Long-distance pipe network danger intelligent early warning system based on Internet platform

Abstract

The invention discloses a long-distance pipe network danger intelligent early warning system based on an internet platform, and relates to the technical field of pipeline early warning, and the system comprises an acquisition module which is used for carrying out the acquisition of pipeline operation data through at least one sensor disposed on a pipeline of a long-distance pipe network; the Internet platform is used for performing risk scoring on the pipeline operation data acquired by the acquisition module; the Internet platform is used for performing risk scoring on the pipeline operation data acquired by the acquisition module, determining a target inspection strategy corresponding to a target scoring result in a corresponding relationship between the scoring result and the inspection strategy according to the target scoring result, and establishing a target scheduling strategy according to the target inspection strategy; and the scheduling module is used for scheduling the inspection robot in any inspection module according to the target scheduling strategy. The safety and reliability of the oil and gas pipeline are remarkably improved, potential risks can be found and processed in time, stable operation of the oil and gas pipeline is guaranteed, and the method has important practical application value.

Description

Background Art

[0002] With the continuous advancement of oil and gas pipeline construction in my country, the safe operation of oil and gas pipelines has become a key link in ensuring national energy security. In recent years, the third-generation oil and gas pipeline technology has developed rapidly, mainly using advanced technologies such as the Internet, big data, and artificial intelligence to achieve comprehensive monitoring, intelligent optimization, and full-link management of pipelines. These new technologies have endowed oil and gas pipelines with significant characteristics such as high speed, high efficiency, safety, and environmental protection, which can effectively improve the operating efficiency and safety level of pipelines.

[0003] The existing early warning pipeline system conducts early warning management through real-time online monitoring of relevant monitoring data. Once an abnormal situation occurs, the system can immediately reflect the location and severity of the incident, and timely and hierarchically remind the relevant responsible personnel through SMS, email, pop-up pages, etc. The person in charge can determine whether it is necessary to initiate the corresponding process for review or disposal based on the early warning information. The early warning system will also record detailed information on historical early warning events, providing important analytical and diagnostic basis for the prediction, identification and evaluation of subsequent early warning events.

[0004] Although the existing early warning pipeline system has a certain early warning capability, it still has some shortcomings. First, most of the current early warning systems use a fixed risk assessment model, which cannot accurately assess the dynamic risk of the pipeline. This fixed model cannot be adjusted according to the actual operation of the pipeline, resulting in the accuracy and timeliness of the risk assessment being limited. Secondly, the inspection strategy lacks flexibility and cannot be adjusted according to changes in the operation of the pipeline section, resulting in low utilization efficiency of the inspection robot. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the prior art and specifically provide a long-distance pipeline network hazard intelligent early warning system based on an Internet platform, which is as follows:

[0006] 1) In the first aspect, the present invention provides an intelligent early warning system for long-distance pipeline network dangers based on an Internet platform, and the specific technical scheme is as follows:

[0007] A collection module, an Internet platform, at least one inspection module and a scheduling module;

[0008] The acquisition module is used to: collect pipeline operation data through at least one sensor arranged on a pipeline of the long-distance pipeline network;

[0009] The Internet platform is used to: perform risk scoring on the pipeline operation data collected by the collection module to obtain a target scoring result, determine a target inspection strategy corresponding to the target scoring result in a corresponding relationship between the scoring result and the inspection strategy according to the target scoring result, and establish a target scheduling strategy according to the target inspection strategy;

[0010] The scheduling module is used to schedule the inspection robot in any inspection module according to the target scheduling strategy.

[0011] The beneficial effects of the long-distance pipeline network hazard intelligent early warning system based on the Internet platform provided by the present invention are as follows:

[0012] By comprehensively using the acquisition module, Internet platform, inspection module and scheduling module, comprehensive monitoring and intelligent management of oil and gas pipelines are realized. The acquisition module is distributed on the transportation pipeline and can obtain pipeline operation data in real time and send it to the Internet platform. The built-in prediction module of the Internet platform calculates the pipeline risk score based on the real-time monitoring data to ensure the accuracy of risk assessment. The early warning module dynamically adjusts the inspection strategy according to the risk score and intelligently determines the scheduling strategy so that the inspection robot can conduct targeted inspections. The scheduling module optimizes the inspection robot according to the scheduling strategy to improve the utilization efficiency of the inspection robot. The system has the ability to intelligently adjust the risk assessment model and inspection strategy according to the pipeline operation status, effectively reducing unnecessary inspection workload and ensuring the reasonable allocation of inspection resources. Through the above technical means, the present invention significantly improves the safety and reliability of oil and gas pipelines, can timely discover and deal with potential risks, ensures the stable operation of oil and gas pipelines, and has important practical application value.

[0013] Based on the above solution, the present invention can also be improved as follows.

[0014] Further, any of the inspection modules is also used for:

[0015] The detection device configured on the inspection robot is used to obtain image data at abnormal positions inside the pipeline in real time, and the image data is uploaded to the Internet platform.

[0016] Further, when the target inspection strategy is to adjust the inspection frequency, the process of establishing the target scheduling strategy according to the target inspection strategy is:

[0017] According to the current inspection frequency, the minimum inspection robot demand is calculated, and the scheduling strategy is determined according to the minimum inspection robot demand and the preset demand, where the preset demand is the estimated inspection robot demand determined according to the historical target inspection strategy.

[0018] Further, the process of determining the scheduling strategy according to the minimum inspection robot demand and the preset demand is specifically as follows:

[0019] When the minimum inspection robot demand is greater than the preset demand, a robot having a difference between the minimum inspection robot demand and the preset demand is retrieved from the target inspection module that is closest to the inspection module.

[0020] 2) In the second aspect, the present invention also provides an intelligent early warning method for dangers in long-distance pipeline networks based on an Internet platform, and the specific technical scheme is as follows:

[0021] Collecting pipeline operation data through at least one sensor arranged on a pipeline of a long-distance pipeline network;

[0022] Performing risk scoring on the pipeline operation data collected by the acquisition module to obtain a target scoring result, determining a target inspection strategy corresponding to the target scoring result in a corresponding relationship between the scoring result and the inspection strategy according to the target scoring result, and establishing a target scheduling strategy according to the target inspection strategy;

[0023] The inspection robot in any inspection module is scheduled according to the target scheduling strategy.

[0024] Based on the above solution, the present invention can also be improved as follows.

[0025] Furthermore, it also includes:

[0026] The detection device configured on the inspection robot is used to obtain image data at abnormal positions inside the pipeline in real time, and the image data is uploaded to the Internet platform.

[0027] Further, when the target inspection strategy is to adjust the inspection frequency, the process of establishing the target scheduling strategy according to the target inspection strategy is:

[0028] According to the current inspection frequency, the minimum inspection robot demand is calculated, and the scheduling strategy is determined according to the minimum inspection robot demand and the preset demand, where the preset demand is the estimated inspection robot demand determined according to the historical target inspection strategy.

[0029] Further, the process of determining the scheduling strategy according to the minimum inspection robot demand and the preset demand is specifically as follows:

[0030] When the minimum inspection robot demand is greater than the preset demand, a robot having a difference between the minimum inspection robot demand and the preset demand is retrieved from the target inspection module that is closest to the inspection module.

[0031] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, wherein the processor is coupled to a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any of the above methods.

[0032] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above methods.

[0033] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0035] Figure 1 This is one of the structural framework diagrams of an intelligent early warning system for dangers in a long-distance pipeline network based on an Internet platform according to an embodiment of the present invention;

[0036] Figure 2 This is a second structural framework diagram of an intelligent early warning system for dangers in a long-distance pipeline network based on an Internet platform according to an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of a flow chart of an intelligent early warning method for dangers in a long-distance pipeline network based on an Internet platform according to an embodiment of the present invention;

[0038] Figure 4 The present invention is a structural framework diagram of an electronic device. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] like Figure 1 as well as Figure 2 As shown, an intelligent early warning system for dangers in a long-distance pipeline network based on an Internet platform according to an embodiment of the present invention includes the following devices:

[0041] A collection module, an Internet platform, at least one inspection module and a scheduling module;

[0042] The acquisition module is used to: collect pipeline operation data through at least one sensor arranged on a pipeline of the long-distance pipeline network;

[0043] The Internet platform is used to: perform risk scoring on the pipeline operation data collected by the collection module to obtain a target scoring result, determine a target inspection strategy corresponding to the target scoring result in a corresponding relationship between the scoring result and the inspection strategy according to the target scoring result, and establish a target scheduling strategy according to the target inspection strategy;

[0044] The scheduling module is used to schedule the inspection robot in any inspection module according to the target scheduling strategy.

[0045] The beneficial effects of the long-distance pipeline network hazard intelligent early warning system based on the Internet platform provided by the present invention are as follows:

[0046] By comprehensively using the acquisition module, Internet platform, inspection module and scheduling module, comprehensive monitoring and intelligent management of oil and gas pipelines are realized. The acquisition module is distributed on the transportation pipeline and can obtain pipeline operation data in real time and send it to the Internet platform. The built-in prediction module of the Internet platform calculates the pipeline risk score based on the real-time monitoring data to ensure the accuracy of risk assessment. The early warning module dynamically adjusts the inspection strategy according to the risk score and intelligently determines the scheduling strategy so that the inspection robot can conduct targeted inspections. The scheduling module optimizes the inspection robot according to the scheduling strategy to improve the utilization efficiency of the inspection robot. The system has the ability to intelligently adjust the risk assessment model and inspection strategy according to the pipeline operation status, effectively reducing unnecessary inspection workload and ensuring the reasonable allocation of inspection resources. Through the above technical means, the present invention significantly improves the safety and reliability of oil and gas pipelines, can timely discover and deal with potential risks, ensures the stable operation of oil and gas pipelines, and has important practical application value.

[0047] The acquisition module collects pipeline operation data through sensors installed at key locations on each pipeline in the long-distance pipeline network, where key locations include but are not limited to the starting point, middle point, end point, station, etc. of the pipeline. Sensors include but are not limited to: pressure sensors, temperature sensors, vibration sensors and flow sensors. Pipeline operation data includes but is not limited to: pressure values, temperature values, vibration frequencies, flow values ​​at the entrances and exits of the station, and pressure values, temperature values, flow values, etc. of a certain section of the pipeline. The above-collected pipeline operation data is transmitted to the Internet platform through wired or wireless network data transmission.

[0048] The Internet platform uses a built-in prediction module to perform risk scoring on the pipeline operation data collected by the collection module, and outputs the target score result to the scheduling module. The early warning module also determines the inspection strategy based on the pipeline risk score, and determines the scheduling strategy based on the inspection strategy; that is, the inspection strategy and scheduling strategy are determined based on the risk score.

[0049] Specifically, the pipeline risk score is calculated, including:

[0050] Assume that two adjacent acquisition modules M i and M i+1 , collect the following data respectively:

[0051] P i ,T i ,V i ,F i Respectively represent modules M i Pressure, temperature, vibration, and flow data;

[0052] P i+1 ,T i+1 ,V i+1 ,F i+1 Respectively represent modules M i+1 Pressure, temperature, vibration, and flow data;

[0053] Calculate the absolute value of the difference between the same type of data of two adjacent acquisition modules. The absolute value of the difference reflects the changes in the pipelines of the two adjacent acquisition modules in this section:

[0054] ΔP=|P i -P i+1 |

[0055] ΔT=|T i -T i+1 |

[0056] ΔV=|V i -V i+1 |

[0057] ΔF=|F i -F i+1 |

[0058] Normalize the absolute value of the difference:

[0059]

[0060] Among them, P max ,T max ,V max ,F max Respectively represent the maximum values ​​of normal ranges of pipeline pressure, temperature, vibration and flow;

[0061] Set weight ω P ,ω T ,ω V ,ω F They represent the contribution weights of pressure, temperature, vibration, and flow to the failure risk, respectively. The risk score R i,i+1 The calculation of is as follows:

[0062] R i,i+1 =K(ω P ·ΔP'+ω T ·ΔT'+ω V ·ΔV'+ω F ·ΔF')

[0063] Where K represents the adjustment factor, which is used to correct the calculation of the risk score based on the actual historical failure risk.

[0064] The early warning module divides risk scores into different levels:

[0065] The first risk level (low risk), the pipeline operates normally without obvious abnormalities. The strategy of adjusting the inspection frequency is adopted to increase or decrease the number of inspections to ensure pipeline safety;

[0066] The second risk level (high risk) means that the pipeline has potential risks and needs rapid inspection. A rapid inspection strategy is adopted, and an inspection robot is immediately arranged to conduct a detailed inspection.

[0067] For example, a long-distance pipeline is selected for field testing. The pipeline is 100 kilometers long, and a collection module is set every 10 kilometers, with a total of 10 collection modules. Each collection module collects pipeline operation data in real time and transmits it to the Internet platform.

[0068] The acquisition module is installed on the pipeline and collects data 120 times per minute. The collected data includes pressure, temperature, vibration and flow, and is transmitted to the Internet platform through the wireless network. After the Internet platform receives the data, the prediction module calculates the pipeline risk score according to the above formula. Assume that at a certain moment, the data collected by the acquisition modules M1 and M2 are as follows:

[0069] P1=50MPa, T1=30℃, V1=0.2m / s, F1=100m 3 / s

[0070] P2=48MPa, T2=32℃, V2=0.25m / s, F2=95m 3 / s

[0071] Calculate the absolute value of the difference in data:

[0072] ΔP=|50-48|=2MPa

[0073] ΔT=|30-32|=2℃

[0074] ΔV=|0.2-0.25|=0.05m / s

[0075] ΔF=|100-95|=5m 3 / s

[0076] Assume that the maximum values ​​of the normal ranges of pressure, temperature, vibration and flow are:

[0077] P max =100MPa, T max =100℃,V max =1m / s, F max =200m 3 / s

[0078] Standardized Difference:

[0079]

[0080]

[0081]

[0082]

[0083] Set weights:

[0084] ω P =0.4,ω T =0.3,ω V =0.2,ω F =0.1

[0085] Adjustment factor K = 1.5;

[0086] The risk score is then calculated as follows:

[0087] R 1,2 =1.5(0.4·0.02+0.3·0.02+0.2·0.05+0.1·0.025)=0.046875.

[0088] More specifically, the steps of establishing a scoring correction model by combining multiple deep learning models and adjusting the factor K according to the data output of the acquisition module are as follows:

[0089] Collect historical fault data, including the specific circumstances of the fault and various types of data from the acquisition module. Preprocess and extract features from the collected data. Normalize or standardize the data to ensure that the numerical range of the input data is suitable for the deep learning model. Select a combination of multiple deep learning models, convolutional neural networks, recurrent neural networks, and feedforward neural networks to establish a score correction model. Improve the accuracy of predictions by combining the advantages of different models. Convolutional Neural Network (CNN): Extracts local features from the data and is suitable for processing local patterns of time series data. Recurrent Neural Network (RNN): Processes sequence data and captures the time dependency of the data. Feedforward Neural Network (FNN): Combines features from CNN and RNN for comprehensive processing and outputs the final adjustment factor K.

[0090] Specific steps:

[0091] Input layer: input various data (pressure, temperature, vibration, flow, etc.) of the acquisition module.

[0092] Convolutional layer (CNN): Convolution operations are performed on the input data through multiple convolution kernels to extract local features.

[0093] Pooling layer (CNN): Reduce the dimension of the data through maximum pooling or average pooling, retaining important features.

[0094] Recurrent layer (RNN): The pooled features are processed by RNN (such as LSTM or GRU) to capture temporal dependencies.

[0095] Fully connected layer (FNN): Features from CNN and RNN are connected to the fully connected layer for further feature extraction and processing.

[0096] Output layer: Output adjustment factor K.

[0097] The technical effect of introducing the adjustment factor K in this embodiment significantly improves the accuracy and practicality of risk score prediction. This adjustment factor is modified according to the actual historical fault risk data, which is mainly reflected in the following aspects: First, the adjustment factor K can dynamically adjust the algorithm for calculating the risk score of the prediction module according to the specific operation status of the pipeline and the historical fault records. By comprehensively considering the actual operation data of the pipeline and the past failure experience, the introduction of the adjustment factor K makes the risk assessment more accurate and real-time, and can more accurately reflect the current safety status of the pipeline. Secondly, the adjustment factor K can also balance the influence weights of different factors in the risk assessment process and adjust the importance of various parameters according to the specific situation. This dynamic adjustment capability makes the system more flexible and adaptable, able to cope with different pipeline conditions and environmental changes, and improves the applicability and universality of the prediction model. In addition, the introduction of the adjustment factor K also improves the accuracy and timeliness of the system's early warning. By real-time monitoring and analysis of historical data, the system can promptly discover potential risk points and problems, and take effective measures in advance for prevention and repair, thereby effectively reducing the possibility of pipeline accidents and ensuring the safety and stability of pipeline operation. Therefore, the application of adjustment factor K in the long-distance pipeline network hazard intelligent early warning system based on the Internet platform not only optimizes the risk assessment algorithm and model, but also improves the system's response speed and decision-making accuracy, providing important technical support and guarantee for pipeline operation and management.

[0098] Assume that the risk level is divided into two levels:

[0099] First risk level (score < 0.08): The inspection strategy is to adjust the inspection frequency.

[0100] Second risk level (score ≥ 0.08): The inspection strategy is rapid inspection.

[0101] Further subdivide the risk level and determine the inspection strategy based on the pipeline risk score:

[0102] If the risk score is greater than 0.07, increase the inspection frequency.

[0103] If the risk score is between 0.05 and 0.07, maintain the original inspection frequency.

[0104] If the risk score is less than or equal to 0.05, reduce the inspection frequency.

[0105] In this example, the risk score is 0.046875, which belongs to the first risk level, but because the score is less than 0.05, the inspection strategy is to reduce the inspection frequency.

[0106] More specifically, in this embodiment, when the risk score of the pipeline reaches or exceeds the second risk level (score ≥ 0.08), the system triggers the fast inspection strategy. In order to improve the inspection efficiency, the Internet platform will further predict the fault location, and the inspection robot will go directly to the predicted fault location for inspection. Fast inspection strategy triggering: Assume that at a certain moment, the pipeline operation data received by the Internet platform is analyzed and the calculated risk score is 0.09, which exceeds the trigger threshold of 0.08 for fast inspection. At this time, the system automatically starts the fast inspection strategy. Fault location prediction: The prediction module of the Internet platform predicts the fault location. Assume that the fault location predicted by the model is at the 45th kilometer of the pipeline. Inspection robot scheduling: Once the prediction module determines the fault location, the scheduling module of the Internet platform directs the inspection robot to the predicted fault location according to the inspection strategy. Robot selection: Select an available inspection robot from the nearest inspection module. For example, the inspection modules at the 40th kilometer and the 55th kilometer each have two inspection robots, and the system selects the robot closest to the 45th kilometer for scheduling. Path planning: The system calculates the optimal path so that the inspection robot can quickly and safely reach the predicted fault location. Task assignment: The scheduling module sends task instructions to the selected inspection robot, and the robot immediately goes to the target location after receiving the instructions. After the inspection robot arrives at the predicted fault location, it performs the following tasks: On-site detection: Use the various sensors on board (such as ultrasonic, infrared imaging, high-definition cameras, etc.) to conduct detailed on-site detection and collect more environmental and status data. Fault confirmation: Further confirm whether there is a fault through the data of on-site detection. If there is indeed a fault, record the specific type, severity and location of the fault. Data upload: The inspection robot uploads the detection data and fault report to the Internet platform in real time for subsequent processing and decision-making reference. After the Internet platform receives the report from the inspection robot, the system analyzes and confirms the fault situation and generates a detailed fault report containing information such as the specific location, fault type and severity. According to the fault report, the system automatically triggers the corresponding emergency response measures, such as notifying the maintenance team, starting the emergency repair procedure, etc., to ensure the rapid repair and safe operation of the pipeline.

[0107] Through this rapid inspection strategy and fault location prediction, the system can quickly respond to high-risk situations, accurately locate potential fault points, and significantly improve inspection efficiency and pipeline safety. The inspection robot goes directly to the predicted fault location, which not only shortens the response time, but also reduces the workload of comprehensive inspections, optimizes resource utilization, and improves the maintenance and management level of the entire long-distance pipeline network.

[0108] Determine the scheduling strategy according to the inspection strategy, and the specific process also includes:

[0109] If the inspection strategy is to adjust the inspection frequency, the minimum demand quantity of the inspection robots of the inspection module is calculated according to the inspection frequency, and the scheduling strategy is determined according to the minimum demand quantity and the preset quantity.

[0110] In another embodiment of the present solution, the scheduling strategy is determined according to the minimum demand and the preset quantity, and the specific process further includes:

[0111] If the minimum demand is greater than the preset number, the difference between the minimum demand and the preset number is calculated, and the scheduling strategy is to schedule the inspection robots of the difference number from the inspection module that is closest to the inspection module and for which the preset number is greater than the minimum demand.

[0112] The scheduling module dispatches inspection robots according to the inspection strategy. Assuming that 2 inspection robots are configured every 10 kilometers, if the score is greater than 0.07, the inspection frequency will be increased and additional robots will be dispatched to high-risk areas.

[0113] Specifically, in order to effectively manage and dispatch inspection robots to meet facility inspection needs, the following specific embodiments are designed to cope with demand changes and strategy execution in different situations:

[0114] 1. Preliminary determination of inspection strategy and scheduling strategy

[0115] First, formulate an inspection strategy based on the characteristics of the facility and the inspection requirements. The core of the inspection strategy includes determining the inspection frequency and the distribution of inspection modules. While determining the inspection frequency, it is necessary to calculate the minimum number of inspection robots required for each inspection module (hereinafter referred to as the minimum requirement).

[0116] 2. Calculate the minimum demand and preset quantity

[0117] For each inspection module, the minimum number of inspection robots required for the module is calculated based on the set inspection frequency. This step involves determining the specific number based on the size of the facility, the inspection objectives, and the expected inspection efficiency. Assuming there are several inspection modules, the minimum required quantity for each module has been calculated.

[0118] At the same time, a preset number of inspection modules is set for each module, that is, the number of inspection robots that each module should have under ideal circumstances.

[0119] 3. Determine the scheduling strategy

[0120] Case 1: The minimum demand quantity is less than or equal to the preset quantity

[0121] In this case, the scheduling strategy is relatively simple, and it only needs to keep the number of inspection robots in each inspection module equal to the preset number. This ensures the normal operation of each module under the inspection frequency.

[0122] Case 2: Minimum demand is greater than the preset quantity

[0123] When the minimum demand is greater than the preset number, it indicates that the inspection demand of some inspection modules exceeds expectations. In this case, the following specific measures need to be taken:

[0124] Calculate the difference: For each inspection module, calculate the difference between the minimum demand and the preset quantity.

[0125] Priority scheduling: From those inspection modules that are closest to the inspection module and whose preset quantity is greater than the minimum demand, inspection robots with the difference quantity will be prioritized for scheduling.

[0126] For example, suppose that in module A, the minimum demand is 5 robots and the preset number is 3. The difference is 2 robots. In module B, the minimum demand is 4 robots and the preset number is 6. 2 robots can be dispatched to other inspection modules.

[0127] Therefore, according to the above situation, two additional robots will be dispatched from module B to module A to ensure that the module can be inspected as required. No additional robots are needed in module B because the preset number is enough to meet the minimum demand.

[0128] Specifically, after receiving the dispatch command, the dispatch drone quickly loaded the inspection robot and flew from module B to module A. The dispatch drone uses automatic flight path planning technology to avoid obstacles and reach the destination as quickly as possible. After arriving at the high-risk area, the dispatch drone landed safely at the designated location, and the inspection robot immediately started working and conducted a comprehensive inspection.

[0129] Through the specific implementation of the above scheduling strategy, the allocation of inspection robots can be effectively managed to ensure the safety and operation efficiency of the facilities. This strategy not only meets the changes in inspection needs, but also can achieve optimal allocation and utilization under limited resources.

[0130] The scheduling module is used to schedule the inspection robot according to the scheduling strategy.

[0131] Specifically, the scheduling module schedules and manages the work of the inspection robot according to the scheduling strategy generated by the early warning module. The scheduling module comprehensively considers the location, status and work tasks of the inspection robot, reasonably allocates inspection tasks, and ensures that the inspection work is carried out efficiently.

[0132] The inspection module is provided with a preset number of inspection robots for conducting inspections according to the inspection strategy;

[0133] Specifically, the inspection module is equipped with several inspection robots, which have autonomous navigation and intelligent detection functions. The inspection robots perform inspections according to the inspection strategies provided by the early warning module. The robots are equipped with high-definition cameras, ultrasonic detectors and infrared thermal imagers, which can detect problems such as appearance defects, internal corrosion and leakage of pipelines. The inspection data is uploaded to the Internet platform in real time for further analysis and processing.

[0134] Determine the scheduling strategy according to the inspection strategy, and the specific process also includes:

[0135] If the inspection strategy is to adjust the inspection frequency, the minimum demand quantity of the inspection robots of the inspection module is calculated according to the inspection frequency, and the scheduling strategy is determined according to the minimum demand quantity and the preset quantity.

[0136] Determining a scheduling strategy according to the minimum demand and the preset quantity, the specific process also includes:

[0137] If the minimum demand is greater than the preset number, the difference between the minimum demand and the preset number is calculated, and the scheduling strategy is to schedule the inspection robots of the difference number from the inspection module that is closest to the inspection module and for which the preset number is greater than the minimum demand.

[0138] Specifically, according to the inspection strategy, the early warning module formulates the scheduling strategy:

[0139] If the inspection strategy is to adjust the inspection frequency, the early warning module calculates the minimum demand for inspection robots and determines the scheduling strategy based on the preset quantity.

[0140] If the minimum demand is greater than the preset quantity, the early warning module calculates the difference and dispatches robots from the nearest inspection module with sufficient number of robots to make up for the demand.

[0141] The scheduling module reasonably allocates inspection tasks according to the scheduling strategy. The inspection robot performs autonomous navigation and inspection according to the scheduling instructions. The robot is equipped with high-definition cameras, ultrasonic detectors and infrared thermal imagers, which can detect problems such as appearance defects, internal corrosion and leakage of pipelines. During the inspection process, the robot uploads the detected data to the Internet platform in real time. The Internet platform analyzes and processes the inspection data. If an abnormality is found, the risk score is updated in time, and the inspection and scheduling strategies are re-formulated. The entire system ensures the safe operation of the pipeline through continuous cycles of data collection, analysis and feedback.

[0142] The long-distance pipeline network hazard intelligent early warning system based on the Internet platform of this embodiment can realize real-time monitoring and risk assessment of pipeline operation status. Through closed-loop management of data collection, analysis and inspection, the system can timely discover and warn potential pipeline risks to prevent accidents. The system's intelligent scheduling and autonomous inspection functions improve the efficiency and accuracy of inspection work, reduce manpower input and management costs, and ensure the safe operation of long-distance pipelines.

[0143] Furthermore, the inspection module is also used for:

[0144] The detection device configured on the inspection robot is used to obtain image data at abnormal positions inside the pipeline in real time, and the image data is uploaded to the Internet platform.

[0145] Further, when the target inspection strategy is to adjust the inspection frequency, the process of establishing the target scheduling strategy according to the target inspection strategy is:

[0146] According to the current inspection frequency, the minimum inspection robot demand is calculated, and the scheduling strategy is determined according to the minimum inspection robot demand and the preset demand, where the preset demand is the estimated inspection robot demand determined according to the historical target inspection strategy.

[0147] Further, the process of determining the scheduling strategy according to the minimum inspection robot demand and the preset demand is specifically as follows:

[0148] When the minimum inspection robot demand is greater than the preset demand, a robot having a difference between the minimum inspection robot demand and the preset demand is retrieved from the target inspection module that is closest to the inspection module.

[0149] like Figure 3 As shown, the present invention also provides an intelligent early warning method for long-distance pipeline network danger based on the Internet platform, and the specific technical scheme is as follows:

[0150] S1, collecting pipeline operation data through at least one sensor arranged on a pipeline of a long-distance pipeline network;

[0151] S2, performing risk scoring on the pipeline operation data collected by the collection module to obtain a target scoring result, determining a target inspection strategy corresponding to the target scoring result in a correspondence relationship between the scoring result and the inspection strategy according to the target scoring result, and establishing a target scheduling strategy according to the target inspection strategy;

[0152] S3, scheduling the inspection robot in any inspection module according to the target scheduling strategy.

[0153] Furthermore, it also includes:

[0154] The detection device configured on the inspection robot is used to obtain image data at abnormal positions inside the pipeline in real time, and the image data is uploaded to the Internet platform.

[0155] Further, when the target inspection strategy is to adjust the inspection frequency, the process of establishing the target scheduling strategy according to the target inspection strategy is:

[0156] According to the current inspection frequency, the minimum inspection robot demand is calculated, and the scheduling strategy is determined according to the minimum inspection robot demand and the preset demand, where the preset demand is the estimated inspection robot demand determined according to the historical target inspection strategy.

[0157] Further, the process of determining the scheduling strategy according to the minimum inspection robot demand and the preset demand is specifically as follows:

[0158] When the minimum inspection robot demand is greater than the preset demand, a robot having a difference between the minimum inspection robot demand and the preset demand is retrieved from the target inspection module that is closest to the inspection module.

[0159] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0160] It should be noted that the beneficial effects of the intelligent early warning method for dangers in long-distance pipeline networks based on the Internet platform provided in the above-mentioned embodiment are the same as the beneficial effects of the intelligent early warning system for dangers in long-distance pipeline networks based on the Internet platform, which will not be repeated here. In addition, when the system provided in the above-mentioned embodiment realizes its functions, it only uses the division of the above-mentioned functional modules as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above-mentioned embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0161] like Figure 4 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above methods, specifically:

[0162] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements the intelligent early warning method for dangers of long-distance pipeline networks based on the Internet platform provided in the above-mentioned embodiment. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for realizing the functions of the device, which will not be repeated here.

[0163] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any one of the above methods.

[0164] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0165] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above methods.

[0166] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0167] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0168] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0169] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An intelligent early warning system for long-distance pipeline network danger based on the Internet platform, characterized in that: include: A collection module, an Internet platform, at least one inspection module and a scheduling module; The acquisition module is used to: collect pipeline operation data through at least one sensor arranged on a pipeline of the long-distance pipeline network; The Internet platform is used to: perform risk scoring on the pipeline operation data collected by the collection module to obtain a target scoring result, determine a target inspection strategy corresponding to the target scoring result in a corresponding relationship between the scoring result and the inspection strategy according to the target scoring result, and establish a target scheduling strategy according to the target inspection strategy; The scheduling module is used to schedule the inspection robot in any inspection module according to the target scheduling strategy.

2. According to the Internet platform-based long-distance pipeline network hazard intelligent early warning system according to claim 1, it is characterized in that: Any of the inspection modules is also used for: The detection device configured on the inspection robot is used to obtain image data at abnormal positions inside the pipeline in real time, and the image data is uploaded to the Internet platform.

3. According to the Internet platform-based long-distance pipeline network hazard intelligent early warning system of claim 1, it is characterized in that: When the target inspection strategy is to adjust the inspection frequency, the process of establishing the target scheduling strategy according to the target inspection strategy is as follows: According to the current inspection frequency, the minimum inspection robot demand is calculated, and the scheduling strategy is determined according to the minimum inspection robot demand and the preset demand, where the preset demand is the estimated inspection robot demand determined according to the historical target inspection strategy.

4. According to claim 3, the intelligent early warning system for long-distance pipeline network danger based on the Internet platform is characterized in that: The process of determining the scheduling strategy according to the minimum inspection robot demand and the preset demand is as follows: When the minimum inspection robot demand is greater than the preset demand, a robot having a difference between the minimum inspection robot demand and the preset demand is retrieved from the target inspection module that is closest to the inspection module.

5. An intelligent early warning method for long-distance pipeline network danger based on the Internet platform, characterized in that: include: Collecting pipeline operation data through at least one sensor arranged on a pipeline of a long-distance pipeline network; Performing risk scoring on pipeline operation data to obtain a target scoring result, determining a target inspection strategy corresponding to the target scoring result in a correspondence relationship between the scoring result and the inspection strategy according to the target scoring result, and establishing a target scheduling strategy according to the target inspection strategy; The inspection robot in any inspection module is scheduled according to the target scheduling strategy.

6. According to claim 5, a long-distance pipeline network hazard intelligent early warning method based on the Internet platform is characterized in that: Also includes: The detection device configured on the inspection robot is used to obtain image data at abnormal positions inside the pipeline in real time, and the image data is uploaded to the Internet platform.

7. According to claim 5, a long-distance pipeline network hazard intelligent early warning method based on an Internet platform is characterized in that: When the target inspection strategy is to adjust the inspection frequency, the process of establishing the target scheduling strategy according to the target inspection strategy is as follows: According to the current inspection frequency, the minimum inspection robot demand is calculated, and the scheduling strategy is determined according to the minimum inspection robot demand and the preset demand, where the preset demand is the estimated inspection robot demand determined according to the historical target inspection strategy.

8. The method for intelligent early warning of danger in long-distance pipeline network based on Internet platform according to claim 7 is characterized in that: The process of determining the scheduling strategy according to the minimum inspection robot demand and the preset demand is specifically as follows: When the minimum inspection robot demand is greater than the preset demand, a robot having a difference between the minimum inspection robot demand and the preset demand is retrieved from the target inspection module that is closest to the inspection module.

9. An electronic device, characterized in that: The electronic device comprises a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the method according to any one of claims 5 to 8.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor to enable the computer to implement the method according to any one of claims 5 to 8.

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