Long-distance pipe network safety monitoring method, system and equipment based on Internet and medium

Abstract

The invention discloses a long-distance pipe network safety monitoring method, system and device based on the Internet and a medium, and relates to the technical field of data safety monitoring of long-distance pipelines, and the method comprises the steps: collecting the parameter information of a to-be-monitored long-distance pipe network during operation in real time, and inputting the parameter information into a pre-constructed operation state simulation model for the long-distance pipe network, a simulation result is obtained; determining abnormal data based on the simulation result in combination with a preset abnormal condition; and adjusting the acquisition frequency of the parameter information based on the abnormal data, and generating an abnormal signal at the same time. The parameter information collected in real time is processed in combination with the pre-constructed running state simulation model for the long-distance transmission pipe network, the purpose of visually displaying the current running state of the long-distance transmission pipe network to be monitored can be achieved, accurate monitoring of the running state of the long-distance transmission pipe network can be achieved, and rapid operation and maintenance can be conducted on abnormal conditions.

Description

Background Art

[0002] The long-distance oil and gas pipeline network is an important energy infrastructure. As the "artery" that connects oil and gas production and consumption, it not only has the characteristics of large diameter, high pressure, and long transportation distance, but also involves multiple complex scenarios such as crossing and traversing, and there are many potential risk factors. In recent years, with the accelerated advancement of the interconnection of oil and gas pipelines in my country, the operating conditions of the oil and gas pipeline network have become more complex. Due to its upstream and downstream linkage coupling and multi-input and multi-output operation mode, its operating risks have also increased accordingly, which has put forward higher requirements for the safety and stability of the operation of the long-distance oil and gas pipeline network. In this context, how to achieve effective monitoring of the operating status of the long-distance pipeline network is of great significance to timely discover and deal with major safety risks of the long-distance oil and gas pipelines and ensure the safe supply of national energy.

[0003] At present, the long-distance pipeline network safety monitoring system has at least the following problems: First, the long-distance pipeline network has a long transportation distance. When an abnormality occurs in its operating state, such as pressure loss, deformation, corrosion, and pipeline rupture, the operation and maintenance personnel often need to spend a lot of time to reach the abnormal location. However, the existing monitoring system still has many false alarms and missed alarms due to various reasons, such as sensor drift, disconnection, and relatively simple and single abnormal judgment methods. Secondly, there are many relay devices in the long-distance pipeline network system, and different devices correspond to different functions. Faced with a large number of functional modules, when an abnormality occurs, it is difficult for the operation and maintenance personnel to quickly locate the fault. In some cases, they need to query the information or seek expert help to analyze and deal with the fault. In addition, in order to achieve comprehensive and accurate monitoring of the pipeline network, the long-distance pipeline network monitoring system often needs to collect a variety of information such as pressure, temperature, pipeline thickness, deformation, and positioning in the pipe, and further process the collected data. This massive amount of data will bring huge challenges to the timeliness of collection, transmission, and processing. How to coordinate the accuracy and timeliness of long-distance pipeline network monitoring is an urgent problem to be solved. Summary of the invention

[0004] 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 safety monitoring method, system, equipment and medium based on the Internet, as follows:

[0005] 1) In the first aspect, the present invention provides a long-distance pipeline network safety monitoring method based on the Internet, and the specific technical scheme is as follows:

[0006] Collect parameter information of the long-distance pipeline network to be monitored in real time during operation, and input the parameter information into a pre-built simulation model of the operation status of the long-distance pipeline network to obtain simulation results;

[0007] Based on the simulation results and in combination with preset abnormal conditions, determining abnormal data;

[0008] Based on the abnormal data, the acquisition frequency of the parameter information is adjusted, and an abnormal signal is generated at the same time.

[0009] The beneficial effects of the Internet-based long-distance pipeline network safety monitoring method provided by the present invention are as follows:

[0010] By collecting parameter information of the monitored long-distance pipeline network in real time during operation, the purpose of timely processing and timely discovery of abnormalities is achieved. In addition, processing the parameter information collected in real time in combination with a pre-built simulation model for the operation status of the long-distance pipeline network can achieve the purpose of intuitively displaying the current operation status of the long-distance pipeline network to be monitored. The present invention can realize accurate monitoring of the operation status of the long-distance pipeline network and rapid operation and maintenance of abnormal situations.

[0011] Based on the above solution, the present invention can also be improved as follows.

[0012] Furthermore, the pre-built operation status simulation model for the long-distance pipeline network is obtained by training the composition information of the long-distance pipeline network, and the composition information includes: connection relationship of the transmission trunk line, pressure station, pipeline diameter, metering equipment and purification equipment.

[0013] Furthermore, the parameter information includes: pipe section location information and basic pipeline parameters, and the basic pipeline parameters include: at least one of the temperature of the fluid in the pipe, the pressure of the fluid in the pipe, the flow rate of the fluid in the pipe, the pipe diameter and the pipe wall thickness.

[0014] Further, the process of inputting the parameter information into the pre-built operation status simulation model for the long-distance pipeline network is as follows:

[0015] According to the pipe section position information in the parameter information, an area corresponding to the pipe section position information is determined in the pre-built operation status simulation model for the long-distance pipeline network, and the pipeline basic parameters in the parameter information are input into the area, and the target pipeline basic parameters corresponding to the area are collected, and the target pipeline basic parameters are output as the simulation results.

[0016] Further, the process of determining abnormal data is:

[0017] Preprocessing the parameter information collected in real time during the operation of the long-distance pipeline network to be monitored, and judging whether there is a suspected abnormality in the long-distance pipeline network to be monitored according to the preprocessing result;

[0018] When the judgment result is yes, the parameter information of the monitored long-distance pipeline network during operation is continuously collected within a preset unit time period to generate a parameter information set, and the average value of the parameter information within the preset unit time period is determined according to the parameter information set, wherein the preset unit time period includes a preceding time period and a succeeding time period, and the preceding time period is the time period before the moment corresponding to the parameter information with suspected abnormality;

[0019] According to the average value of the parameter information and the basic parameters of the target pipeline, abnormal data of the long-distance pipeline network to be monitored is determined.

[0020] Further, the process of adjusting the acquisition frequency of the parameter information is as follows:

[0021] According to the average value of the parameter information and the target difference of the target pipeline basic parameter, the target sampling rate level corresponding to the target difference is determined in the corresponding relationship between the difference and the sampling rate level, and the collection frequency of the parameter information is adjusted according to the target sampling rate level.

[0022] Furthermore, it also includes:

[0023] Generate an inspection instruction according to the pipe section position in the abnormal signal;

[0024] According to the inspection instruction, the optimal inspection equipment is deployed to perform inspection according to the inspection path, and the inspection path is generated according to the pipe section position in the abnormal signal.

[0025] 2) In the second aspect, the present invention also provides an Internet-based long-distance pipeline network safety monitoring system, and the specific technical solution is as follows:

[0026] The simulation unit is used to: collect parameter information of the long-distance pipeline network to be monitored in real time during operation, and input the parameter information into a pre-built simulation model for the operation status of the long-distance pipeline network to obtain simulation results;

[0027] The determination unit is used to: determine abnormal data based on the simulation result and in combination with preset abnormal conditions;

[0028] The monitoring unit is used to: adjust the acquisition frequency of the parameter information based on the abnormal data, and generate an abnormal signal at the same time.

[0029] 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.

[0030] 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.

[0031] 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

[0032] 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:

[0033] Figure 1 A schematic diagram of a flow chart of a long-distance pipeline network safety monitoring method based on the Internet according to an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a subsystem model architecture in an embodiment of an Internet-based long-distance pipeline network safety monitoring method according to an embodiment of the present invention;

[0035] Figure 3 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] like Figure 1 As shown, a long-distance pipeline network safety monitoring method based on the Internet according to an embodiment of the present invention comprises the following steps:

[0038] S1, real-time collection of parameter information of the long-distance pipeline network to be monitored during operation, and inputting the parameter information into a pre-built operation status simulation model for the long-distance pipeline network to obtain simulation results;

[0039] S2, based on the simulation results and combined with the preset abnormal conditions, determine the abnormal data;

[0040] S3, based on the abnormal data, adjust the acquisition frequency of the parameter information and generate an abnormal signal at the same time.

[0041] The beneficial effects of the Internet-based long-distance pipeline network safety monitoring method provided by the present invention are as follows:

[0042] By collecting parameter information of the monitored long-distance pipeline network in real time during operation, the purpose of timely processing and timely discovery of abnormalities is achieved. In addition, processing the parameter information collected in real time in combination with a pre-built simulation model for the operation status of the long-distance pipeline network can achieve the purpose of intuitively displaying the current operation status of the long-distance pipeline network to be monitored. The present invention can realize accurate monitoring of the operation status of the long-distance pipeline network and rapid operation and maintenance of abnormal situations.

[0043] S1, real-time collection of parameter information of the long-distance pipeline network to be monitored during operation, and input of the parameter information into a pre-built simulation model of the operation status of the long-distance pipeline network to obtain simulation results. Specifically, the steps are as follows:

[0044] S11, establishing a first simulation model based on the composition information of the entire long-distance pipeline network;

[0045] The composition information includes: the types and parameters of the functional modules constituting the long-distance pipeline network, the topological relationship between the functional modules, the transmission law of the fluid, and the fluid dynamics; specifically, it may be a gathering and transportation pipeline network, a transmission trunk line, purification equipment, a pressure station, a pipeline diameter, a metering device, etc.; when the fluid passes through different functional modules, the parameters of the fluid will change differently; initially, the composition information of the long-distance pipeline network is obtained, and the first simulation model is obtained based on the big data model training.

[0046] Embodiment 1, by obtaining all functional modules of the entire long-distance pipeline network (including but not limited to: data processing module, data transceiver module), parameters (length of pipe sections at different positions, pipe wall thickness, valve position, etc.), topological relationship between functional modules (acquisition module is connected to data processing module, and the result of data processing module processing is transmitted through data transceiver module), fluid transmission law (rate of transmission of different fluids in a certain section of pipe, etc.). Through the above content, a model of the entire long-distance pipeline network is constructed, and a panel or interface for parameter adjustment or input is set at each node (including but not limited to the start, middle and tail position of the pipe section, valve position, station position, etc.). In this way, the model (first simulation model) can be more realistically made to fit the actual situation of the actual long-distance pipeline network.

[0047] S12, using sensors to collect parameter information of the long-distance pipeline network to be monitored during operation in real time;

[0048] The parameter information of the long-distance pipeline network to be monitored during operation includes: temperature, pressure, flow rate, etc. of the fluid in the pipe; it can also be the diameter of the pipe network, the thickness of the pipe wall, etc.; different parameter information is collected and acquired based on different types of sensors; each sensor corresponds to a different location node; the sensors include: fixed position sensors and movable sensors; according to the on-site working conditions, fixed position sensors are generally fixed at suitable positions, for example, they can be laid at intervals on the outer wall of the pipe, or they can be set inside the pipe network; movable sensors can be fixed or integrated on the active inspection module, or they can be handheld movable sensors; the active inspection module can be a robot and / or a drone.

[0049] Generally speaking, compared with movable sensors, the inspection and patrol frequency of fixed position sensors is lower, and the accuracy of fixed position sensors will gradually decrease with the influence of factors such as continuous working time and drift (sensor drift refers to the phenomenon that the sensor output changes over time when the input remains unchanged. There are two reasons for the drift: one is the sensor's own structural parameters; the other is the surrounding environment (such as temperature, humidity, etc.). The most common drift is temperature drift, that is, the change in output caused by the change in ambient temperature. The temperature drift is mainly manifested as temperature zero drift and temperature sensitivity drift.) In order to achieve accurate monitoring of the operating status of the long-distance pipeline network, it is convenient for staff to quickly discover the fault of the fixed position sensor; in another embodiment of the present scheme, the present invention also includes: based on the movable sensor The sensor and the fixed position sensor simultaneously obtain the same type of parameter information at a certain position node, and determine whether the fixed position sensor has an abnormality by making a difference; when the difference between the two is lower than the first threshold, it is determined that there is no abnormality in the fixed position sensor; when the difference between the two is greater than the second threshold, it is determined that there is an abnormality in the fixed position sensor, and an alarm message is issued to prompt the staff to check whether the sensor can be continued to be used by returning it for repair. If so, the sensor is associated with the current position node of the sensor, and the sensor is returned to the factory for repair. After the repair, the sensor is placed in the spare sensor box corresponding to the position node, waiting to be activated; when the difference between the two is between the first threshold and the second threshold, the parameter information collected by the fixed position sensor is compensated based on the difference between the two to improve the accuracy of data collection.

[0050] S13, synchronously transmitting parameter information collected by the specific sensor to the first simulation model to simulate the operation status of the long-distance pipeline network;

[0051] Specific sensors are sensors used to collect parameter data at specific nodes. Specific nodes refer to pipeline intersections, stations, etc. Specific sensors refer to flow sensors at certain intersections, and pressure sensors corresponding to the input and output ports of the transmission station. By inputting the basic information at the above key nodes into the first simulation model, the basic parameters of the first simulation model are improved, so that the first simulation model can realize real-time inspection of the working status of the current long-distance pipeline network at any non-above key node.

[0052] For example, the first simulation model constructed by Example 1 is input at the station node in the first simulation model, and the flow rate, transported goods, pressure and other data of the current actual station are input at the receiving station. After the above operation, the simulation of the transported goods in the first simulation model is completed. At this point, the parameters at any node in the first simulation model (the flow rate value of a certain intermediate transfer station) can be checked.

[0053] The first simulation model obtains parameter information collected by a specific sensor, and realizes an overall simulation of the working state of the long-distance pipeline network based on the parameter information at the corresponding specific node or position of the long-distance pipeline network. The parameter information at any node position of the long-distance pipeline network can be obtained through the first simulation model; for example, the temperature, pressure and flow rate of the fluid at the node 1 are t1, p1, and v1 respectively. After being transported for a distance x and the boosting effect of the boosting station, the temperature, pressure and flow rate of the fluid will become t2, p2, and v2 respectively; at this time, only a small amount of parameter information based on the first simulation model and specific nodes or positions is needed to realize the simulation and acquisition of parameter information at any node position of the long-distance pipeline network; the simulated and acquired parameter information at any node position can be used to assist in the judgment of the operating state of the long-distance pipeline network. Based on the simulated and real parameter information, the accuracy of judging the operating state of the long-distance pipeline network can be improved;

[0054] In addition, considering the continuous working time of the long-distance pipeline network and related functional modules, the location of the pipeline network and functional modules, the atmospheric environment corresponding to the location, the soil depth of the pipeline network, soil pH, soil temperature, soil humidity and other factors, all of which will have a corresponding impact on the working state and transmission law of the long-distance pipeline network; in order to improve the simulation accuracy of the first simulation model, the first simulation model can be iteratively trained and updated based on the parameter information collected by the specific sensor and the preset model;

[0055] The preset model refers to the natural loss and offset model, which can iteratively train and update the first simulation model based on parameter information collected by specific sensors, such as the continuous working time of the long-distance pipeline network and related functional modules, the location of the pipeline network and functional modules, the atmospheric environment corresponding to the location, the soil depth of the pipeline network, soil pH, soil temperature, humidity, etc., so that it can more realistically simulate the operation rules of the long-distance pipeline network.

[0056] S2, based on the simulation results and combined with the preset abnormal conditions, determine the abnormal data; including:

[0057] S21, preprocessing, feature extraction and classification of parameter information collected in real time by sensors on the long-distance pipeline network to be monitored in the real scene, and determining whether there is a potential abnormality at the corresponding position of the long-distance pipeline network;

[0058] Potential abnormality means that the classification of parameter information does not correspond to the type of sensor. For example, if the parameter information is 10A, but the sensor that collects the parameter information is a pressure sensor, it is determined to be a potential abnormality or a suspected abnormality.

[0059] It should be noted that the sensor here can be a specific sensor used to synchronously transmit data to the first simulation model, or it can be other sensors, which are not limited here; generally speaking, the number and types of specific sensors in the monitoring system are less than other sensors.

[0060] S22. When there is a potential abnormality, the average value of the parameter information collected by the sensor in real time within a unit time period is calculated, and the parameter information at the corresponding position is determined based on the first simulation model; wherein:

[0061] The average value refers to the average value of n data collected within a unit time period based on the collection frequency.

[0062] The size of the unit time period can be adjusted according to actual needs; however, it is clear that the unit time period includes both the time period before the potential anomaly occurs and the time period after the potential anomaly occurs, and the length of the previous time period is greater than or equal to the length of the latter time period;

[0063] The parameter information in the time period before the potential abnormality occurs can be used to preliminarily determine the occurrence of the abnormality, and the parameter information in the time period after the potential abnormality occurs can be used to further confirm the occurrence of the abnormality; in order to improve the accuracy of abnormality judgment, the time period after the potential abnormality occurs can also be greater than the time period before the potential abnormality occurs;

[0064] The parameter information in the time period before and after the potential exception occurs is stored in the corresponding cache and storage module respectively, and is deleted regularly based on the first-in-first-out mechanism to save storage space.

[0065] S23, comparing the average value of parameter information collected by the sensor in real time within a unit time period with the parameter information at the corresponding position determined based on the first simulation model to obtain abnormal conditions of the long-distance pipeline network; including:

[0066] Calculate the difference between the average value of the parameter information collected by the sensor in real time within a unit time period and the parameter information at the corresponding position determined based on the first simulation model, and determine the abnormality degree of the long-distance pipeline network at the corresponding position based on the difference between the two;

[0067] Considering that the transmission of fluid in the long-distance pipeline network is directional, when there is a potential abnormality at a certain position node, such as deformation, corrosion or rupture at a certain position of the pipeline network, it will affect the parameter information of the front and rear functional modules at the position, that is, the corresponding parameter information at the front and rear position nodes corresponds to different situations; under normal circumstances, that is, when only the position node has an abnormality, the difference between the parameter information of the front position node and the parameter information determined based on the first simulation model is small, and the parameter information of the rear position node should have a large difference with the parameter information determined based on the first simulation model; but there is also the possibility that the front and rear position nodes and the current position node have abnormalities at the same time and the abnormality is falsely reported, and different operating states will correspond to different parameter information situations;

[0068] In order to further improve the accuracy of long-distance pipeline network operation status monitoring and avoid the occurrence of abnormal operation status false alarms, the following options are optional:

[0069] S24, based on the topological relationship of the long-distance pipeline network (i.e., the connection relationship of the nodes), obtaining the average value of the parameter information collected by the corresponding sensors at at least one position node in front of and behind the potential abnormal position node of the long-distance pipeline network; determining the parameter information corresponding to at least one position node in front of and behind the potential abnormal position node based on the first simulation model;

[0070] The position nodes before and after the potential abnormal position node correspond to the upstream and downstream functional modules of the abnormal functional module;

[0071] The parameter information collected by the corresponding sensor at at least one location node before and after the potential abnormal location node is also stored in the corresponding cache and storage module, and is deleted regularly based on the first-in-first-out mechanism to save storage space;

[0072] S25, subtracting an average value of parameter information collected by a corresponding sensor at at least one position node before the potential abnormal position node and parameter information corresponding to at least one position node before the potential abnormal position node determined to exist based on the first simulation model, to obtain a first difference value;

[0073] S26, subtracting an average value of parameter information collected by a corresponding sensor at at least one position node behind the potential abnormal position node from the parameter information corresponding to at least one position node behind the potential abnormal position node determined based on the first simulation model to obtain a second difference value;

[0074] S27. When the first difference is less than the preset threshold and the second difference is greater than the preset threshold, it is confirmed that there is an abnormality in the potential abnormal position node, and it is prompted that the possibility of abnormality in at least one subsequent position node cannot be ruled out; when the first difference is greater than the preset threshold and the second difference is greater than the preset threshold, it is confirmed that there is an abnormality in the potential abnormal position node, and it is prompted that the possibility of abnormality in at least one of the previous and subsequent position nodes cannot be ruled out; when the first difference is less than the preset threshold, it is confirmed that there is a false alarm.

[0075] S3, based on the abnormal data, adjusts the acquisition frequency of parameter information and generates an abnormal signal at the same time, including:

[0076] S31: Under normal circumstances, the sensor collects data at the first level sampling rate;

[0077] The first level sampling rate is relatively low and can be set according to the actual situation and specific needs on site;

[0078] S32: When there is a potential abnormality, adjusting the sensor sampling rate to the second level;

[0079] S33: Calculate the difference between the average value of the parameter information collected by the sensor in real time within a unit time period and the parameter information at the corresponding position determined based on the first simulation model, and adjust the sensor sampling rate to a value corresponding to the third level based on the difference between the two;

[0080] It should be noted that the first and second level sampling rates can be expressed as M1 and M2, and the initial sampling rate of the third level can be expressed as M3; M1 <M2<M3;

[0081] Based on the difference between the two, the sensor sampling rate is adjusted to a value corresponding to the third level, which can be expressed as: M = M3 + D*K1;

[0082] Wherein, M represents the real-time sampling rate of the sensor, D represents the difference between the average value of the parameter information collected by the sensor in real time within a unit time period and the parameter information at the corresponding position determined based on the first simulation model, and K1 is a preset coefficient;

[0083] The method further includes S34: when it is prompted that the possibility of an abnormality in at least one of the following position nodes cannot be ruled out, adjusting the sampling rate of the sensor corresponding to the following position node from the first level to the second level;

[0084] S35: When it is prompted that the possibility that at least one of the front and rear position nodes has an abnormality cannot be ruled out, the sampling rate of the sensor corresponding to at least one of the front and rear position nodes is adjusted from the first level to the second level;

[0085] S36: When it is confirmed that there is a false alarm, the sensor sampling rate is adjusted from the current value to the first level;

[0086] Optionally, as shown, in order to facilitate the staff to view and obtain the operation status of the long-distance pipeline network in real time and intuitively, the method further includes: establishing a 3D simulation model of the long-distance pipeline network, and displaying the real operation status of the long-distance pipeline network in real time on a display device, and highlighting the corresponding position node when an abnormal situation occurs;

[0087] Optionally, the parameter information collected by the sensor in real time, the parameter information at the corresponding position determined by the first simulation model, and the difference between the two can also be displayed in real time on the display device to assist the staff in conducting autonomous diagnosis of abnormal situations.

[0088] In another embodiment of this solution, the present invention further comprises:

[0089] S4, mobilize the active inspection module to quickly move to the abnormality location and confirm the abnormality; including:

[0090] S41, determining the location node information where the abnormal situation occurs, and generating corresponding inspection instructions;

[0091] S42, combining the distribution and status information of active inspection modules around the location node, sending the inspection instruction to the appropriate active inspection module;

[0092] A suitable active inspection module refers to an idle, relatively close robot and / or drone equipped with a corresponding type of movable sensor. The specific determination method will not be described in detail here.

[0093] As shown, in S43, the active inspection module automatically performs path planning in combination with the inspection instruction, and moves to the location node where the abnormal situation occurs to confirm the abnormal situation on site;

[0094] S44, during the movement of the active inspection module, the fixed position sensor located on the moving path is detected in real time based on the built-in movable sensor to see if there is any abnormality, and different strategies are executed based on the detection results;

[0095] S45, when the active inspection module cannot confirm the abnormal situation, perform manual inspection;

[0096] For example, when the abnormal situation is a fluid leakage in the pipe network, when the active inspection module moves to the leakage location, the camera module can confirm the occurrence of the abnormal situation; when the abnormal situation is serious corrosion of the pipe network, the active inspection module will not be able to confirm the abnormal situation, and manual inspection is required;

[0097] Optionally, manual inspection can also directly trigger the generation when it is determined that there is an abnormal situation in the long-distance pipeline network to reconfirm the abnormal situation;

[0098] Long-distance pipeline networks have long transportation distances and are mostly distributed in the wilderness. It is too costly to deploy a large number of sensors to locate abnormalities. When an abnormality is discovered, the staff can often only reach the approximate location, and the pipeline network is sometimes buried underground, which is not conducive to the operation and maintenance personnel to quickly determine the abnormal location and perform efficient operation and maintenance;

[0099] It also includes: S5, generating and distributing manual inspection tasks, and the staff performs on-site operation and maintenance based on the contents of the manual inspection tasks, including:

[0100] S51, determining a first range according to the location node information where the abnormal situation occurs, and generating and issuing a corresponding manual inspection task;

[0101] The first range refers to a circular range with the location node information where the abnormal situation occurs as the center and a preset value as the radius;

[0102] S52, the inspection personnel may receive manual inspection tasks through the manual auxiliary inspection module they wear;

[0103] The manual assisted inspection module can be MR glasses or other handheld terminals;

[0104] S53, based on the manual auxiliary inspection module, real-time playback of navigation information of the location node where the abnormal situation occurs;

[0105] Navigation information can be either voice information or real-time route display information;

[0106] S54. After the inspection personnel arrives within the first range, based on the 3D simulation model of the long-distance pipeline network, the location, direction, abnormal location nodes, and specific parameter information of the pipeline network are highlighted on the display interface of the manual auxiliary inspection module, and the inspection personnel's own location is displayed at the same time. The staff is assisted to quickly move to the laying or burying path of the pipeline network corresponding to the abnormal location node or near the abnormal equipment through voice prompts and direction signs;

[0107] S55, the manual assisted inspection module uses the camera module to capture in real time the "pipeline laying mark below" set at intervals above the pipeline laying path. After discovering the laying mark, the manual assisted inspection module will realize the positioning, navigation and calibration of the pipeline laying path based on the laying mark, pre-stored map data and the inspection personnel's own position, so as to avoid the positioning error caused by the later offset of the pipeline network and improve the positioning accuracy;

[0108] S56, the manual auxiliary inspection module can also view the direction of the pipeline network, the installation and parameter information of the cascade pipeline network, cascade equipment and corresponding position sensors in real time; such as the pipeline network diameter, pipeline network material, pipeline network transmission fluid type, pipeline network construction method, pipeline network unit price, etc., to assist staff in operation and maintenance;

[0109] Optionally, the method further includes: S57, determining the damage condition of the pipeline network corresponding to the abnormal condition based on the movable sensor integrated on the manual auxiliary inspection module for determining the deformation amplitude, corrosion condition and rupture area of ​​the pipeline network;

[0110] The movable sensors used to determine the deformation amplitude, corrosion condition and rupture area of ​​the pipe network refer to pipe network deformation sensors, magnetic leakage sensors and ultrasonic sensors, which can determine the deformation amplitude, corrosion condition and rupture area of ​​the pipe network based on the parameter information collected in real time;

[0111] S58. Automatically generate corresponding operation and maintenance strategies based on the damage of the pipeline network; including:

[0112] S581. When the deformation is large, the corrosion is serious, or the rupture area is large, it is recommended to replace the pipe network and determine its cutting and replacement position and length;

[0113] Large deformation, severe corrosion, and large crack area mean that the corresponding parameter information exceeds a certain preset value;

[0114] S582: When the deformation is small, the corrosion is slight, and there is no crack, it is recommended to patch or continue to use;

[0115] Small deformation and slight corrosion means that the corresponding parameter information is less than a certain preset value;

[0116] S583. Send the parameter information collected on site to the background for further evaluation.

[0117] Furthermore, the pre-built operation status simulation model for the long-distance pipeline network is obtained through training of the composition information of the long-distance pipeline network, and the composition information includes: connection relationship of the transmission trunk line, booster station, pipeline diameter, metering equipment and purification equipment.

[0118] Furthermore, the parameter information includes: pipe section location information and basic pipeline parameters, and the basic pipeline parameters include: at least one of the temperature of the fluid in the pipe, the pressure of the fluid in the pipe, the flow rate of the fluid in the pipe, the pipe diameter and the pipe wall thickness.

[0119] Furthermore, the process of inputting parameter information into the pre-built operation status simulation model for the long-distance pipeline network is as follows:

[0120] According to the pipe section location information in the parameter information, the area corresponding to the pipe section location information is determined in the pre-built operation status simulation model for the long-distance pipeline network, and the pipeline basic parameters in the parameter information are input into the area, and the target pipeline basic parameters corresponding to the area are collected, and the target pipeline basic parameters are output as simulation results.

[0121] Further, the process of determining abnormal data is:

[0122] Preprocess the parameter information collected in real time during the operation of the long-distance pipeline network to be monitored, and determine whether there is any suspected abnormality in the long-distance pipeline network to be monitored based on the preprocessing results;

[0123] When the judgment result is yes, the parameter information of the monitored long-distance pipeline network during operation is continuously collected within a preset unit time period to generate a parameter information set, and the average value of the parameter information within the preset unit time period is determined according to the parameter information set, wherein the preset unit time period includes a preceding time period and a succeeding time period, and the preceding time period is the time period before the moment corresponding to the parameter information with suspected abnormality;

[0124] According to the average value of parameter information and the basic parameters of the target pipeline, the abnormal data of the long-distance pipeline network to be monitored is judged.

[0125] Further, the process of adjusting the collection frequency of parameter information is as follows:

[0126] According to the average value of the parameter information and the target difference of the basic parameters of the target pipeline, the target sampling rate level corresponding to the target difference is determined in the corresponding relationship between the difference and the sampling rate level, and the collection frequency of the parameter information is adjusted according to the target sampling rate level.

[0127] Furthermore, it also includes:

[0128] Generate inspection instructions based on the pipe section position in the abnormal signal;

[0129] According to the inspection instructions, the optimal inspection equipment is deployed to conduct inspections according to the inspection path, and the inspection path is generated according to the pipe section position in the abnormal signal.

[0130] like Figure 2 As shown, the method is completed by a monitoring subsystem arranged in any pipeline transportation master control room, and the monitoring subsystem includes:

[0131] An overall control module, a first interactive module, a first display, a first storage module, n long-distance pipeline network functional modules, an active inspection module, a manual assisted inspection module, a cloud server, a mobile terminal, and a first alarm module; wherein the overall control module includes: a simulation unit, a determination unit, and a monitoring unit.

[0132] Among them, n long-distance pipeline network function modules are connected in sequence and can respectively realize different fluid transportation functions, and it also includes a corresponding data acquisition module, which can collect parameter information of the long-distance pipeline network operation site and send it to the overall control module;

[0133] The overall control module can establish a first simulation model based on the composition information of the long-distance pipeline network; receive parameter information collected in real time by sensors during the operation of the long-distance pipeline network; synchronously transmit the parameter information collected by specific sensors to the first simulation model to simulate the operation status of the long-distance pipeline network; analyze and process the parameter information in combination with the simulation results of the first simulation model to obtain abnormal conditions of the long-distance pipeline network; adjust the sampling rate of the sensor based on the abnormal conditions; mobilize the active inspection module to quickly move to the abnormality and confirm the abnormality; generate and distribute manual inspection tasks, and the staff performs on-site operation and maintenance based on the content of the manual inspection tasks;

[0134] When an abnormal situation is confirmed, the overall control module controls the first alarm module to sound an alarm;

[0135] The overall control module can transmit real-time data information to the cloud server, and the staff can monitor the safety status of the long-distance pipeline network through mobile terminals;

[0136] The active inspection module can realize autonomous inspection of long-distance pipeline networks;

[0137] The manual assisted inspection module can assist staff in performing manual inspection tasks;

[0138] The first interactive module, the first display, and the first storage module respectively have the functions of interacting with control instructions, displaying information related to the long-distance pipeline network, and storing data information;

[0139] The fixed position sensor may be a data acquisition module arranged at the corresponding positions of the n long-distance pipeline network function modules;

[0140] n long-distance pipeline network functional modules include a first control module, a first secondary cache module, a first power supply module, a first communication module, a second alarm module, a second interaction module, a second display, a second storage module, and a data acquisition module;

[0141] The first control module can realize the control of the long-distance pipeline network function module, complete the preprocessing of the real-time acquisition parameter information, and cache, store and send the preprocessed parameter information;

[0142] The caching, storage and sending of parameter information are respectively implemented by the first secondary cache module, the second storage module and the first communication module;

[0143] The data acquisition module is a fixed position sensor, including a first temperature sensor, a first humidity sensor, a first pressure sensor, a first flow velocity sensor, a first pipe network deformation sensor, a first magnetic leakage sensor and a first ultrasonic sensor, a second temperature sensor, a second humidity sensor, a first soil pH sensor, a first position sensor, a first wind speed sensor, and a first high-definition camera module. Different data acquisition modules can realize real-time acquisition of different types of parameter information;

[0144] The first temperature sensor, the first humidity sensor, the first pressure sensor, and the first flow rate sensor can realize the collection of temperature, humidity, pressure, and flow rate parameter information inside and around the pipe network;

[0145] The first position sensor, the second temperature sensor, the second humidity sensor, the first soil pH sensor, and the first wind speed sensor can realize the collection of the location of the pipe network and the corresponding ambient temperature, humidity, soil pH, and wind speed parameter information of the location;

[0146] The active inspection module includes: a second control module, a second secondary cache device, a second power supply module, a second communication module, a third alarm module, a third interactive module, a third display, a third storage module, a second pressure sensor, a second flow velocity sensor, a second pipe network deformation sensor, a second magnetic leakage sensor and a second ultrasonic sensor, a third temperature sensor, a third humidity sensor, a second soil pH sensor, a second position sensor, a second wind speed sensor, and a second high-definition camera module;

[0147] The second control module can complete path planning, automatic navigation, image recognition, and can collect parameter information preprocessing in real time, and cache, store and send the preprocessed parameter information; the cache, storage and sending of the parameter information are respectively realized by the second secondary cache module, the third storage module and the second communication module;

[0148] The active inspection module is provided with movable sensors, including: a second pressure sensor, a second flow velocity sensor, a second pipe network deformation sensor, a second magnetic leakage sensor, a second ultrasonic sensor, a third temperature sensor, a third humidity sensor, a second soil pH sensor, a second position sensor, a second wind speed sensor, and a second high-definition camera module; it can realize the real-time collection of different types of parameter information;

[0149] The manual auxiliary inspection equipment includes: a third control module, a fourth interactive module, a fourth display, a fourth storage module, a third secondary cache module, a third communication module, a third pressure sensor, a third flow velocity sensor, a third pipe network deformation sensor, a third magnetic leakage sensor and a third ultrasonic sensor, a fourth temperature sensor, a fourth humidity sensor, a third soil pH sensor, a third position sensor, a third wind speed sensor, and a third high-definition camera module;

[0150] The third control module can complete path planning, automatic navigation, image recognition, and can collect parameter information preprocessing in real time, and cache, store and send the preprocessed parameter information; the cache, storage and sending of the parameter information are respectively realized by the third secondary cache module, the fourth storage module and the third communication module;

[0151] The manual auxiliary inspection equipment is provided with movable sensors, including: a third pressure sensor, a third flow velocity sensor, a third pipe network deformation sensor, a third magnetic leakage sensor and a third ultrasonic sensor, a fourth temperature sensor, a fourth humidity sensor, a third soil pH sensor, a third position sensor, a third wind speed sensor, and a third high-definition camera module; it can realize the real-time collection of different types of parameter information;

[0152] Optionally, the overall control module can also simultaneously obtain the same type of parameter information at a certain position node based on the movable sensor and the fixed position sensor, and determine whether the fixed position sensor has an abnormality by making a difference; when the difference between the two is lower than a first threshold, it is determined that the fixed position sensor has no abnormality; when the difference between the two is greater than a second threshold, it is determined that the fixed position sensor has an abnormality, and the staff is prompted to replace it; when the difference between the two is between the first threshold and the second threshold, the parameter information collected by the fixed position sensor is compensated based on the difference between the two;

[0153] The overall control module can synchronously transmit the parameter information collected by the specific sensor to the first simulation model to simulate the operation status of the long-distance pipeline network.

[0154] The overall control module can analyze and process the parameter information in combination with the simulation results of the first simulation model to obtain abnormal conditions of the long-distance pipeline network. This process has been elaborated in detail above and will not be repeated here.

[0155] The overall control module can also: execute S2 to determine abnormal data based on the simulation results and the preset abnormal conditions.

[0156] The overall control module adjusts the sampling rate of the sensor based on the abnormal condition.

[0157] The overall control module can also establish a 3D simulation model of the long-distance pipeline network and display the real operating status of the long-distance pipeline network in real time on the display device. When an abnormal situation occurs, the corresponding location node is highlighted;

[0158] The parameter information collected by the sensor in real time, the parameter information at the corresponding position determined by the first simulation model, and the difference between the two can also be displayed in real time on the display device to assist the staff in autonomous diagnosis of abnormal conditions;

[0159] The display device may be a first, second, third, or fourth display;

[0160] The overall control module mobilizes the active inspection module to quickly move to the abnormality location to confirm the abnormality; including:

[0161] Determine the location node information where the abnormal situation occurs and generate corresponding inspection instructions;

[0162] Combined with the distribution and status information of active inspection modules around the location node, the inspection command is sent to the appropriate active inspection module;

[0163] The active inspection module automatically plans the route based on the inspection instructions, moves to the location node where the abnormal situation occurs, and conducts on-site confirmation of the abnormal situation;

[0164] During the movement of the active inspection module, the built-in movable sensor detects in real time whether there are any abnormalities in the fixed position sensors on the moving path, and executes different strategies based on the detection results;

[0165] When the active inspection module cannot confirm the abnormal situation, manual inspection is carried out;

[0166] The overall control module can generate and distribute manual inspection tasks. The staff can perform on-site operation and maintenance based on the contents of the manual inspection tasks, including:

[0167] According to the location node information where the abnormal situation occurs, a first range is determined, and a corresponding manual inspection task is generated and issued;

[0168] Inspection personnel can receive manual inspection tasks through the manual auxiliary inspection module they wear;

[0169] The manual assisted inspection module can be MR glasses or other handheld terminals;

[0170] Based on the manual assisted inspection module, the navigation information of the location node where the abnormal situation occurs is played in real time;

[0171] When the inspection personnel arrives in the first range, the location, direction, abnormal location nodes, and specific parameter information of the pipeline network are highlighted on the display interface of the manual auxiliary inspection module based on the 3D simulation model of the long-distance pipeline network. The inspection personnel's own location is also displayed, and voice prompts and direction signs are used to assist the staff to quickly move to the laying or burying path of the pipeline network corresponding to the abnormal location node or near the abnormal equipment;

[0172] The manual inspection module uses the camera module to capture the "pipeline laying signs below" set at intervals above the pipeline laying path in real time. After discovering the laying signs, the manual inspection module will locate, navigate and calibrate the pipeline laying path based on the laying signs, pre-stored map data and the inspection personnel's own position;

[0173] The manual assisted inspection module can also view the direction of the pipeline network, the installation and parameter information of the cascade pipeline network, cascade equipment and corresponding position sensors in real time to assist staff in operation and maintenance.

[0174] Optionally, it also includes: determining the damage condition of the pipeline network corresponding to the abnormal condition based on a movable sensor integrated on the manual auxiliary inspection module for determining the deformation amplitude, corrosion condition and rupture area of ​​the pipeline network;

[0175] Optionally, corresponding operation and maintenance strategies can be automatically generated based on the damage of the pipeline network, including:

[0176] When the deformation is large, the corrosion is serious, or the rupture area is large, it is recommended to replace the pipe network and determine its cutting and replacement position and length; when the deformation is small, the corrosion is slight, and there is no rupture, it is recommended to patch or continue to use;

[0177] Large deformation, severe corrosion, and large crack area mean that the corresponding parameter information exceeds a certain preset value; small deformation and slight corrosion mean that the corresponding parameter information is less than a certain preset value;

[0178] The parameter information collected on site can also be sent to the background for further evaluation.

[0179] The present invention provides a long-distance pipeline network safety monitoring method and system based on the Internet, comprising establishing a first simulation model based on the composition information of the long-distance pipeline network; using sensors to collect parameter information of the long-distance pipeline network in real time during operation; synchronously transmitting the parameter information collected by a specific sensor to the first simulation model to simulate the operation status of the long-distance pipeline network; analyzing and processing the parameter information in combination with the simulation result of the first simulation model to obtain abnormal conditions of the long-distance pipeline network; adjusting the sampling rate of the sensor based on the abnormal conditions; mobilizing an active inspection module to quickly move to the abnormality occurrence site to confirm the abnormal conditions; generating and distributing manual inspection tasks, and the staff performs on-site operation and maintenance based on the contents of the manual inspection tasks; the present invention can realize accurate monitoring of the operation status of the long-distance pipeline network and quickly operate and maintain the abnormal conditions.

[0180] 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.

[0181] The present invention also provides an Internet-based long-distance pipeline network safety monitoring system, and the specific technical solution is as follows:

[0182] The simulation unit is used to: collect parameter information of the long-distance pipeline network to be monitored in real time during operation, and input the parameter information into a pre-built simulation model of the operation status of the long-distance pipeline network to obtain simulation results;

[0183] The determination unit is used to: determine abnormal data based on the simulation results and in combination with preset abnormal conditions;

[0184] The monitoring unit is used to adjust the acquisition frequency of parameter information based on abnormal data and generate an abnormal signal at the same time.

[0185] It should be noted that the beneficial effects of the Internet-based long-distance pipeline network safety monitoring system provided by the above embodiment are the same as the beneficial effects of the Internet-based long-distance pipeline network safety monitoring method described above, which will not be repeated here. In addition, when the system provided by the above 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 by the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0186] like Figure 3As 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:

[0187] 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 a long-distance pipeline network safety monitoring method based on the Internet provided in the above 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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. A long-distance pipeline network safety monitoring method based on the Internet, characterized in that: include: Collect parameter information of the long-distance pipeline network to be monitored in real time during operation, and input the parameter information into a pre-built simulation model of the operation status of the long-distance pipeline network to obtain simulation results; Based on the simulation results and in combination with preset abnormal conditions, determining abnormal data; Based on the abnormal data, the acquisition frequency of the parameter information is adjusted, and an abnormal signal is generated at the same time.

2. According to claim 1, a long-distance pipeline network safety monitoring method based on the Internet is characterized in that: The pre-built operation status simulation model for the long-distance pipeline network is obtained by training the composition information of the long-distance pipeline network, and the composition information includes: connection relationship of the transmission trunk line, pressure station, pipeline diameter, metering equipment and purification equipment.

3. The Internet-based long-distance pipeline network safety monitoring method according to claim 1 is characterized in that: The parameter information includes: pipe section location information and basic pipeline parameters, and the basic pipeline parameters include: at least one of the temperature of the fluid in the pipe, the pressure of the fluid in the pipe, the flow rate of the fluid in the pipe, the pipe diameter and the pipe wall thickness.

4. The Internet-based long-distance pipeline network safety monitoring method according to claim 3 is characterized in that: The process of inputting the parameter information into the pre-built operation status simulation model for the long-distance pipeline network is as follows: According to the pipe section position information in the parameter information, an area corresponding to the pipe section position information is determined in the pre-built operation status simulation model for the long-distance pipeline network, and the pipeline basic parameters in the parameter information are input into the area, and the target pipeline basic parameters corresponding to the area are collected, and the target pipeline basic parameters are output as the simulation results.

5. The Internet-based long-distance pipeline network safety monitoring method according to claim 4 is characterized in that: The process of determining abnormal data is: Preprocessing the parameter information collected in real time during the operation of the long-distance pipeline network to be monitored, and judging whether there is a suspected abnormality in the long-distance pipeline network to be monitored according to the preprocessing result; When the judgment result is yes, the parameter information of the monitored long-distance pipeline network during operation is continuously collected within a preset unit time period to generate a parameter information set, and the average value of the parameter information within the preset unit time period is determined according to the parameter information set, wherein the preset unit time period includes a preceding time period and a succeeding time period, and the preceding time period is the time period before the moment corresponding to the parameter information with suspected abnormality; According to the average value of the parameter information and the basic parameters of the target pipeline, abnormal data of the long-distance pipeline network to be monitored is determined.

6. The Internet-based long-distance pipeline network safety monitoring method according to claim 5 is characterized in that: The process of adjusting the acquisition frequency of the parameter information is as follows: According to the average value of the parameter information and the target difference of the target pipeline basic parameter, the target sampling rate level corresponding to the target difference is determined in the corresponding relationship between the difference and the sampling rate level, and the collection frequency of the parameter information is adjusted according to the target sampling rate level.

7. The Internet-based long-distance pipeline network safety monitoring method according to claim 1 is characterized in that: Also includes: Generate an inspection instruction according to the pipe section position in the abnormal signal; According to the inspection instruction, the optimal inspection equipment is deployed to perform inspection according to the inspection path, and the inspection path is generated according to the pipe section position in the abnormal signal.

8. An Internet-based long-distance pipeline network safety monitoring system, characterized in that: include: The simulation unit is used to: collect parameter information of the long-distance pipeline network to be monitored in real time during operation, and input the parameter information into a pre-built simulation model for the operation status of the long-distance pipeline network to obtain simulation results; The determination unit is used to: determine abnormal data based on the simulation result and in combination with preset abnormal conditions; The monitoring unit is used to: adjust the acquisition frequency of the parameter information based on the abnormal data, and generate an abnormal signal at the same time.

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 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium 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 the method according to any one of claims 1 to 7.

Images