Integrated pipeline management system, method, equipment and medium

Abstract

The invention relates to the technical field of pipeline management, and discloses an integrated pipeline management system, method and equipment and a medium. The system is provided with the data acquisition and monitoring module, the data processing and analysis module and the abnormity monitoring and alarm module, real-time and accurate monitoring and analysis of the running state of the pipeline are realized by fusing advanced technologies of the Internet of Things, big data, artificial intelligence and the like, potential problems and faults are accurately predicted, and the working efficiency is improved. The accuracy and foresight of pipeline management are greatly improved, the consumption of human resources is reduced, and intelligent management of the pipeline is effectively realized.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline management, and in particular to an integrated pipeline management system, method, equipment and medium. Background Art

[0002] In many fields such as energy, chemical industry and municipal administration, pipelines are important carriers of material transportation. Their safe, stable and efficient operation is of vital importance to ensuring the normal order of production and life.

[0003] In current pipeline management technology, pipelines are generally managed by manual regular inspections and empirical judgment based on limited data, which consumes a lot of human resources and cannot effectively achieve intelligent management. Summary of the invention

[0004] The present invention provides an integrated pipeline management system, method, equipment and medium, which are used to solve the defects in related technologies that pipeline management is performed by manual regular inspection and empirical judgment based on limited data, which consumes a lot of human resources and cannot effectively realize intelligent management. It can reduce human resource consumption and effectively realize intelligent management of pipelines.

[0005] In a first aspect, the present invention provides an integrated pipeline management system, comprising: A data acquisition and monitoring module, including a plurality of sensors installed inside, outside and within a preset range of a target pipeline, for collecting pipeline operation data and environmental data through the plurality of sensors; A data processing and analysis module, connected to the data acquisition and monitoring module, for receiving the pipeline operation data and the environmental data sent by the data acquisition and monitoring module, and performing data cleaning, data sorting, data storage and data analysis on the pipeline operation data and the environmental data in sequence to generate an evaluation report; The abnormal monitoring and alarm module is connected to the data processing and analysis module, and is used to receive the evaluation report sent by the data processing and analysis module, input the evaluation report into the trained abnormal warning model to perform abnormal detection on the target pipeline, and when an abnormality is detected in the target pipeline, determine the abnormality type of the target pipeline, determine the corresponding alarm level according to the abnormality type, and output the corresponding alarm signal according to the alarm level to prompt relevant staff to process the target pipeline.

[0006] Optionally, the pipeline operation data includes pressure data, temperature data, flow data, vibration data and / or displacement data, and the environmental data includes soil moisture and / or gas concentration.

[0007] Optionally, the system further includes: The emergency response and disposal module is connected to the abnormal monitoring and alarm module, and is used to respond to the alarm signal sent by the abnormal monitoring and alarm module and perform emergency processing according to the pre-built emergency response system, emergency plan and process.

[0008] Optionally, the system further includes: The pipeline integrity management module is connected to the data acquisition and monitoring module, the emergency response and disposal module and the emergency response and disposal module, and is used to perform integrity assessment and management based on the pipeline operation data and the environmental data sent by the data acquisition and monitoring module and the assessment report sent by the data processing and analysis module, so as to identify pipeline defects and potential risks, generate maintenance plans and risk control measures corresponding to the pipeline defects and potential risks, and send the identified pipeline defects and potential risks to the emergency response and disposal module so that the emergency response and disposal module can perform emergency processing.

[0009] Optionally, the system further includes: The collaborative operation and information sharing module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module and multiple department devices, and is used for collaborative operation and information sharing between the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module and each of the department devices.

[0010] Optionally, the system further includes: The self-learning and optimization module is connected to the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module and the pipeline integrity management module, and is used to optimize the abnormal warning model based on the pipeline operation data and the environmental data sent by the data acquisition and monitoring module, and the evaluation report sent by the data processing and analysis module to obtain the optimized abnormal warning model and send it to the abnormal monitoring and alarm module. It is also used to optimize the emergency response system, emergency plan and process in the emergency response and disposal module, and optimize the maintenance plan and risk control countermeasures generated by the pipeline integrity management module.

[0011] Optionally, the system further includes: The user interface and interaction module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module, the collaborative operation and information sharing module and the self-learning and optimization module, and is used to interact with the user based on the user authority while adapting to the screen size and resolution of the user terminal device.

[0012] In a second aspect, the present invention provides an integrated pipeline management method, which is applied to the system described in the first aspect or any corresponding embodiment thereof, and the method comprises: Collect pipeline operation data and environmental data through multiple sensors installed inside, outside and within a preset range of the target pipeline; Performing data cleaning, data sorting, data storage and data analysis on the pipeline operation data and the environmental data in sequence to generate an evaluation report; Inputting the evaluation report into a trained abnormality warning model to perform abnormality detection on the target pipeline, and when an abnormality is detected in the target pipeline, determining the abnormality type of the target pipeline; The corresponding alarm level is determined according to the abnormality type, and the corresponding alarm signal is output according to the alarm level to prompt relevant staff to handle the pipeline.

[0013] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the integrated pipeline management method of the second aspect above by executing the computer instructions.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the integrated pipeline management method of the second aspect.

[0015] The integrated pipeline management system, method, equipment and medium provided by the present invention are provided with a data acquisition and monitoring module, a data processing and analysis module and an abnormality monitoring and alarm module. By integrating advanced Internet of Things, big data, artificial intelligence and other technologies, real-time and accurate monitoring and analysis of pipeline operation status can be achieved, potential problems and faults can be accurately predicted, the accuracy and foresight of pipeline management can be greatly improved, human resource consumption can be reduced, and intelligent management of pipelines can be effectively realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of the structure of an integrated pipeline management system provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of another integrated pipeline management system provided by an embodiment of the present invention; Figure 3 A flow chart of an integrated pipeline management method provided by an embodiment of the present invention; Figure 4 A flow chart of another integrated pipeline management method provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In order to solve the problem in related technologies that pipeline management is performed by manual regular inspections and empirical judgments based on limited data, which consumes a lot of human resources and cannot effectively realize intelligent management, the integrated pipeline management system, method, equipment and medium proposed in this embodiment can be provided with a data acquisition and monitoring module, a data processing and analysis module and an abnormality monitoring and alarm module. By integrating advanced Internet of Things, big data and artificial intelligence technologies, real-time and accurate monitoring and analysis of pipeline operation status can be achieved, potential problems and faults can be accurately predicted, the accuracy and foresight of pipeline management can be greatly improved, human resource consumption can be reduced, and intelligent management of pipelines can be effectively realized.

[0020] Combine the following Figure 1-Figure 2 The integrated pipeline management system of the present invention is described.

[0021] like Figure 1 As shown, this embodiment proposes a first integrated pipeline management system, which may include: The data acquisition and monitoring module 101 includes a plurality of sensors installed inside, outside and within a preset range of the target pipeline, and is used to collect pipeline operation data and environmental data through the plurality of sensors; The data processing and analysis module 102 is connected to the data acquisition and monitoring module 101, and is used to receive the pipeline operation data and environmental data sent by the data acquisition and monitoring module 101, and sequentially perform data cleaning, data sorting, data storage and data analysis on the pipeline operation data and environmental data to generate an evaluation report; The abnormal monitoring and alarm module 103 is connected to the data processing and analysis module 102, and is used to receive the evaluation report sent by the data processing and analysis module 102, and input the evaluation report into the trained abnormal warning model to perform abnormal detection on the target pipeline. When an abnormality is detected in the target pipeline, the abnormality type of the target pipeline is determined, and the corresponding alarm level is determined according to the abnormality type. According to the alarm level, the corresponding alarm signal is output to prompt relevant staff to process the target pipeline.

[0022] It should be noted that the data acquisition and monitoring module 101 can be used to collect pipeline operation data and environmental data in real time. Optionally, the pipeline operation data includes pressure data, temperature data, flow data, vibration data and / or displacement data, and the environmental data includes soil moisture and / or gas concentration.

[0023] Among them, the pressure data collection calculation formula is: .

[0024] in, P Indicates the pressure value. V Indicates the sensor output voltage, k 1 and k 2 is the calibration coefficient of the pressure sensor; The temperature data collection calculation formula is: .

[0025] in, T Indicates the temperature value, R Indicates thermistor resistance, R 0 Indicates the resistance of the thermistor at a specific reference temperature. The thermistor is a temperature-sensitive resistor whose resistance value changes significantly with temperature. It is generally divided into positive temperature coefficient thermistors and negative temperature coefficient thermistors. k 3 and k 4 is the calibration coefficient of the temperature sensor.

[0026] The calibration factor of the pressure sensor installed on the pipeline k 1 Set to 0.01, k 2 Set to 5. Sensor output voltage V is 10 volts, according to the pressure data collection calculation formula P =0.01× V + 5, the pressure value at this time can be calculated P is 5.1 standard pressure units; in the temperature sensor used, the resistance of the thermistor is R is 1000 ohms, R 0 For 2000 ohms, the calibration factor k 3 is 0.1, k 4 is 20. According to the temperature data collection calculation formula: ; .

[0027] Calculated temperature value: .

[0028] T The unit is Celsius. It should be noted that this embodiment can accurately collect pressure and temperature data in the pipeline, and provide reliable basic data for subsequent pipeline operation analysis and management.

[0029] Among them, the data acquisition and monitoring module 101 includes multiple sensors installed inside the pipeline, outside the pipeline and within a preset range to build a sensor network. Optionally, in this embodiment, pressure sensors and temperature sensors can be installed every 50 kilometers inside the target pipeline to monitor the gas pressure and temperature changes inside the target pipeline; vibration sensors and displacement sensors can be installed every 100 kilometers outside the target pipeline to detect possible vibration and displacement of the target pipeline; soil moisture sensors are installed every 20 kilometers in the soil within the preset range of the target pipeline (such as within 5 meters on both sides of the pipeline), and gas concentration sensors are installed every 50 kilometers to monitor soil moisture and possible natural gas leakage respectively. All sensors form a sensor network through the Zigbee wireless communication protocol. Each sensor is assigned a unique identifier for identification and distinction during data transmission and processing. The pressure sensor and temperature sensor collect data every 10 seconds; the vibration sensor and displacement sensor collect data every 30 seconds; the soil moisture sensor and gas concentration sensor collect data every 60 seconds. The data collected by the sensor is transmitted to a nearby gateway device in real time via the Zigbee network, and the gateway device then sends the data to the data processing and analysis module 102 via the 4G network.

[0030] Specifically, the data processing and analysis module 102 can be used to receive the pipeline operation data and environmental data transmitted by the data acquisition and monitoring module 101, build a data processing platform using cloud computing and distributed storage technology, perform data cleaning, data organization, data storage and data analysis on the pipeline operation data and environmental data, and generate an evaluation report.

[0031] Optionally, the data processing and analysis module 102 can receive pipeline operation data (such as pressure, temperature, flow, etc.) and environmental data (such as soil moisture, gas concentration, etc.) transmitted by the data acquisition and monitoring module 101 in real time through a high-speed network dedicated line, use cloud computing services to build a data processing platform, use a distributed storage system to store massive amounts of data, and use the Pandas library in Python to clean the received data. For example, outliers that obviously deviate from the normal range in the pressure data are removed, and missing data is supplemented by linear interpolation. Assuming that the pressure data sequence collected within a certain period of time is [10, 15, 20, None, 25], the missing value is supplemented with 22.5 after linear interpolation to obtain a new data sequence [10, 15, 20, 22.5, 25], and the cleaned data is sorted according to The data is classified and sorted by dimensions such as timestamp, sensor type, and collection location to facilitate subsequent analysis and storage. The sorted data is stored in a distributed file system, and a regular data backup strategy is set to prevent data loss. The stored data is deeply analyzed using the big data analysis framework. For example, the changing trends of pipeline pressure and temperature in different time periods, as well as the correlation between pressure and flow, are analyzed. By establishing a regression model, it is found that there is a linear relationship between pressure and flow: flow = 0.5*pressure + 5. Based on the results of data analysis, a detailed evaluation report is generated. The evaluation report may include a comprehensive evaluation of the pipeline operation status, analysis of potential risks, and optimization suggestions. For example, the report points out that the pressure of a section of the target pipeline is high during the high temperature period, which poses a certain safety hazard. It is recommended to increase cooling measures or increase the monitoring frequency.

[0032] Among them, data cleaning can use the mean filter algorithm to remove noise, and set the pressure data sequence as: .

[0033] The calculation formula of the filtered pressure value is: .

[0034] in, is the pressure value after filtering, is the sliding window size, is the total number of pressure data, is the sequence number of the pressure data, is the pressure data sequence with sequence number pressure data.

[0035] Assume there is a pressure data sequence [12, 15, 18, 16, 20, 19, 17, 14], set the sliding window size is 3. First, calculate the average of the first three data: (12+15+18) / 3=5; the first pressure value after filtering is 15; then calculate the second filtered value, moving the window: (15+18+16) / 3≈16.3; and so on, the entire filtered pressure data sequence can be obtained.

[0036] It should be noted that the data storage of the data processing and analysis module 102 can adopt a distributed database with a data backup and recovery mechanism.

[0037] Specifically, the abnormal monitoring and alarm module 103 can be used to receive the evaluation report transmitted by the data processing and analysis module 102, and use the abnormal warning model constructed based on machine learning and deep learning technology to perform abnormal detection on the evaluation report, so as to make real-time predictions and warnings on potential problems and failures of the target pipeline. When abnormal data is detected, it is determined that there is an abnormality in the target pipeline. At this time, the abnormal type of the target pipeline can be further determined, the corresponding alarm level can be determined, and the corresponding alarm signal can be generated and output according to the alarm level, and relevant personnel can be notified through various communication methods to prompt relevant staff to handle the target pipeline.

[0038] Optionally, the abnormal monitoring and alarm module 103 can receive an evaluation report generated from the data processing and analysis module 102. When the evaluation report contains statistical information, trend analysis, and potential abnormal point identification of pipeline operation data, the abnormal monitoring and alarm module 103 can use the TensorFlow framework and the abnormal warning model built based on the long short-term memory network and the random forest algorithm to process and analyze the latest received evaluation report at a frequency of once every 5 minutes. For example, when it is found that the pipeline pressure rises sharply in a short period of time and exceeds the normal threshold range obtained based on historical data and model training, it is judged that there may be problems such as pipeline leakage or blockage. After monitoring the abnormal data, the abnormal type is determined and the corresponding alarm level is determined. The alarm level can be divided into three levels, with a yellow alarm for mild abnormality, an orange alarm for moderate abnormality, and a red alarm for severe abnormality. For yellow alarms, the abnormal monitoring and alarm module 103 can notify pipeline maintenance personnel to pay attention through internal system messages; for orange alarms, the abnormal monitoring and alarm module 103 can notify relevant persons in charge through text messages; for red alarms, in addition to text message notifications, the abnormal monitoring and alarm module 103 will also call the preset emergency contact number and simultaneously activate the sound and light alarm device to sound an alarm in the control center, where the communication methods include text messages, phone calls, system message push, and emails. Relevant personnel can set their preferred and backup methods for receiving notifications in the system in advance to ensure that they can receive information in a timely manner in an emergency.

[0039] Optionally, the anomaly warning model may be an early warning model for anomaly detection based on a long short-term memory network, with the input being cleaned and organized multi-dimensional time series data.

[0040] After data cleaning and sorting, multi-dimensional time series data is obtained, such as historical data containing pressure, temperature and flow. These data are input into the early warning model based on the long short-term memory network. The model calculates through function mapping and outputs the predicted value. Assume that the prediction result shows that the pressure may exceed the normal range in the future, so an early warning is issued.

[0041] The integrated pipeline management system proposed in this embodiment includes a data collection and monitoring module 101, a data processing and analysis module 102, and an abnormality monitoring and alarm module 103. By integrating advanced technologies such as the Internet of Things, big data, and artificial intelligence, it can achieve real-time and accurate monitoring and analysis of the pipeline operation status, accurately predict potential problems and failures, greatly improve the accuracy and foresight of pipeline management, reduce human resource consumption, and effectively realize intelligent management of pipelines.

[0042] based on Figure 1 This embodiment proposes a second integrated pipeline management system, which also includes: The emergency response and disposal module is connected to the abnormal monitoring and alarm module, and is used to respond to the alarm signal sent by the abnormal monitoring and alarm module, and perform emergency handling according to the pre-built emergency response system, emergency plan and process.

[0043] Specifically, the emergency response and disposal module can receive the alarm signals sent by the abnormal monitoring and alarm module, build a complete emergency response system, formulate detailed and scientific emergency plans and processes, and automatically activate the emergency response mechanism when a pipeline accident or emergency occurs, and quickly coordinate multiple resources through the collaborative operation platform for orderly disposal.

[0044] It should be noted that in this embodiment, an emergency command center can be established, including a general commander, heads of various departments and technical experts, the responsibilities and powers of each member are clarified, and a 24-hour duty system is established; according to the types of accidents that may occur in the pipeline (such as leakage, explosion, fire, etc.), a detailed emergency plan and process are formulated. For example, for a pipeline leakage accident, the process includes: immediately stopping the operation of the relevant pipeline, evacuating surrounding personnel, demarcating the dangerous area, starting the location and emergency repair of the leakage point, etc. The emergency response and disposal module can receive the alarm signal sent by the abnormal monitoring and alarm module in real time. For example, when the emergency response and disposal module receives a red alarm signal about a sudden and sharp drop in pipeline pressure, it can preliminarily determine that a serious pipeline leak may have occurred; the emergency response mechanism is automatically activated, the emergency command center immediately enters the working state, the commander-in-chief issues instructions, and each department takes prompt action according to its predetermined responsibilities, and quickly coordinates multiple resources such as fire fighting, medical care, and emergency repairs through the collaborative operation platform; the fire department is notified to dispatch fire trucks and firefighters, and rush to the scene with professional equipment to extinguish the fire and dilute the leaked substances; the medical department organizes emergency teams and ambulances to stand by in a safe area, ready to treat the wounded at any time; the emergency repair team carries professional tools and spare parts, and quickly carries out emergency repairs based on the location information of the leak; at the same time, the park management department is coordinated to be responsible for surrounding road traffic control and personnel evacuation to ensure that the rescue channel is unobstructed; during the entire disposal process, the accident handling progress and resource allocation are updated in real time through the collaborative operation platform to ensure that the disposal work is carried out in an orderly and efficient manner.

[0045] Optionally, the above system may further include: The pipeline integrity management module connects the data acquisition and monitoring module, the emergency response and disposal module and the emergency response and disposal module, and is used to perform integrity assessment and management based on the pipeline operation data and environmental data sent by the data acquisition and monitoring module and the assessment report sent by the data processing and analysis module, so as to identify pipeline defects and potential risks, generate maintenance plans and risk control measures corresponding to the pipeline defects and potential risks, and send the identified pipeline defects and potential risks to the emergency response and disposal module so that the emergency response and disposal module can perform emergency processing.

[0046] It should be noted that, in this embodiment, the framework and process of the pipeline integrity management module can be constructed according to internationally recognized pipeline integrity management standards and relevant domestic industry specifications.

[0047] Specifically, the pipeline integrity management module can construct multiple functional modules based on the relevant pipeline integrity management system and standard specifications. For example, a pipeline basic information management module can be constructed to record basic information such as pipeline design parameters, material properties, and installation date. For another example, a detection plan and execution module can also be constructed: receive pipeline operation parameters and environmental data transmitted by the data acquisition and monitoring module, formulate regular internal inspection, external inspection and online monitoring plans, and ensure that the inspection work is carried out on time. For another example, a data analysis and evaluation module can also be constructed to receive pipeline operation data and environmental data collected from the data acquisition and monitoring module, and conduct comprehensive analysis and evaluation of the inspection data and daily operation data.

[0048] Specifically, the pipeline integrity management module evaluates and manages pipeline integrity by utilizing detection methods combined with data analysis and evaluation, timely discovers pipeline defects and potential risks, and formulates scientific and reasonable maintenance plans and risk control measures.

[0049] Among them, regarding detection methods, the pipeline integrity management module can conduct internal pipeline inspections every three years, using intelligent pipe cleaners equipped with multiple sensors to detect corrosion, cracks and other defects on the inner wall of the pipeline. Conduct external pipeline inspections once a year, including appearance inspections and anti-corrosion layer inspections. Install an online monitoring system to monitor pipeline pressure, temperature, vibration and other parameters in real time.

[0050] Among them, regarding integrity assessment, the pipeline integrity management module can integrate the data of internal inspection, external inspection and online monitoring, as well as factors such as the operating years and environmental conditions of the pipeline, and use an exponential comprehensive evaluation model to evaluate the integrity of the pipeline. For example, assuming that there are five evaluation indicators (corrosion degree, crack size, pressure fluctuation, environmental corrosiveness, and maintenance history), the corresponding weights are 0.3, 0.2, 0.2, 0.15, and 0.15, respectively, and the indicator scores are 80, 70, 85, 90, and 75, respectively. The comprehensive evaluation value of pipeline integrity is: E = 0.3 × 80 + 0.2 × 70 + 0.2 × 85 + 0.15 × 90 + 0.15 × 75 = 79.75.

[0051] Among them, regarding defect and risk discovery, the pipeline integrity management module can find, based on assessment results and data analysis, that a certain section of pipeline has serious corrosion defects and is located in a densely populated area, posing a high potential risk.

[0052] Regarding maintenance plans and risk control countermeasures, the pipeline integrity management module can formulate maintenance plans based on the defects and risks found: partially replace the pipeline within one month, and strengthen daily monitoring and inspections in the area. At the same time, formulate risk control countermeasures, such as temporarily adjusting the gas flow during maintenance and strengthening safety publicity for surrounding residents.

[0053] Specifically, the pipeline integrity management module receives pipeline operation data (pressure data, temperature data, flow data, vibration data and displacement data) and environmental data (soil moisture and gas concentration) collected by the data acquisition and monitoring module. These data can serve as an important basis for pipeline integrity assessment and help to promptly detect pipeline defects and potential risks.

[0054] Specifically, the pipeline integrity management module can perform integrity assessment and management based on the data processed by the data processing and analysis module. The data processing and analysis module generates an assessment report after cleaning, sorting, storing and analyzing the collected pipeline operation data and environmental data, providing more accurate and reliable data support for the pipeline integrity management module. For example, the data processing and analysis module may determine data anomalies in certain areas through data analysis. The pipeline integrity management module can focus on the pipeline integrity of the corresponding area based on this information. The pipeline integrity management module feeds back the results of its pipeline integrity assessment to the data processing and analysis module, so that the data processing and analysis module can further comprehensively analyze the operating status of the entire pipeline management system and provide a data basis for generating a more comprehensive assessment report.

[0055] Specifically, when the pipeline integrity management module detects serious defects or potential high-risk situations in the pipeline, it will pass the information to the emergency response and disposal module to trigger the emergency response mechanism. For example, if the pipeline integrity management module assesses that a certain section of the pipeline is at risk of rupture, it will immediately notify the emergency response and disposal module to prepare. After handling the pipeline accident, the emergency response and disposal module will feed back the accident handling results to the pipeline integrity management module. The pipeline integrity management module can adjust the subsequent maintenance plan and risk control countermeasures according to the accident handling situation to improve the integrity management level of the pipeline.

[0056] Optionally, the pipeline integrity assessment can use an exponential comprehensive evaluation model, assuming that there are n evaluation indicators. This embodiment can set a corresponding weight for each evaluation indicator. After determining the indicator score value corresponding to each evaluation indicator, a weighted sum can be performed based on the weight and indicator score value corresponding to each evaluation indicator. Assume that there are 4 evaluation indicators, and the corresponding weights are [0.25, 0.3, 0.2, 0.25], and the indicator scores are [80, 90, 70, 85]. Then the comprehensive evaluation value of pipeline defects is: E = 0.25 × 80 + 0.3 × 90 + 0.2 × 70 + 0.25 × 85 = 82.25. This embodiment can judge the integrity status of the pipeline based on the calculated comprehensive evaluation value, and formulate corresponding maintenance and management strategies accordingly.

[0057] Optionally, the above system may further include: The collaborative operation and information sharing module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module and multiple department equipment, and is used for collaborative operation and information sharing between the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module and each department equipment.

[0058] Specifically, the collaborative work and information sharing module can be used to build a collaborative work platform based on cloud computing, conduct online collaborative work among multiple departments and multiple links, and use task allocation, progress tracking, and information sharing functions to break down information barriers between departments and conduct real-time sharing and exchange of information.

[0059] Optionally, the collaborative operation and information sharing module can receive pipeline operation data and environmental data from the data acquisition and monitoring module so that each department can timely understand the real-time operation status of the pipeline and perform collaborative operations. For example, the maintenance department can arrange inspection and maintenance plans based on these data, and the decision-making department can formulate pipeline management strategies based on the data.

[0060] Optionally, the collaborative operation and information sharing module can obtain the evaluation report generated by the data processing and analysis module, so that each department can understand the overall operation status and potential problems of the pipeline, so as to better collaborate. For example, the engineering department can formulate a pipeline renovation plan based on the data analysis results in the evaluation report. The data and opinions fed back by each department through the collaborative operation and information sharing module can provide more information input for the data processing and analysis module, helping it to optimize the data analysis algorithm and improve the accuracy of the analysis.

[0061] Specifically, this embodiment can use cloud computing services to build a collaborative operation and information sharing module. The collaborative operation and information sharing module can create exclusive accounts for personnel in different departments and links, and set different permissions according to their responsibilities; the departments involved include engineering construction departments, pipeline operation departments, safety supervision departments, repair and maintenance departments, etc. Personnel from various departments communicate and collaborate in real time through the platform. When there is a new pipeline construction or maintenance task, the project leader creates the task on the platform and allocates it according to the professional capabilities and resource conditions of each department. For example, the pipeline laying task is assigned to the engineering construction department, and the equipment maintenance task is assigned to the repair and maintenance department. Each task has a clear time node and progress requirements. After completing the phased work, the heads of each department update the task progress on the platform. Project managers can intuitively view the progress of all tasks through the platform, find out the lagging tasks in time and coordinate; the engineering construction department uploads information such as pipeline design drawings and construction plans to the platform for reference by other departments; the operation department shares the operating parameters and flow data of the pipeline in real time; the safety supervision department issues safety inspection reports and rectification requirements. In this way, the information barriers between departments are broken and real-time sharing and communication of information are realized. For example, during a pipeline maintenance task, the maintenance department discovered new problems on site and promptly fed back the situation to the engineering construction department and the safety supervision department through the platform. The engineering construction department adjusted the maintenance plan based on the feedback, and the safety supervision department evaluated the impact of the new problem on pipeline safety and proposed corresponding safety measures.

[0062] Specifically, during the emergency response process, the collaborative operation and information sharing module can provide the emergency response and disposal module with resource information and collaborative support from various departments. For example, it can notify relevant departments to provide emergency materials, technical support and other resources, coordinate the actions of various departments, and improve the efficiency of emergency response. The emergency response and disposal module will feed back the progress and results of emergency handling to the collaborative operation and information sharing module so that various departments can understand the situation in a timely manner and adjust work arrangements and resource allocation.

[0063] Optionally, the collaborative operation and information sharing module can convey the maintenance plan and risk control measures formulated by the pipeline integrity management module to relevant departments to ensure collaborative implementation by all departments. For example, the maintenance department is notified to carry out pipeline maintenance according to the maintenance plan, and the safety department is notified to strengthen supervision of high-risk areas. The feedback information from each department in the process of implementing the maintenance plan and risk control measures is passed to the pipeline integrity management module through the collaborative operation and information sharing module so that it can adjust and optimize the management strategy according to the actual situation.

[0064] Specifically, the collaborative work and information sharing module can support users in setting permissions and controlling access to shared information. Such permission setting and access control not only ensure the secure sharing of information, but also enable different users to work together effectively within their responsibilities.

[0065] Optionally, the above system further includes: The self-learning and optimization module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module and the pipeline integrity management module. It is used to optimize the abnormal warning model based on the pipeline operation data and environmental data sent by the data acquisition and monitoring module, and the evaluation report sent by the data processing and analysis module to obtain the optimized abnormal warning model and send it to the abnormal monitoring and alarm module. It is also used to optimize the emergency response system, emergency plan and process in the emergency response and disposal module, and optimize the maintenance plan and risk control countermeasures generated by the pipeline integrity management module.

[0066] Specifically, this embodiment can use Python's Scikit-learn library and TensorFlow framework to build a self-learning and optimization module based on random forests and deep neural networks. Collect historical data such as operation data, maintenance records, fault reports, and corresponding management strategies and parameter adjustments of the pipeline system over the past years from the database. At the same time, record the effect and feedback after each actual operation as practical experience data. The self-learning and optimization module learns from these historical data and practical experience. For example, discover the changing rules of pipeline pressure in specific seasons and time periods, and the impact of different maintenance measures on pipeline life; automatically adjust management strategies and parameters based on the new knowledge and rules learned. For example, if the self-learning and optimization module predicts that the pipeline temperature will increase significantly in the coming summer, which may lead to an increased risk of failure, it will automatically adjust the cooling system parameters of the pipeline, or arrange more frequent inspection plans in advance. For another example, if it is found that a new type of maintenance technology is applied well in a similar pipeline system, the model will recommend incorporating it into the current pipeline management strategy and adjust resource allocation and work plans accordingly.

[0067] Among them, the self-learning and optimization module can be used to build self-learning algorithms and models using machine learning technology, and automatically adjust management strategies and parameters by learning new knowledge and rules from historical data and practical experience.

[0068] Specifically, the self-learning and optimization module can receive pipeline operation data and environmental data from the data acquisition and monitoring module as basic data for learning new knowledge and rules. By analyzing and learning these data, the self-learning and optimization module can discover the patterns and trends of pipeline operation and provide a basis for adjusting management strategies and parameters.

[0069] Specifically, the self-learning and optimization module can use the data processed by the data processing and analysis module for learning and optimization. The data analysis results of the data processing and analysis module can provide more valuable information for the self-learning and optimization module, helping it to better understand the pipeline operation status and potential problems.

[0070] Specifically, the self-learning and optimization module can feed back the optimized management strategies and parameters to the data processing and analysis module, enabling it to perform more accurate data analysis and generate evaluation reports based on the new strategies and parameters.

[0071] Optionally, the self-learning and optimization module can learn and optimize according to the abnormal detection and alarm signals of the abnormal monitoring and alarm module. By analyzing the abnormal warning and alarm conditions, the self-learning and optimization module can adjust the abnormal warning algorithm and model to improve the accuracy and timeliness of the abnormal warning. The optimized warning algorithm and model are fed back to the abnormal monitoring and alarm module, so that it can better predict and warn potential problems and failures of the pipeline in real time.

[0072] Specifically, the self-learning and optimization module can provide more scientific and efficient emergency response strategies and resource allocation suggestions for the emergency response and disposal module by learning from historical emergency response events and summarizing experiences and lessons. For example, according to different types of pipeline accidents, it can provide the best emergency resource allocation plan and disposal process optimization suggestions.

[0073] Specifically, the self-learning and optimization module can learn and optimize according to the evaluation results and maintenance plan of the pipeline integrity management module. By analyzing the process and results of pipeline integrity management, the self-learning and optimization module can propose more scientific and reasonable maintenance plans and risk control measures to improve the integrity management level of the pipeline. The optimized maintenance plans and risk control measures are fed back to the pipeline integrity management module, enabling it to better evaluate and manage the integrity of the pipeline.

[0074] Optionally, the above system further includes: The user interface and interaction module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormality monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module, the collaborative operation and information sharing module and the self-learning and optimization module, and is used to interact with users based on user permissions while adapting to the screen size and resolution of the user terminal device.

[0075] Specifically, the user interface and interaction module can be used to build a simple, intuitive and friendly user operation interface using Web technology and mobile application development technology, supporting a variety of terminal devices and operating systems, and at the same time using responsive design technology to enable the system interface to adapt to the screen size and resolution of different terminal devices.

[0076] Specifically, this embodiment can use Web technologies such as HTML5, CSS3 and JavaScript to build a web user interface, and use the React Native framework to develop mobile applications. In both the web and mobile applications, the main interface uses a simple layout to highlight key information, such as the real-time operating status of the pipeline (pressure, temperature, flow), warning information and important notifications. Use clear charts (such as line charts to represent pressure change trends, bar charts to represent flow comparisons in different areas) and intuitive icons to display data. Ensure that the system interface can be used on a variety of terminal devices, including desktop computers, laptops, tablets and smartphones. Whether it is Windows, Mac OS, Android or iOS operating system, users can access and use the system smoothly. Responsive design comes into play when users access the system on screens of different sizes. For example, on a large screen (such as a desktop computer monitor), the interface will display more detailed information and analysis charts in a multi-column layout; on a small screen (such as a mobile phone), it will automatically adjust to a single-column layout, giving priority to the most important information, and hiding the secondary information in the menu, which can be expanded by clicking. Users can view data in different time periods, switch pipeline information in different areas, set personalized warning thresholds, etc. through common interactive operations such as clicking, sliding, and zooming. At the same time, a search function is provided to facilitate users to quickly find the information they need. For example, a maintenance worker uses a mobile phone to open the system application on the way to the maintenance site. The interface automatically adapts to the mobile phone screen and clearly displays the location and fault description of the pipeline to be repaired. The maintenance worker can view the recent operation data of the pipeline by sliding the screen to make full preparations for the maintenance work.

[0077] It should be noted that the user interface and interaction module have permission management functions, and users with different permissions can access and operate different content.

[0078] The integrated pipeline management system proposed in this embodiment can integrate advanced technologies such as the Internet of Things, big data, and artificial intelligence to achieve real-time and accurate monitoring and analysis of pipeline operation status, accurately predict potential problems and failures, and greatly improve the accuracy and foresight of pipeline management. At the same time, the system can self-learn and optimize, constantly adapt to changes in the pipeline operation environment, automatically adjust management strategies and parameters, and improve management efficiency and effectiveness. It can also break the information islands between departments, realize multi-department collaborative operations and information sharing, and significantly improve work efficiency and collaboration capabilities. At the same time, a complete emergency response system and pipeline integrity management mechanism can respond to pipeline accidents and potential risks in a timely and effective manner, ensure the safety and reliability of pipeline operation, and reduce accident losses and impacts on the environment.

[0079] like Figure 3 As shown, this embodiment proposes an integrated pipeline management method, which can be applied to any of the above systems. The method may include the following steps: S301. Collect pipeline operation data and environmental data through multiple sensors installed inside, outside and within a preset range of the target pipeline.

[0080] S302, performing data cleaning, data sorting, data storage and data analysis on the pipeline operation data and environmental data in turn to generate an evaluation report.

[0081] S303: Input the evaluation report into the trained abnormal warning model to perform abnormality detection on the target pipeline. When an abnormality is detected in the target pipeline, determine the abnormality type of the target pipeline.

[0082] S304: Determine a corresponding alarm level according to the abnormality type.

[0083] S305: Output a corresponding alarm signal according to the alarm level to prompt relevant staff to handle the pipeline.

[0084] The integrated pipeline management method proposed in this embodiment can integrate advanced technologies such as the Internet of Things, big data, and artificial intelligence to achieve real-time and accurate monitoring and analysis of pipeline operation status, accurately predict potential problems and failures, greatly improve the accuracy and foresight of pipeline management, reduce human resource consumption, and effectively realize intelligent management of pipelines.

[0085] based on Figure 3 ,like Figure 4 As shown, this embodiment proposes another integrated pipeline management method, which may include: Step 1: Use the sensors deployed by the data acquisition and monitoring module through the Internet of Things technology to build a sensor network to collect pipeline operation parameters and environmental data in real time.

[0086] Step 2: The data processing and analysis module uses cloud computing and distributed storage technologies to build a data processing platform, and uses data mining and data visualization analysis tools to clean, organize, store and analyze the collected pipeline operating parameters and environmental data to generate an evaluation report.

[0087] Step 3: The intelligent early warning and alarm module uses machine learning technology to build early warning algorithms and models. The early warning algorithms and models set warning thresholds and alarm rules based on the evaluation report, predict and warn of potential pipeline problems, and automatically issue early warning or alarm signals when abnormal data is monitored, and notify relevant personnel through various communication methods.

[0088] It should be noted that the intelligent early warning and alarm module is the above-mentioned abnormal monitoring and alarm module.

[0089] Step 4: The emergency response and disposal module receives the warning or alarm signal from the intelligent early warning and alarm module, automatically activates the pre-established emergency response system, and quickly coordinates multiple resources for disposal through the collaborative operation platform in accordance with the formulated emergency plan and process. At the same time, it uses geographic information system and global positioning system technology to locate the accident site and allocate resources.

[0090] Step 5: The pipeline integrity management module uses non-destructive testing technology and in-pipeline testing technology to detect and evaluate the pipeline based on the relevant pipeline integrity management system and standard specifications. Combined with the data analysis results, it builds pipeline business activity management, high consequence area management, and risk management functional modules to evaluate and manage pipeline integrity and formulate scientific and reasonable maintenance plans and risk control measures.

[0091] Step 6: Collaborative operation and information sharing module builds a collaborative operation platform based on cloud computing, which links together multiple departments and links involved in pipeline management, and shares and exchanges information in real time.

[0092] Step 7: The system self-learning and optimization module uses machine learning technology to build self-learning algorithms and models, and automatically adjusts management strategies and parameters by learning new knowledge and rules from historical data and practical experience.

[0093] Step 8: The user interface and interaction module uses Web technology and mobile application development technology to build an operation interface to provide users with a simple, intuitive and friendly operation experience.

[0094] For better explanation Figure 4 In this embodiment, a large oil pipeline transportation network covering thousands of kilometers of pipelines and multiple pumping stations and transfer stations is used as the target pipeline. Figure 4 The contents of each step in are introduced.

[0095] Step 1: Install pressure sensors, temperature sensors, flow sensors, vibration sensors, and environmental sensors (such as soil moisture and gas concentration sensors) at key locations of the pipeline, including pump station inlets and outlets, pipeline bends, and sections crossing complex terrain, and build a sensor network. These sensors transmit the collected data to the data processing center in real time through IoT technologies such as Zigbee or LoRa.

[0096] Step 2: Data processing and analysis module: Build a data processing platform in the cloud, use Hadoop distributed storage system and Spark data analysis engine, clean the data to remove noise and outliers, organize and classify the data for storage, use data mining algorithms to discover potential patterns in the data, and use data visualization tools (such as Tableau) to generate intuitive reports and charts, including daily change curves of pipeline pressure and temperature, monthly statistics of flow, etc., to form an evaluation report.

[0097] Step 3: The intelligent early warning and alarm module builds an early warning model based on the TensorFlow framework and sets early warning thresholds based on historical data and expert experience. For example, when the pipeline pressure rises by more than 20% or the temperature drops by more than 10% in a short period of time, a yellow early warning is triggered; when it exceeds 30%, a red alarm is triggered. Once abnormal data is detected, the operation and maintenance personnel and management personnel are immediately notified via SMS, email and system pop-up windows.

[0098] It should be noted that the intelligent early warning and alarm module is the above-mentioned abnormal monitoring and alarm module.

[0099] Step 4: After receiving the alarm signal, the emergency response and disposal module automatically activates the pre-established emergency response system, uses the geographic information system (GIS) and the global positioning system (GPS) to accurately locate the accident site, and dispatches the nearest maintenance team and materials through the collaborative operation platform. Assuming that a pipeline leak occurs in a mountainous section, the system quickly plans the best rescue route and notifies the nearest maintenance team to rush to the scene with professional equipment.

[0100] Step 5: Pipeline integrity management module regularly uses non-destructive ultrasonic testing technology and intelligent pigs to conduct internal inspections. According to the test results and data analysis, the corrosion degree of the pipeline, weld defects, etc. are evaluated. Combined with the risk assessment model, high-consequence areas are divided, and maintenance and inspection plans are formulated for different risk levels. For example, for severely corroded pipe sections, priority maintenance is arranged and the inspection frequency is increased.

[0101] Step 6: Collaborative work and information sharing module builds a collaborative work platform based on Alibaba Cloud, connecting the pipeline operation department, maintenance department, safety supervision department, etc. Each department shares information in real time through the platform, such as maintenance progress, safety inspection reports, etc., to achieve efficient collaborative work.

[0102] Step 7. The system self-learning and optimization module continuously collects historical data and processing results, uses the random forest algorithm to self-learn and optimize the parameters of the early warning model, and automatically adjusts the management strategy according to changes in the pipeline operating environment. For example, it adjusts the warning thresholds of pressure and temperature as the seasons change.

[0103] Step 8: User Interface and Interaction Module uses HTML5 and CSS3 to build a responsive web interface and mobile application. The interface clearly displays the real-time status, warning information and historical data of the pipeline, supports users to interact through clicks, slides and other operations, and facilitates users to obtain and manage pipeline information anytime and anywhere.

[0104] The integrated pipeline management system in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0105] The embodiment of the present invention also provides a computer device having the above Figure 1 The integrated pipeline management system shown.

[0106] See also Figure 5 , a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0107] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0108] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0109] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0110] The memory 20 may include a volatile memory, such as a random access memory. The memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive. The memory 20 may also include a combination of the above types of memory.

[0111] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0112] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An integrated pipeline management system, characterized in that: include: A data acquisition and monitoring module, including a plurality of sensors installed inside, outside and within a preset range of a target pipeline, for collecting pipeline operation data and environmental data through the plurality of sensors; A data processing and analysis module, connected to the data acquisition and monitoring module, for receiving the pipeline operation data and the environmental data sent by the data acquisition and monitoring module, and performing data cleaning, data sorting, data storage and data analysis on the pipeline operation data and the environmental data in sequence to generate an evaluation report; The abnormal monitoring and alarm module is connected to the data processing and analysis module, and is used to receive the evaluation report sent by the data processing and analysis module, input the evaluation report into the trained abnormal warning model to perform abnormal detection on the target pipeline, and when an abnormality is detected in the target pipeline, determine the abnormality type of the target pipeline, determine the corresponding alarm level according to the abnormality type, and output the corresponding alarm signal according to the alarm level to prompt relevant staff to process the target pipeline.

2. The system according to claim 1, characterized in that The pipeline operation data includes pressure data, temperature data, flow data, vibration data and / or displacement data, and the environmental data includes soil moisture and / or gas concentration.

3. The system according to claim 1, characterized in that The system further comprises: The emergency response and disposal module is connected to the abnormal monitoring and alarm module, and is used to respond to the alarm signal sent by the abnormal monitoring and alarm module and perform emergency processing according to the pre-built emergency response system, emergency plan and process.

4. The system according to claim 3, characterized in that The system further comprises: The pipeline integrity management module is connected to the data acquisition and monitoring module, the emergency response and disposal module and the emergency response and disposal module, and is used to perform integrity assessment and management based on the pipeline operation data and the environmental data sent by the data acquisition and monitoring module and the assessment report sent by the data processing and analysis module, so as to identify pipeline defects and potential risks, generate maintenance plans and risk control measures corresponding to the pipeline defects and potential risks, and send the identified pipeline defects and potential risks to the emergency response and disposal module so that the emergency response and disposal module can perform emergency processing.

5. The system according to claim 4, characterized in that The system further comprises: The collaborative operation and information sharing module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module and multiple department devices, and is used for collaborative operation and information sharing between the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module and each of the department devices.

6. The system according to claim 4, characterized in that The system further comprises: The self-learning and optimization module is connected to the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module and the pipeline integrity management module, and is used to optimize the abnormal warning model based on the pipeline operation data and the environmental data sent by the data acquisition and monitoring module, and the evaluation report sent by the data processing and analysis module to obtain the optimized abnormal warning model and send it to the abnormal monitoring and alarm module. It is also used to optimize the emergency response system, emergency plan and process in the emergency response and disposal module, and optimize the maintenance plan and risk control countermeasures generated by the pipeline integrity management module.

7. The system according to any one of claims 1 to 6, characterized in that The system further comprises: The user interface and interaction module connects the data acquisition and monitoring module, the data processing and analysis module, the abnormal monitoring and alarm module, the emergency response and disposal module, the pipeline integrity management module, the collaborative operation and information sharing module and the self-learning and optimization module, and is used to interact with the user based on the user authority while adapting to the screen size and resolution of the user terminal device.

8. An integrated pipeline management method, characterized in that: Applied to the system of any one of claims 1 to 7, the method comprising: Collect pipeline operation data and environmental data through multiple sensors installed inside, outside and within a preset range of the target pipeline; Performing data cleaning, data sorting, data storage and data analysis on the pipeline operation data and the environmental data in sequence to generate an evaluation report; Inputting the evaluation report into a trained abnormality warning model to perform abnormality detection on the target pipeline, and when an abnormality is detected in the target pipeline, determining the abnormality type of the target pipeline; The corresponding alarm level is determined according to the abnormality type, and the corresponding alarm signal is output according to the alarm level to prompt relevant staff to handle the pipeline.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the integrated pipeline management method described in claim 8 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the integrated pipeline management method described in claim 8.

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