Intelligent gas pipeline safety monitoring method, internet of things system, device and medium

Abstract

The embodiment of the specification provides a smart gas pipeline safety monitoring method, an Internet of Things system, a device and a medium. The method comprises the following steps: based on a smart gas sensing network platform, obtaining transportation characteristics of at least one gas pipeline section from a detection device corresponding to the at least one gas pipeline section in a preset area; obtaining pipeline characteristics and transportation characteristics of at least one gas pipeline section in the preset area, and determining a patrol demand degree of the at least one gas pipeline section based on the pipeline characteristics and the transportation characteristics; determining at least one target pipeline section based on the patrol demand degree; sending the at least one target pipeline section to a smart gas data center, and further sending the target pipeline section to a smart gas user platform based on a smart gas service platform; generating a remote control instruction based on the target pipeline section and sending the remote control instruction to the smart gas data center, and sending the remote control instruction to a smart gas object platform to perform deep patrol based on the smart gas sensing network platform.

Description

Technical Field

[0001] This manual relates to the field of gas pipeline safety monitoring, and in particular to a smart gas pipeline safety monitoring method, Internet of Things system, device and medium. Background Technology

[0002] Natural gas is flammable and explosive, making its transportation extremely important and placing high demands on the reliability of gas pipelines. To ensure safe gas transportation, regular inspections of gas pipelines are necessary. However, conducting regular, systematic inspections of the gas pipeline network not only consumes significant manpower, resources, and time, but also risks that some pipeline faults may not be detected and addressed immediately.

[0003] Therefore, it is hoped that a smart gas pipeline safety monitoring method, Internet of Things system, device and medium can be proposed to dynamically monitor the status of gas pipelines and identify pipeline sections that need key maintenance, so as to improve the efficiency of gas pipeline safety monitoring. Summary of the Invention

[0004] This specification provides one or more embodiments of a smart gas pipeline safety monitoring method. It is implemented based on a smart gas pipeline safety monitoring Internet of Things (IoT) system. The IoT system includes a smart gas user platform, a smart gas service platform, a smart gas pipeline network safety management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas pipeline network safety management platform includes a smart gas data center and a smart gas pipeline network inspection management sub-platform. The method is executed by the smart gas pipeline network safety management platform and includes: the smart gas data center, based on the smart gas sensor network platform, acquiring the transport characteristics of at least one gas pipeline segment from detection devices corresponding to at least one gas pipeline segment in a preset area; the gas detection devices being configured in the smart gas object platform; the smart gas pipeline network inspection management sub-platform... The platform is used to: obtain pipeline characteristics and transportation characteristics of at least one gas pipeline segment in a preset area from the smart gas data center, and determine the inspection demand of the at least one gas pipeline segment based on the pipeline characteristics and transportation characteristics; determine at least one target pipeline segment based on the inspection demand of the at least one gas pipeline segment; send the at least one target pipeline segment to the smart gas data center, and further send the target pipeline segment to the smart gas user platform based on the smart gas service platform; generate a remote control command based on the at least one target pipeline segment and send it to the smart gas data center, and send the remote control command to the smart gas object platform based on the smart gas sensor network platform to perform a deep inspection.

[0005] One embodiment of this specification provides a smart gas pipeline safety monitoring Internet of Things (IoT) system. The IoT includes a smart gas user platform, a smart gas service platform, a smart gas pipeline safety management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas pipeline safety management platform includes a smart gas data center and a smart gas pipeline inspection management sub-platform. The smart gas pipeline safety management platform is configured to perform the following operations: the smart gas data center, based on the smart gas sensor network platform, obtains the transport characteristics of at least one gas pipeline segment from detection devices corresponding to at least one gas pipeline segment in a preset area; the gas detection devices are configured in the smart gas object platform; the smart gas pipeline inspection... The management sub-platform is configured to: obtain pipeline characteristics and transportation characteristics of at least one gas pipeline segment in a preset area from the smart gas data center; determine the inspection demand of the at least one gas pipeline segment based on the pipeline characteristics and transportation characteristics; determine at least one target pipeline segment based on the inspection demand of the at least one gas pipeline segment; send the at least one target pipeline segment to the smart gas data center, and further send the target pipeline segment to the smart gas user platform based on the smart gas service platform; generate a remote control command based on the at least one target pipeline segment and send it to the smart gas data center, and send the remote control command to the smart gas object platform to perform a deep inspection based on the smart gas sensor network platform.

[0006] This specification provides one or more embodiments of a smart gas pipeline safety monitoring device, the device including at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least a portion of the computer instructions to implement a smart gas pipeline safety monitoring method.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes a smart gas pipeline safety monitoring method.

[0008] This invention aims to overcome the potential hazards caused by the untimely in-depth inspection of pipelines requiring urgent inspection. By determining the inspection demand of at least one gas pipeline segment based on pipeline and transportation characteristics, and then identifying at least one target pipeline segment for in-depth inspection, this invention allows for thorough inspection of pipeline segments in urgent need of inspection, effectively reducing the likelihood of potential hazards and achieving better gas pipeline safety monitoring. Furthermore, by determining the minimum map based on the pipeline diagram and then using a one-stroke drawing algorithm to determine the target inspection route, the optimal inspection route can be obtained, thereby improving efficiency and saving resources. Attached Figure Description

[0009] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0010] Figure 1 This is an exemplary schematic diagram of an IoT system for intelligent gas pipeline safety monitoring, as shown in some embodiments of this specification.

[0011] Figure 2 This is an exemplary flowchart of a smart gas pipeline safety monitoring method according to some embodiments of this specification;

[0012] Figure 3 This is an exemplary schematic diagram of an inspection demand prediction model according to some embodiments of this specification;

[0013] Figure 4 This is an exemplary flowchart of a method for determining a target inspection route for a crawling robot according to some embodiments of this specification;

[0014] Figure 5 This is an exemplary schematic diagram illustrating the determination of a target inspection route for a crawling robot according to some embodiments of this specification. Detailed Implementation

[0015] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0016] Figure 1 This is an exemplary schematic diagram of an IoT system for intelligent gas pipeline safety monitoring according to some embodiments of this specification. In some embodiments, the intelligent gas pipeline safety monitoring IoT system 100 may include an intelligent gas user platform, an intelligent gas service platform, an intelligent gas pipeline network safety management platform, an intelligent gas sensor network platform, and an intelligent gas object platform.

[0017] In some embodiments, information processing in the Internet of Things (IoT) can be divided into a processing flow for sensing information and a processing flow for control information. Control information can be generated based on sensing information. Specifically, the processing of sensing information involves the smart gas user platform acquiring the sensing information and transmitting it to the management platform. Control information, on the other hand, is distributed from the smart gas pipeline safety management platform to the smart gas user platform to achieve corresponding control.

[0018] A smart gas user platform can be a platform for interacting with users. In some embodiments, the smart gas user platform can be configured as a terminal device, such as a mobile device, tablet computer, or any combination thereof. In some embodiments, the smart gas user platform can be used to provide feedback on target pipeline segments to users. In some embodiments, the smart gas user platform has a gas user sub-platform and a regulatory user sub-platform. The gas user sub-platform is for gas users, who are users of gas. In some embodiments, the gas user sub-platform can receive target pipeline segments to remind gas users. For example, the gas user sub-platform can be used to remind gas users of information about potential impacts from in-depth pipeline inspections. The regulatory user sub-platform is for regulatory users and monitors the operation of the entire smart gas pipeline safety monitoring IoT system. Regulatory users are users of the safety department. In some embodiments, the smart gas user platform can interact bidirectionally with the smart gas service platform. It receives target pipeline segments, etc., uploaded by the smart gas service platform and sends pipeline inspection management related information query instructions to the smart gas data center, etc.

[0019] A smart gas service platform can be a platform for receiving and transmitting data and / or information. For example, a smart gas service platform can send target pipeline segments to a smart gas user platform. In some embodiments, the smart gas service platform includes a smart gas consumption service sub-platform and a smart regulatory service sub-platform. The smart gas consumption service sub-platform corresponds to the gas user sub-platform and provides safe gas consumption services to gas users. The smart regulatory service sub-platform corresponds to the regulatory user sub-platform and provides safety supervision services to gas regulatory users. In some embodiments, the smart gas service platform can interact bidirectionally with the smart gas pipeline safety management platform. It receives target pipeline segments, etc., uploaded by the smart gas data center and sends pipeline inspection management related information query instructions to the smart gas data center of the smart gas pipeline safety management platform.

[0020] A smart gas pipeline safety management platform can refer to a platform that coordinates and integrates the connections and collaboration between various functional platforms, gathers all the information from the Internet of Things (IoT), and provides sensing, management, and control functions for the IoT operating system. For example, a smart gas pipeline safety management platform can acquire pipeline characteristics and transportation characteristics of gas pipeline sections in a preset area.

[0021] In some embodiments, the intelligent gas pipeline safety management platform includes an intelligent gas data center and an intelligent gas pipeline inspection management sub-platform. The intelligent gas data center and the intelligent gas pipeline inspection management sub-platform interact bidirectionally. The intelligent gas pipeline inspection management sub-platform obtains pipeline characteristics and transportation characteristics of at least one gas pipeline segment in a preset area from the intelligent gas data center and feeds back corresponding remote control commands. The intelligent gas pipeline safety management platform interacts with the intelligent gas service platform and the intelligent gas sensor network platform through the intelligent gas data center. In some embodiments, the intelligent gas data center can receive transportation characteristics uploaded by the sensor network platform and send them to the intelligent gas pipeline inspection management sub-platform for processing, then send the aggregated and processed data to the intelligent gas service platform and / or the intelligent gas sensor network platform. In some embodiments, the intelligent gas pipeline inspection management sub-platform of the intelligent gas pipeline safety management platform includes an inspection plan management module, an inspection time early warning module, an inspection status management module, and an inspection problem management module.

[0022] A smart gas sensor network platform can be a functional platform for managing sensor communication. It can be configured as a communication network and gateway, enabling functions such as network management, protocol management, command management, and data parsing. In some embodiments, the smart gas sensor network platform can connect to a smart gas pipeline safety management platform and a smart gas object platform, realizing the functions of sensing and communication of perception and control information. For example, the smart gas sensor network platform can receive remote control commands issued by the smart gas data center and send these commands to the smart gas object platform.

[0023] The intelligent gas pipeline platform can be a functional platform for generating sensing information. In some embodiments, the intelligent gas pipeline platform may also include an intelligent gas pipeline equipment sub-platform and an intelligent gas pipeline inspection engineering sub-platform. The intelligent gas pipeline equipment sub-platform may include pressure sensors, flow meters, temperature sensors, etc. The pressure sensor is used to obtain the actual transport pressure within the gas pipeline section; the flow meter is used to obtain the actual transport flow rate within the gas pipeline section; and the temperature sensor is used to obtain the actual transport temperature within the gas pipeline section. The intelligent gas pipeline inspection engineering sub-platform may include a crawling robot for deep inspection.

[0024] It should be noted that the smart gas user platform in this embodiment can be a desktop computer, tablet computer, laptop computer, mobile phone, or other electronic device capable of data processing and communication, and is not limited in many ways. It should be understood that the data processing process mentioned in this embodiment can be performed by the server's processor, and the data stored on the server can be stored on the server's storage devices, such as hard drives or other storage devices. In specific applications, the smart gas sensor network platform can employ multiple gateway servers or multiple smart routers, and is not limited in many ways. It should be understood that the data processing process mentioned in this embodiment can be performed by the gateway server's processor, and the data stored on the gateway server can be stored on the gateway server's storage devices, such as hard drives and SSDs.

[0025] In some embodiments of this specification, smart gas pipeline safety monitoring is implemented through the IoT functional architecture of five platforms, completing the closed loop of information flow and making IoT information processing smoother and more efficient.

[0026] Figure 2 This is an exemplary flowchart of a smart gas pipeline safety monitoring method according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by a smart gas pipeline safety management platform.

[0027] Step 210: Based on the intelligent gas sensor network platform, obtain the transportation characteristics of at least one gas pipeline segment from the detection equipment corresponding to at least one gas pipeline segment in the preset area.

[0028] The preset area refers to the area where the gas pipeline segment to be inspected is located. The preset area can include multiple gas pipeline segments. For example, the preset area can be the area where a gas pipeline segment of a certain residential area, community, or administrative district is located.

[0029] Gas pipeline sections are used for gas transportation, and multiple gas pipeline sections can be connected for use. Testing equipment is used to detect various parameters of the gas pipeline. The testing equipment is configured within the smart city platform. The testing equipment can include various types, such as pressure sensors, flow meters, temperature sensors, and other devices with detection functions.

[0030] Transportation characteristics refer to actual parameters related to transportation of a corresponding gas pipeline segment obtained through detection equipment. For example, transportation characteristics may include the actual transportation pressure within the gas pipeline segment obtained through a pressure sensor; the actual transportation flow rate of the gas pipeline segment obtained through a flow meter; and the actual transportation temperature of the gas pipeline segment obtained through a temperature sensor. In some embodiments, transportation characteristics can be represented by a vector. For example, a transportation characteristic vector can be constructed. Each element in the vector can represent a transportation-related actual parameter (e.g., a represents actual transportation pressure, b represents actual transportation flow, c represents actual transportation temperature, etc.).

[0031] In some embodiments, the smart gas data center can acquire real-time detection parameters from monitoring equipment as transport characteristics of a gas pipeline segment. For example, the smart gas data center can acquire real-time uploaded detection parameters from monitoring equipment through a smart gas service platform. In some embodiments, the smart gas data center can acquire transport characteristics of a gas pipeline segment based on historical data within a preset time period (e.g., 10 days, 20 days, 30 days, etc.). For example, the smart gas data center can acquire historical detection parameters of a gas pipeline segment over 10 days and use the average value of the historical detection parameters as the transport characteristics of that gas pipeline segment.

[0032] Step 220: Obtain the pipeline characteristics and transportation characteristics of at least one gas pipeline segment in the preset area from the smart gas data center, and determine the inspection demand of at least one gas pipeline segment based on the pipeline characteristics and transportation characteristics.

[0033] Pipeline characteristics refer to the rated parameters of a gas pipeline segment obtained based on stored data from a smart gas data center. For example, pipeline characteristics may include parameters such as the transmission pressure (e.g., low pressure, medium pressure, sub-high pressure, high pressure) and age (e.g., design life, service life, remaining life) of the gas pipeline segment. In some embodiments, pipeline characteristics can be represented by vectors. For example, a pipeline characteristic vector can be constructed. Each element in the vector can represent the rated parameters of a gas pipeline section (e.g., i represents the low-pressure rating, j represents the medium-pressure rating, k represents the remaining years, etc.).

[0034] In some embodiments, pipeline characteristics can be obtained based on historical data. For example, pipeline characteristics can be obtained based on the factory parameters of gas pipeline segments stored in a smart gas data center.

[0035] In some embodiments, pipeline features also include the environment in which the gas pipeline section is located.

[0036] The environment surrounding a gas pipeline section refers to the actual environment of the location where the gas pipeline section is installed. Information about this environment can include humidity, soil pH, and road conditions. The environment can affect the use of the gas pipeline section. For example, humidity and soil pH affect the degree of corrosion the pipeline section is susceptible to. For instance, a low pH in the soil where the pipeline section is located may exacerbate corrosion. Similarly, road conditions affect the probability of damage from external forces. For example, if the road where the pipeline section is located is frequently used by trucks, resulting in severe road damage, it may increase the probability of damage from external forces.

[0037] In some embodiments, a smart gas data center can obtain information about the environment of a gas pipeline section manually. For example, it can acquire environmental information input by a user terminal.

[0038] Using the environment where the gas pipeline section is located as a characteristic of the gas pipeline section, and fully considering the impact of environmental factors on the gas pipeline section, can make the determination of the target pipeline section more accurate.

[0039] Inspection demand refers to a numerical value or letter that reflects the degree to which a gas pipeline section needs inspection. For example, the inspection demand can be represented by a value between 1 and 100, the letter 'af', or a star rating. The higher the value, the higher the lexicographical ranking, or the higher the star rating, the higher the maintenance priority.

[0040] In some embodiments, the intelligent gas pipeline inspection management sub-platform can determine the inspection demand of at least one gas pipeline segment based on pipeline characteristics and transportation characteristics. In some embodiments, the intelligent gas pipeline inspection management sub-platform can manually preset correspondence rules between pipeline characteristics and transportation characteristics and inspection demand, and determine the inspection demand based on these rules. For example, a lookup table between pipeline characteristic parameters and transportation characteristic parameters and inspection demand can be preset, and the inspection demand can be obtained by looking up the table based on the pipeline characteristics and transportation characteristics. In some embodiments, the intelligent gas pipeline inspection management sub-platform can determine the inspection demand through an inspection demand prediction model. For details on determining the inspection demand through an inspection demand prediction model and model training, please refer to [link to relevant documentation]. Figure 3 And its contents.

[0041] Step 230: Based on the inspection demand of at least one gas pipeline segment, determine at least one target pipeline segment.

[0042] The target pipeline section refers to the gas pipeline section that requires in-depth inspection. For more information on target pipeline sections, please refer to the relevant description of gas pipeline sections.

[0043] In some embodiments, the intelligent gas pipeline inspection management sub-platform can determine at least one target pipeline segment by setting a threshold for inspection demand. For example, for multiple gas pipeline segments A, B, C, and D, their corresponding inspection demand scores are 66, 77, 85, and 91, respectively. When the threshold for inspection demand is 80, gas pipeline segments C and D are determined as target pipeline segments.

[0044] Step 240: Send at least one target pipeline segment to the smart gas data center, and further send the target pipeline segment to the smart gas user platform based on the smart gas service platform.

[0045] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can send at least one target pipeline segment to the intelligent gas data center. For example, the intelligent gas pipeline inspection and management sub-platform can send information such as the target pipeline segment's number, location, and inspection demand level to the intelligent gas data center. In some embodiments, when the target pipeline segment is further sent to the intelligent gas user platform based on the intelligent gas service platform, it can be displayed on the user platform. For example, it can be displayed to users (e.g., managers) based on the target pipeline segment's number from smallest to largest or the inspection demand level from highest to lowest. Another example is that inspection reminder information for the target pipeline segment can be displayed to users (e.g., gas users).

[0046] Step 250: Based on at least one target pipeline segment, generate a remote control command and send it to the smart gas data center. Then, based on the smart gas sensor network platform, send the remote control command to the smart gas object platform to perform in-depth inspection.

[0047] Remote control commands are instructions used to control deep inspection. In some embodiments, remote control commands may include controlling a crawling robot to perform deep inspection, or dispatching personnel to perform deep inspection. In some embodiments, remote control commands may also include the target pipeline segment number, location, etc.

[0048] Deep inspection refers to a thorough examination of the interior of gas pipelines. When conducting deep inspections using crawling robots, the robots can adhere to the inner wall of the pipeline using suction cups or magnetic materials and then move around. The movement of the crawling robot can be remotely controlled or automatically driven by a pre-programmed sequence. In some embodiments, the crawling robot can be equipped with infrared devices, cameras, etc., to detect the condition of the pipeline's inner wall (e.g., corrosion); it can also be equipped with various sensors to monitor gas pressure, flow rate, temperature, etc., within the pipeline.

[0049] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can generate remote instructions based on the location of at least one target pipeline segment. In some embodiments, the remote control instructions may include a manual inspection route. The intelligent gas pipeline inspection and management sub-platform can determine a manual inspection route based on at least one target pipeline segment.

[0050] In some embodiments, the remote control commands may also include the target inspection route of the crawling robot. The intelligent gas pipeline inspection management sub-platform can determine the target inspection route of the crawling robot based on at least one target pipeline segment.

[0051] A target inspection route refers to the shortest inspection route for a crawling robot to perform in-depth inspection of at least one target pipe segment. For example, a target inspection route could start from target pipe segment B, pass through target pipe segments A, D, and C in sequence, and end at target pipe segment E.

[0052] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can determine the target inspection route of the crawling robot based on at least one target pipeline segment. For example, the intelligent gas pipeline inspection and management sub-platform can pre-determine the inspection order of target pipeline segments based on a ranking of inspection demand from high to low. Then, a greedy algorithm is used to obtain the route to each target pipeline segment sequentially, thereby determining the target inspection route. Alternatively, the intelligent gas pipeline inspection and management sub-platform can also construct a pipeline map and then determine the target inspection route using a one-stroke drawing algorithm. For a detailed explanation of determining the target inspection route based on a pipeline map, please refer to [link to relevant documentation]. Figure 4 , Figure 5 And its related descriptions. It should be noted that when determining the target inspection route, the optimal or near-optimal solution of the shortest inspection route can be used as the target inspection route.

[0053] Based on at least one target pipeline segment, the target inspection route of the crawling robot can be determined, enabling the crawling robot to perform in-depth inspections according to the inspection requirements and crawl along the relatively optimal route.

[0054] In some embodiments, the crawling robot can arrive at its standby position in advance based on a remote control command. When the inspection time arrives, it triggers an inspection operation along the target inspection route.

[0055] Some embodiments in this specification, based on pipeline characteristics and transportation characteristics, determine the inspection requirement of at least one gas pipeline segment, thereby identifying at least one target pipeline segment and conducting in-depth inspections of that target pipeline segment. This allows for in-depth inspections of pipeline segments requiring urgent inspection, effectively reducing the likelihood of potential hazards and achieving better gas pipeline safety monitoring results.

[0056] Figure 3This is an exemplary schematic diagram of an inspection demand prediction model 300 shown in some embodiments of this specification.

[0057] In some embodiments, the intelligent gas pipeline inspection management sub-platform can predict the inspection demand of at least one gas pipeline segment based on pipeline characteristics and transportation characteristics using an inspection demand prediction model.

[0058] In some embodiments, such as Figure 3 As shown, the inspection demand prediction model 320 can be a machine learning model. In some embodiments, the inspection demand prediction model 320 can be a deep learning neural network model. Exemplary deep learning neural network models may include convolutional neural networks (CNNs), deep neural networks (DNNs), or combinations thereof.

[0059] In some embodiments, such as Figure 3 As shown, the input to the inspection demand prediction model 320 may include pipeline feature 310-1 and transportation feature 310-2. The input pipeline feature 310-1 and transportation feature 310-2 can be a transportation feature vector and a pipeline feature vector, respectively. In some embodiments, the output of the inspection demand prediction model 320 may include an inspection demand degree 330. More details regarding pipeline features, transportation features, and inspection demand degree can be found in [reference needed]. Figure 2 And its related descriptions.

[0060] In some embodiments, such as Figure 3 As shown, the input to the inspection demand prediction model 320 also includes the last deep inspection time 310-3 of at least one gas pipeline section.

[0061] In some embodiments of this specification, the intelligent gas pipeline inspection management sub-platform takes into account the impact of the last in-depth inspection time of at least one gas pipeline segment when determining the inspection demand using the inspection demand prediction model. Gas pipeline segments with a long interval between the last in-depth inspection should be inspected first, making the determined inspection demand more accurate.

[0062] In some embodiments, such as Figure 3 As shown, the inputs to the inspection demand prediction model 320 also include climate impact factor 310-4. Climate impact factor 310-4 can be determined based on future weather conditions for at least one gas pipeline segment and the pipeline burial depth.

[0063] Climate impact factors refer to climate-related factors that affect pipeline segments. In some embodiments, climate impact factors may be related to future weather conditions and the burial depth of at least one gas pipeline segment. For example, if a pipeline segment is expected to experience heavy rainfall in its area and is shallowly buried, it may be affected.

[0064] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can obtain future weather conditions for the area where the pipeline section is located through a weather forecast platform, and obtain the burial depth of the pipeline section through relevant design drawings and construction drawings. Based on the future weather conditions and the pipeline burial depth, climate impact factors can be determined. For example, climate impact factors can be represented by levels. A "Level 1" climate impact factor can be assigned to a condition with poor future weather conditions (such as rainfall and acid rain) and a shallow pipeline burial depth; a "Level 2" climate impact factor can be assigned to a condition with poor future weather conditions and a deep pipeline burial depth; and a "Level 3" climate impact factor can be assigned to a condition with good future weather conditions (such as sunny days) and a very deep pipeline burial depth.

[0065] In some embodiments of this specification, the intelligent gas pipeline inspection management sub-platform can more accurately predict the inspection demand of pipeline sections based on climate impact factors and an inspection demand prediction model. The closer the pipeline is to the ground, the greater the likelihood of being affected by weather. Therefore, if the future weather in the area where the pipeline section is located is expected to be severe, the inspection demand of that pipeline section will be increased accordingly to ensure the accuracy of the inspection demand.

[0066] In some embodiments, such as Figure 4 As shown, the inspection demand prediction model can be obtained through training. For example, training samples are input into the initial inspection demand prediction model, and a loss function is established based on the labels and the output of the initial inspection demand prediction model to update the parameters of the initial inspection demand prediction model. When the loss function of the initial inspection demand prediction model meets preset conditions, the model training is complete, and the trained inspection demand prediction model is obtained. These preset conditions can include loss function convergence, the number of iterations reaching an iteration threshold, etc.

[0067] In some embodiments, training samples may include positive and negative samples. Positive samples are sample pipeline features and sample transportation features corresponding to pipeline segments found to be faulty after in-depth inspection in historical data, or sample pipeline features and sample transportation features corresponding to pipeline segments that were not deeply inspected in historical data but developed faults within a short future time period (e.g., a time period less than a time threshold, such as 10 days, 20 days, 30 days, etc.). The label value of positive samples can be assigned a value from 0 to 1 based on the severity of the fault or the specific time of the "short time period". For example, the fault repair time can be used to characterize the severity of the fault; the longer the repair time, the higher the severity of the fault. A label value of 0.1 corresponds to a fault repair time of 0-2 hours, and a label value of 0.2 corresponds to a fault repair time of 2-4 hours, etc. As another example, a label value of 1 corresponds to a short time period of 0-2 days, and a label value of 0.9 corresponds to a short time period of 3-5 days, etc. Negative samples are either pipeline segments found to be normal after in-depth inspection in historical data, or pipeline segments that were not subject to in-depth inspection in historical data but will remain normal for a considerable period of time (e.g., longer than a time threshold, such as 30 days, 60 days, or 100 days). Negative samples can be labeled as 0.

[0068] In some embodiments, the input to the inspection demand prediction model 320 may also include the last deep inspection time 310-3 and / or the climate impact factor 310-4. Correspondingly, the training samples may also include the sample's last deep inspection time and / or the sample's climate impact factor.

[0069] In some embodiments of this specification, the intelligent gas pipeline inspection management sub-platform can quickly and accurately predict the inspection demand of pipeline segments based on pipeline characteristics and transportation characteristics using an inspection demand prediction model. This helps the intelligent gas pipeline inspection management sub-platform to conduct in-depth inspections of target pipeline segments that urgently require in-depth inspection based on the inspection demand. Furthermore, by inputting the time of the last in-depth inspection and / or climate influence factors, the inspection demand prediction model can further improve the accuracy of the predicted inspection demand.

[0070] Figure 4 This is an exemplary flowchart illustrating a method for determining a target inspection route for a crawling robot, according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by the intelligent gas pipeline inspection and management sub-platform.

[0071] Step 410: Construct a pipeline diagram based on at least one gas pipeline segment.

[0072] Piping diagrams can show the piping structure. Figure 5 Taking the exemplary schematic diagram of determining the target inspection route of the crawling robot as an example, the dashed lines (such as pipe segments AB, CD, etc.) in the pipe structure diagram represent the pipes to be inspected, while the solid lines (such as pipe segments AL, FL, etc.) represent the pipes not to be inspected. A pipe diagram can be constructed based on the pipe structure diagram.

[0073] A pipeline graph can include nodes and edges. In some embodiments, nodes can include pipeline nodes to be detected and non-pipeline nodes. For example, in a pipeline graph, white nodes (such as nodes a, b, and c) represent pipeline nodes to be detected, and black nodes (such as nodes f, g, and h) represent non-pipeline nodes. In some embodiments, edges can be used to connect pipeline nodes to be detected and non-pipeline nodes. An edge is characterized by the length of the pipeline segment. For example, the edge connecting node e to node c reflects the length of the pipeline between the two nodes.

[0074] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can construct a pipeline map based on at least one gas pipeline segment by using conversion rules. For example... Figure 5 As shown, the transformation rules include abstracting intersections with branches (i.e., at least three edges) into a non-detected pipe node. For example, if intersection L contains pipe segments AL, FL, IL, and HL, this intersection can be abstracted as node f; each connected pipe segment to be detected is treated as a whole and abstracted as a pipe node to be detected. For example, pipe segments GH and LH are connected and can be treated as a whole and abstracted as pipe node e; and the end of a non-detected pipe segment (no longer connected to other pipes) is abstracted as a non-detected pipe node. For example, pipe segment KJ is no longer connected to other pipe segments, and its end can be abstracted as non-detected pipe node g.

[0075] Step 420: Determine a minimum graph containing at least one target pipeline segment based on the pipeline graph. The minimum graph includes multiple connected subgraphs corresponding to at least one target pipeline segment and the shortest path connecting the connected subgraphs.

[0076] A connected subgraph of a minimum graph is a subgraph composed of interconnected nodes of the pipeline to be tested within the minimum graph, with each node corresponding to a target pipeline segment. The shortest path is the shortest path among all paths that connect the connected subgraphs.

[0077] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can determine the minimum spanning tree based on the pipeline diagram using the minimum spanning tree algorithm, and determine the minimum graph based on the minimum spanning tree.

[0078] A spanning tree is a connected graph that contains all nodes in a pipeline graph and has no closed loops. A minimum spanning tree is the spanning tree with the smallest sum of edge weights among multiple spanning trees corresponding to a pipeline graph. In some embodiments, the edge weights can be determined based on the edge's characteristics, i.e., the length of the pipeline segment. The longer the pipeline segment, the greater the weight. For example, a lookup table of pipeline segment lengths and weights can be pre-defined, and the edge weights can be determined by looking up the table. Figure 5 For example, the minimum spanning tree includes all nodes in the pipeline graph and has no closed cycles. Among the multiple spanning trees corresponding to the pipeline graph, the minimum spanning tree has the smallest sum of edge weights.

[0079] Minimum spanning tree algorithms are used to determine the minimum spanning tree. A variety of feasible algorithms can be included, such as Prim's algorithm and Kruskal's algorithm.

[0080] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can use the Prim algorithm or the Kruskal algorithm to determine and connect the adjacent nodes with the smallest weights that will not form a closed loop, thereby forming a minimum spanning tree.

[0081] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can use the obtained minimum spanning tree as the minimum graph.

[0082] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can prune the minimum spanning tree and determine the pruned graph as the minimum graph.

[0083] Pruning refers to the operation of removing non-detected pipeline nodes and their connected edges. In some embodiments, it can be determined whether pruning can be applied to a pipeline node by judging whether, after removing the non-detected pipeline nodes and their connected edges, at least one connected subgraph in the remaining connected subgraph includes all the pipeline nodes to be detected. Figure 5 For example, the minimum spanning tree includes nodes f, g, and h (not to be detected pipelines) and their corresponding edges. After removing nodes g and h and their connecting edges, the remaining connected subgraph includes a connected subgraph containing all nodes a, b, c, d, and e (not to be detected pipelines). Therefore, nodes g and h can be pruned. However, after removing node f and its connecting edges, the remaining connected subgraph does not include a connected subgraph containing all nodes a, b, c, d, and e (not to be detected pipelines). Therefore, node f cannot be pruned.

[0084] By determining the minimum spanning tree using the minimum spanning tree algorithm and pruning it, the minimum graph can be determined, which helps to obtain the optimal inspection route.

[0085] Step 430: Based on the minimum graph, determine the target inspection route using a one-stroke drawing algorithm.

[0086] The one-stroke drawing algorithm is an algorithm used to determine the number of singularities N in a minimum graph, thereby determining whether the minimum graph can be drawn in one stroke without repeating line segments.

[0087] The number of singular vertices, N, refers to the number of singular vertices in a minimal graph. A singular vertex is a node connected by an odd number of edges. For example, in a minimal graph, node a is connected by three edges, making node a a singular vertex. In addition, nodes c, d, and e are each connected by one edge, meaning nodes c, d, and e are also singular vertices in the minimal graph. Therefore, the number of singular vertices in the minimal graph is 4.

[0088] In some embodiments, in response to the number of singularities N being 0 in the one-stroke drawing algorithm, the intelligent gas pipeline inspection and management sub-platform can determine that the minimum graph can be drawn in one stroke without repeating line segments, and thus determine the target inspection route. Specifically, the starting point and ending point of the target inspection route can be determined to be any same node.

[0089] In some embodiments, in response to the number of singularities N in the one-stroke drawing algorithm being 2, the intelligent gas pipeline inspection and management sub-platform can determine that the minimum map can be drawn in one stroke without repeating line segments, and thus determine the target inspection route. Specifically, the starting point and ending point of the target inspection route can be determined to be 2 singularities.

[0090] In some embodiments, the intelligent gas pipeline inspection and management sub-platform can also respond to the fact that the number of singular points N in the one-stroke drawing algorithm is greater than 2, pair the singular points, and connect the paired singular points with connecting lines to determine the processed minimum graph; based on the processed minimum graph, determine the target inspection route.

[0091] The pairing principle can include a target inspection route determined based on the characteristics of the edges in the minimum graph and / or the last deep inspection time of at least one gas pipeline segment. For example, in the minimum graph, if the distance between the existing gas pipeline segments between nodes a and d is 30 meters, and the distance between the newly added gas pipeline segment (i.e., a gas pipeline segment that exists in the pipeline graph but not in the minimum graph) between nodes c and e is 25 meters, then nodes d and e, and nodes d and c, cannot be connected because there are neither "existing gas pipeline segments" nor "newly added gas pipeline segments". Therefore, nodes c and e, which are closer together, can be selected for connection. As another example, if the time intervals between the last deep inspection and nodes a, c, d, and e are 15 days, three months, one month, and six months, respectively, then node c, which has the longest time interval between the last deep inspection and node e, can be paired and connected. The result of the pairing connection is shown in the processed minimum graph.

[0092] Connecting lines are used to connect paired singularities. For example, in the processed minimal graph, node c is connected to node e by a connecting line, indicating that node c and node e are paired.

[0093] In some embodiments, in response to the number of singular vertices N in the one-stroke drawing algorithm being greater than 2, the intelligent gas pipeline inspection and management sub-platform can pair N or N-2 singular vertices together based on a pairing principle. The paired vertices are then reconnected using the original road as an edge, thereby determining the target inspection route. The reconnected path is the repeated path. For example, after pairing N singular vertices, the number of singular vertices in the processed minimum graph is 0. The intelligent gas pipeline inspection and management sub-platform can determine the target inspection route based on the method for determining the target inspection route when the number of singular vertices N is 0. As another example, after pairing N-2 singular vertices, the number of singular vertices in the processed minimum graph is 2. The intelligent gas pipeline inspection and management sub-platform can determine the target inspection route based on the method for determining the target inspection route when the number of singular vertices N is 2. Figure 5 For example, the processed minimum graph includes two singularities. The intelligent gas pipeline inspection and management sub-platform can determine the target inspection route based on the method of determining the number of singularities N = 2, taking node d as the starting point and node e as the ending point, and then determining at least one path drawn in one stroke without repeating line segments from the starting point to the ending point (i.e., node d → node a → node b → node c → node e), as the target inspection route.

[0094] When pairing gas pipelines, prioritize adding shorter pipeline segments with longer intervals between the last deep inspection to obtain the optimal inspection route.

[0095] Some embodiments in this specification determine the minimum diagram based on the pipeline diagram, and then determine the target inspection route through a one-stroke drawing algorithm. This can obtain the optimal inspection route, thereby improving efficiency and saving resources.

[0096] This specification includes a computer-readable storage medium that stores computer instructions that, when executed by a processor, implement a smart gas pipeline safety monitoring method.

[0097] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A smart gas pipeline safety monitoring method, characterized in that, This is implemented based on an intelligent gas pipeline safety monitoring IoT system. The IoT system includes, in sequence, an intelligent gas user platform, an intelligent gas service platform, an intelligent gas pipeline safety management platform, an intelligent gas sensor network platform, and an intelligent gas object platform. The intelligent gas pipeline safety management platform includes an intelligent gas data center and an intelligent gas pipeline inspection management sub-platform. The method is executed by the intelligent gas pipeline safety management platform and includes: The smart gas data center, based on the smart gas sensor network platform, obtains the transportation characteristics of at least one gas pipeline segment from the detection equipment corresponding to at least one gas pipeline segment in the preset area. The gas detection equipment is configured in the smart gas object platform. The intelligent gas pipeline inspection and management sub-platform is used for: The system obtains pipeline characteristics and transportation characteristics of at least one gas pipeline segment in a preset area from the smart gas data center, and predicts the inspection demand of the at least one gas pipeline segment based on the pipeline characteristics and transportation characteristics through an inspection demand prediction model, wherein the inspection demand prediction model is a machine learning model. Based on the inspection demand of the at least one gas pipeline segment, at least one target pipeline segment is determined; The at least one target pipeline segment is sent to the smart gas data center, and the target pipeline segment is further sent to the smart gas user platform based on the smart gas service platform; Based on the at least one gas pipeline segment, construct a pipeline diagram; Based on the pipeline diagram, a minimum graph containing the at least one target pipeline segment is determined. The minimum graph includes multiple connected subgraphs corresponding to the at least one target pipeline segment and the shortest path connecting the connected subgraphs. Based on the minimum graph, the target inspection route of the crawling robot is determined by a one-stroke drawing algorithm; Based on the target inspection route, a remote control command is generated and sent to the smart gas data center. Then, based on the smart gas sensor network platform, the remote control command is sent to the smart gas object platform to perform a deep inspection.

2. A smart gas pipeline safety monitoring IoT system, characterized in that, The Internet of Things (IoT) comprises a smart gas user platform, a smart gas service platform, a smart gas pipeline safety management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas pipeline safety management platform includes a smart gas data center and a smart gas pipeline inspection management sub-platform. The smart gas pipeline safety management platform is configured to perform the following operations: The smart gas data center, based on the smart gas sensor network platform, obtains the transportation characteristics of at least one gas pipeline segment from the detection equipment corresponding to at least one gas pipeline segment in the preset area. The gas detection equipment is configured in the smart gas object platform. The intelligent gas pipeline inspection and management sub-platform is configured as follows: The system obtains pipeline characteristics and transportation characteristics of at least one gas pipeline segment in a preset area from the smart gas data center, and predicts the inspection demand of the at least one gas pipeline segment based on the pipeline characteristics and transportation characteristics through an inspection demand prediction model, wherein the inspection demand prediction model is a machine learning model. Based on the inspection demand of the at least one gas pipeline segment, at least one target pipeline segment is determined; The at least one target pipeline segment is sent to the smart gas data center, and the target pipeline segment is further sent to the smart gas user platform based on the smart gas service platform; Based on the at least one gas pipeline segment, construct a pipeline diagram; Based on the pipeline diagram, a minimum graph containing the at least one target pipeline segment is determined. The minimum graph includes multiple connected subgraphs corresponding to the at least one target pipeline segment and the shortest path connecting the connected subgraphs. Based on the minimum graph, the target inspection route of the crawling robot is determined by a one-stroke drawing algorithm; Based on the target inspection route, a remote control command is generated and sent to the smart gas data center. Then, based on the smart gas sensor network platform, the remote control command is sent to the smart gas object platform to perform a deep inspection.

3. A smart gas pipeline safety monitoring device, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least a portion of the computer instructions to implement the method as claimed in claim 1.

4. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the intelligent gas pipeline safety monitoring method as described in claim 1.

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