness of monitoring, 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.

[0107] In some embodiments, in response to the estimated fault data satisfying the second monitoring condition, the company management platform may generate a fault monitoring instruction based on the estimated fault data, wherein the second monitoring condition may include that the estimated fault data is not empty.

[0108] In some embodiments, the company management platform can send fault monitoring instructions to the smart gas equipment object platform, control the crawling robot of the smart gas equipment object platform to move to the pipeline to be monitored and perform mobile patrol.

[0109] In some embodiments of the present specification, since gas leaks are often dynamic, if the crawling robot is fixed in one position for detection, untimely detection and slow response may occur. By analyzing the estimated fault data and generating fault monitoring instructions, the crawling robot can be timely controlled to move to the pipeline to be monitored and perform mobile patrols, thereby expanding the monitoring range and reducing monitoring blind spots, thereby discovering potential faults in advance.

[0110] The evaluation layer refers to a model used to determine the estimated evaluation parameters. In some embodiments, the evaluation layer may be a machine learning model. For example, the evaluation layer may include any one or combination of a recurrent neural network (RNN) model or other custom model structures.

[0111] 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. The historical monitoring data includes current gas monitoring data and estimated monitoring data output by the prediction layer. The historical fault data includes estimated fault data output by the prediction layer.

[0112] The estimated evaluation parameter refers to the estimated evaluation parameter of the future time. In some embodiments, the estimated evaluation parameter may include evaluation parameters of multiple future time points.

[0113] In some embodiments, the company management platform may determine the estimated evaluation parameters of each future time point in sequence through the evaluation layer. When determining a future time point, the historical monitoring data and historical fault data input into the evaluation layer may include the estimated monitoring data and estimated fault data corresponding to the future time point, as well as the estimated monitoring data and estimated fault data of the future time point before the future time point.

[0114] Exemplarily, when the company management platform determines the estimated evaluation parameters of the second future time point through the evaluation layer, the historical monitoring data and historical fault data input into the evaluation layer may also include the estimated monitoring data and estimated fault data of the second future time point and the estimated monitoring data and estimated fault data of the first future time point, wherein the second future time point is later than the first future time point.

[0115] In some embodiments, the training method and process of the evaluation layer are similar to the training method and process of the above-mentioned evaluation model, and its implementation method and process can refer to the training method and process of the above-mentioned evaluation model. The difference is that the company management platform can use a third training sample and a third label, and 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 may include actual evaluation parameters at a future time point. Among them, the sample estimated monitoring data and the sample estimated fault data can be obtained through the prediction layer. For instructions on obtaining sample historical monitoring data, sample historical fault data, sample pipeline data, sample gas monitoring data, and actual evaluation parameters, please refer to the relevant description above.

[0116] In some embodiments, for a gas pipeline that has not been determined as a pipeline to be modified, the company management platform can determine an estimated pipeline health score corresponding to the estimated evaluation parameter based on the estimated evaluation parameter, 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 modified. For more information about pipeline health scores and health score thresholds, please refer to the relevant description above.

[0117] In some embodiments, in response to the average decrease in 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 modified. As an 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 decrease as the average value. For more information about the preset amplitude threshold, please refer to the relevant description above.

[0118] In some embodiments, the transformation strategy parameters may also include a transformation time point of the pipeline to be transformed. The transformation time point refers to a time point at which the pipeline to be transformed is transformed.

[0119] In some embodiments, the company management platform may determine the time point when the estimated pipeline health score is lower than the health score threshold for the first time as the transformation time point based on the estimated pipeline health scores corresponding to multiple future time points.

[0120] In some embodiments, the company management platform can query the historical evaluation parameters that are the same as the estimated evaluation parameters in the preset parameter table based on the estimated evaluation parameters at the transformation time point and the pipeline to be transformed, and determine the reference construction parameters corresponding to the historical evaluation parameters as the construction parameters of the pipeline to be transformed. For an explanation of the preset parameter table, see Figure 2 and its related description.

[0121] In some embodiments of the 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, the hierarchical evaluation model can quickly and accurately determine the estimated evaluation parameters, reduce the difficulty of training the evaluation model, improve the evaluation model, and make it easy to adjust and maintain.

[0122] Figure 4 is an exemplary schematic diagram of an impact estimation model according to some embodiments of the present specification.

[0123] In some embodiments, the transformation strategy parameters may further include the degree of impact of the affected area, and the control instructions may further include indirect control instructions. The company management platform may construct a transformation map 420 based on the gas monitoring data 314, the pipeline data to be modified 412, and the construction parameters 413; based on the transformation map 420, the degree of impact 440 is determined through the impact estimation model 430. The company management platform may also determine the monitoring intensity corresponding to the affected area based on the degree of impact 440; generate indirect control instructions based on the monitoring intensity, and send the indirect control instructions to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the affected area. For instructions on gas monitoring data, pipeline data to be modified, and construction parameters, please refer to Figure 2 as well as Figure 3 and its related description.

[0124] Indirect control instructions refer to instructions used to control the monitoring parameters of gas monitoring equipment.

[0125] The degree of impact of the affected area is used to characterize the degree of impact on the affected area when the pipeline to be modified is constructed. The affected area refers to the area affected by the gas pipeline network modification. In some embodiments, the affected area may include the pipeline to be modified and the area where the gas pipeline that may be affected is located. For example, the affected area may include the pipeline to be modified and the pipelines around the pipeline to be modified.

[0126] The transformation graph refers to a graph structure that represents the association relationship between different gas monitoring devices.

[0127] In some embodiments, the company management platform can construct a transformation map based on the connection relationship of multiple gas monitoring devices. The nodes of the transformation map can include monitoring device nodes (such as node 421), and the monitoring device nodes represent the location of the gas monitoring device. The node characteristics of the monitoring device nodes can include historical monitoring data and gas monitoring data corresponding to the gas monitoring device. For an explanation of historical monitoring data, see Figure 2 and its related description.

[0128] The edges of the transformation graph can represent the connectivity between nodes and are directed edges, and the direction of the edge is the direction of gas flow. In some embodiments, the edges of the transformation graph can include transformation edges and general edges, both of which are gas pipelines connected between nodes. Among them, the transformation edge represents the pipeline to be transformed, and the general edge represents the gas pipeline that is not the pipeline to be transformed.

[0129] 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 evaluation parameter sequence. For information about pipeline parameter data, geological parameter data, historical fault data, and evaluation parameter sequence, see Figure 2 , Figure 3 and its related description.

[0130] In some embodiments, the features of the modified edge and the features of the general edge may further include a sequence of evaluation parameters of the gas pipeline corresponding to the edge.

[0131] In some embodiments of the present specification, since the evaluation parameters can reflect the actual condition of the gas pipeline, introducing the evaluation parameters in the transformation map can more accurately estimate which areas will be affected by the transformation and the degree of impact, thereby avoiding underestimation of the degree of impact on the affected area and causing damage to the affected area.

[0132] The impact estimation model refers to a model used to determine the degree of impact, and in some embodiments, the impact estimation model may be a machine learning model. For example, the impact estimation model may include any one or combination of a graph neural network (GNN) model or other custom model structures.

[0133] In some embodiments, the input of the impact estimation model may include a transformation map, and the output may include an impact area and the degree of impact of the impact area.

[0134] In some embodiments, the impact estimation model can be obtained based on training of a training sample set. The training sample set includes multiple training samples and labels corresponding to each training sample. Each training sample includes a sample transformation map. The label corresponding to each training sample is the actual degree of impact of the impact area.

[0135] In some embodiments, the company management platform may train the influence estimation model based on the training sample set by a gradient descent method, etc. The training method of the influence estimation model is similar to the training method of the evaluation model, and its implementation method may refer to the training method of the evaluation model.

[0136] In some embodiments, the training sample set can be obtained based on historical data. For example, the company management platform can construct a sample transformation map based on historical gas monitoring data, historical pipeline data to be modified, and historical construction parameters in the historical data, determine the area actually affected when modifying the pipeline to be modified as the affected area, and determine the actual degree of impact of the affected area as a label.

[0137] 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 transformation process. For example, the company management platform can obtain the gas monitoring data of the general edge in the sample transformation map before and after the transformation construction, count the data change amplitude of the gas monitoring data before and after the construction, determine the general edge with a data change amplitude greater than the change threshold as the affected area in the label, and determine the actual degree of influence of the affected area through a preset corresponding relationship. Among them, the change threshold can be preset based on historical experience.

[0138] The preset corresponding relationship may be preset based on historical experience. In some embodiments, the preset corresponding relationship may include that the degree of influence is positively correlated to the magnitude of data change, and the greater the magnitude of data change, the higher the degree of influence.

[0139] In some embodiments, the company management platform can also obtain the average vibration amplitude of the general side before and after the reconstruction of the general side through the vibration sensor deployed on the gas pipeline corresponding to the general side, determine the general side with an average vibration amplitude greater than the vibration threshold as the affected area in the label, and determine the actual degree of influence of the affected area through the preset corresponding relationship. Among them, the preset corresponding relationship here can include that the degree of influence is positively correlated with the average vibration amplitude, and the larger the average vibration amplitude, the higher the degree of influence. The vibration threshold can be preset based on historical experience.

[0140] The vibration sensor is used to collect the vibration amplitude of the pipeline based on the collection frequency. The collection frequency can be pre-set based on historical experience. The average vibration amplitude refers to the average value of the vibration amplitude collected based on the collection frequency.

[0141] In some embodiments, the company management platform may also perform a weighted summation of the average vibration amplitude and the data change amplitude, and determine the obtained sum as the degree of influence. The weight of the weighted summation may be preset based on historical experience.

[0142] In some embodiments, the number of training samples corresponding to each transformation method in the training sample set meets a preset number condition.

[0143] The preset quantity refers to a condition for limiting the number of training samples corresponding to different transformation methods in the training sample set. In some embodiments, the preset quantity condition may include that the number of training samples corresponding to each transformation method is not less than the corresponding quantity threshold. Among them, the quantity threshold corresponding to accessory replacement is less than the quantity threshold corresponding to pipeline repair, and the quantity threshold corresponding to pipeline repair is less than the quantity threshold corresponding to pipeline replacement. For an explanation of the transformation methods, see Figure 2 and its related description.

[0144] In some embodiments, the size of each quantity threshold can be preset. For example, the size of the quantity threshold can be positively correlated with the average transformation cost of the method corresponding to the quantity threshold. The average transformation cost of the transformation method refers to the average of the costs of using the transformation method multiple times in history.

[0145] In some embodiments of the present specification, by setting the number of training samples corresponding to each modification method in the training sample set to meet the preset number condition, the impact estimation model can maintain good generalization ability when facing different types of modification methods. By installing vibration sensors on the pipes around the pipes to be modified and combining the data change amplitude of the gas monitoring data to determine the degree of impact, possible safety hazards can be discovered more promptly.

[0146] Monitoring intensity refers to data that characterizes the intensity of monitoring performed by the gas monitoring equipment in the affected area. In some embodiments, the monitoring intensity may include the collection frequency of the gas monitoring equipment.

[0147] In some embodiments, the company management platform may determine the monitoring intensity according to a preset intensity relationship based on the degree of impact of the impact area and the importance of the area, wherein the preset intensity relationship may include that the monitoring intensity is positively correlated with the degree of impact and the importance of the area.

[0148] The regional importance can represent the degree to which the impact area needs to be monitored.

[0149] In some embodiments, the company management platform may determine the pipeline importance of the gas pipelines in the affected area as the regional importance. If the affected area includes multiple gas pipelines, the regional importance may be the average of the pipeline importances of the multiple gas pipelines.

[0150] The importance of the pipeline can represent the degree to which the gas pipeline needs to be monitored. In some embodiments, the company management platform can calculate the average flow rate of the gas pipeline and the population density of the area where the gas pipeline is located by weighted calculation to obtain the importance of the pipeline. The weights of the two can be pre-set based on historical experience. The average flow rate can be obtained by the smart gas equipment object platform. The population density can be obtained by user input or other methods.

[0151] In some embodiments, the company management platform can generate indirect control instructions based on the monitoring intensity, and send the indirect control instructions to the smart gas equipment object platform through the gas company sensor 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.

[0152] In some embodiments, in response to the monitoring intensity and / or the degree of impact satisfying the third monitoring condition, a monitoring expansion instruction is generated based on the monitoring intensity and / or the degree of impact; the monitoring expansion instruction is sent to the smart gas equipment object platform through the gas company's sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the affected area and add gas monitoring equipment and / or vibration sensors.

[0153] The monitoring expansion instruction refers to an instruction for instructing the installation of additional gas monitoring equipment and / or vibration sensors. In some embodiments, in response to the monitoring intensity and / or the degree of impact satisfying the third monitoring condition, the company management platform may generate a monitoring expansion instruction based on the monitoring intensity and / or the degree of impact. The third monitoring condition may include that the monitoring intensity is greater than the monitoring limit threshold and / or the degree of impact is greater than the impact threshold.

[0154] In some embodiments, the monitoring limit threshold may be the highest acquisition frequency that the gas monitoring device can achieve. In some embodiments, the impact threshold may be positively correlated to the importance of the impact area, and the higher the importance, the greater the impact threshold.

[0155] In some embodiments, in response to the degree of impact meeting the third monitoring condition, the company management platform can determine the number of vibration sensors to be installed based on the length of the pipeline in the affected area and the corresponding relationship between the length and the number of installations, and evenly distribute the locations of the installations on the pipeline, and generate a monitoring expansion instruction based on the number and locations of the installed vibration sensors. The corresponding relationship between the length and the number of installations can include that the number of installations is positively correlated with the length.

[0156] In some embodiments, in response to the monitoring intensity satisfying the third monitoring condition, the company management platform can install additional gas monitoring equipment in a quantity that can meet the monitoring intensity at the location of the original gas monitoring equipment based on the monitoring intensity and the monitoring limit threshold, and generate a monitoring expansion instruction based on the location, quantity and time point of data acquisition of the additional gas monitoring equipment.

[0157] Exemplarily, the monitoring intensity is 2s / time, but the upper monitoring threshold is 4s / time, and the time points at which the original gas monitoring equipment obtains data are 0s, 4s, 8s, ..., then a gas monitoring equipment is installed in the position of the original gas monitoring equipment, and the time points at which the installed gas monitoring equipment obtains data are 2s, 6s, 10s, ..., and through cross-monitoring and collection, a monitoring intensity of 2s / time is achieved.

[0158] In some embodiments, the company management platform can send monitoring expansion instructions to the smart gas equipment object platform through the gas company sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the affected area and add gas monitoring equipment and / or vibration sensors. Among them, the crawling robot can fix the added gas monitoring equipment and / or vibration sensor at the location where it needs to be installed based on the monitoring expansion instruction. The crawling robot can also carry the gas monitoring equipment and / or vibration sensor and squat at the location where it needs to be installed, so as to remove the added gas monitoring equipment and / or vibration sensor later.

[0159] In some embodiments of the present specification, by generating monitoring expansion instructions and controlling the crawling robot to move to the affected area and adding gas monitoring equipment and / or vibration sensors, when the current monitoring intensity of the affected area is insufficient, equipment for monitoring can be added in time to ensure effective monitoring of the affected area.

[0160] In some embodiments of the present specification, by constructing a transformation map, the complex gas monitoring equipment and related data can be effectively organized, and the impact estimation model can be used to quickly and accurately determine the degree of impact of the impact area, and then the monitoring intensity of the gas monitoring equipment in the impact area can be determined based on the impact intensity, thereby ensuring effective monitoring of the impact area.

[0161] The present invention also provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any one of the methods in the above embodiments.

[0162] Furthermore, certain features, structures or characteristics in one or more embodiments of the present invention may be appropriately combined.

[0163] In some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present invention are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0164] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the referenced materials of this invention and the contents of this invention, the descriptions, definitions, and / or usage of terms in this invention shall prevail.

Claims

1. A smart gas pipeline network transformation method based on the supervision of the Internet of Things, characterized in that: The method is executed by a gas company management platform of a smart gas pipe network transformation system based on a supervision Internet of Things, and the method includes: Obtaining gas monitoring data from the smart gas equipment object platform through the gas company sensor network platform, and storing the gas monitoring data in the gas database; Acquire 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, wherein the transformation strategy parameters include at least one of the pipeline data to be transformed and the construction parameters of the pipeline to be transformed; and Based on the transformation strategy parameters, a control instruction is generated, and the control instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

2. The method according to claim 1, characterized in that The method further comprises: Acquire 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, wherein the evaluation model is a machine learning model; In response to the evaluation parameter sequence satisfying a preset condition, determining the pipeline to be modified; Determining the construction parameters based on the pipeline to be modified and the evaluation parameter sequence; and In response to the pipeline health score of the pipeline to be modified satisfying the first monitoring condition, a leakage monitoring instruction is generated based on the pipeline health score, and the leakage monitoring instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the pipeline to be modified and perform leakage monitoring.

3. The method according to claim 2, characterized in that The evaluation parameter sequence includes estimated evaluation parameters, the evaluation model includes a prediction layer and an evaluation layer, the historical monitoring data is obtained through the gas database, and based on the historical monitoring data, the historical fault data, the pipeline data and the gas monitoring data, the evaluation parameter sequence is determined through the evaluation model, including: 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, determining estimated monitoring data and estimated fault data through the prediction layer; and The estimated evaluation parameters are determined by 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.

4. The method according to claim 3, characterized in that The method further comprises: In response to the estimated fault data satisfying the second monitoring condition, a fault monitoring instruction is generated based on the estimated fault data, and the fault monitoring instruction is sent to the smart gas equipment object platform through the gas company's sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the pipeline to be monitored and perform mobile patrol.

5. The method according to claim 1, characterized in that The transformation strategy parameters also include the degree of impact on the affected area, the control instructions also include indirect control instructions, and the method further includes: Constructing a transformation map based on the gas monitoring data, the pipeline data to be modified and the construction parameters; Based on the transformation map, determining the degree of impact through an impact estimation model, wherein the impact estimation model is a machine learning model; Based on the degree of impact, determining the monitoring intensity corresponding to the impact area; and The indirect control instruction is generated based on the monitoring intensity, and the indirect control instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the affected area.

6. A smart gas pipe network transformation system based on the supervision of the Internet of Things, characterized in that: 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 as follows: Obtaining gas monitoring data from the smart gas equipment object platform through the gas company sensor network platform, and storing the gas monitoring data in a gas database; Acquire 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, wherein the transformation strategy parameters include at least one of the pipeline data to be modified and the construction parameters of the pipeline to be modified; as well as Based on the transformation strategy parameters, a control instruction is generated, and the control instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment of the smart gas equipment object platform.

7. The system according to claim 1, characterized in that The gas company management platform is further configured as follows: Acquire 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, wherein the evaluation model is a machine learning model; In response to the evaluation parameter sequence satisfying a preset condition, determining the pipeline to be modified; Determining the construction parameters based on the pipeline to be transformed and the evaluation parameter sequence; as well as In response to the pipeline health score of the pipeline to be modified satisfying the first monitoring condition, a leakage monitoring instruction is generated based on the pipeline health score, and the leakage monitoring instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the crawling robot of the smart gas equipment object platform to move to the pipeline to be modified and perform leakage monitoring.

8. The system according to claim 7, characterized in that The evaluation parameter sequence includes estimated evaluation parameters, the evaluation model includes a prediction layer and an evaluation layer, and the gas company management platform is further configured as follows: 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, determining estimated monitoring data and estimated fault data through the prediction layer; as well as The estimated evaluation parameters are determined by 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.

9. The system according to claim 8, characterized in that The transformation strategy parameters also include the degree of impact on the affected area, the control instructions also include indirect control instructions, and the gas company management platform is further configured as follows: Constructing a transformation map based on the gas monitoring data, the pipeline data to be modified and the construction parameters; Based on the transformation map, determining the degree of impact through an impact estimation model, wherein the impact estimation model is a machine learning model; Based on the degree of impact, determining the monitoring intensity corresponding to the affected area; as well as The indirect control instruction is generated based on the monitoring intensity, and the indirect control instruction is sent to the smart gas equipment object platform through the gas company sensor network platform to control the monitoring parameters of the gas monitoring equipment corresponding to the affected area.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method as claimed in claim 1.

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