Safety monitoring method, device and system for long-distance pipeline and electronic equipment

Abstract

The invention relates to a safety monitoring method, device and system for a long-distance pipeline and electronic equipment, and the method comprises the steps: determining the number N of key pipeline nodes from each pipe section according to a sorting result of influence weight values of each pipe section and a preset influence weight threshold value; respectively determining a calculation load value X and a calculation load value Y according to the model of the controller; determining and calculating a total load value Z according to a pre-established performance-operation load curve of the controller; calculating a load value X, a load value Y and a total load value Z according to the number N of key pipeline nodes, and calculating N1 pairs of target sensors and the number N2 of estimated data; according to the sorting result, the N1 pairs of target sensors are arranged in the corresponding key pipeline nodes, so that the collected data in the corresponding key pipeline nodes are obtained based on the N1 pairs of target sensors, and safety monitoring of the long-distance pipeline is achieved. Through the method provided by the invention, the sensor setting cost can be saved while the system monitoring precision is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of long-distance pipeline production control, and in particular to a long-distance pipeline safety monitoring method, device, system and electronic equipment. Background Art

[0002] In the existing monitoring and control system for long-distance oil and gas pipelines, in order to ensure the safety of the oil and gas pipelines, sensors need to be set up to obtain the oil and gas status and pipeline status in the pipeline. However, since the oil and gas pipelines are tens or hundreds of kilometers long, in order to realize the monitoring and control of long-distance pipelines, a large number of sensors are required to collect pipeline status data, resulting in excessively high costs for setting up the monitoring and control system for long-distance oil and gas pipelines, and the setting of a large number of sensors also increases the subsequent maintenance costs. For example, the invention patent with the patent document publication number CN116482227A provides a pipeline corrosion monitoring method, which collects real-time dynamic data of multiple areas in the pipeline through multiple sensors; the sensors include multiple types of hanging sheet sensors, probe sensors, and ultrasonic sensors, and this setting method is too costly.

[0003] In order to reduce the cost of setting up the monitoring and control system for long-distance oil and gas pipelines, there are also literatures that can estimate the pressure and flow data of the surrounding pipelines based on the sensors set in a certain pipeline node according to the correlation between the data collected by adjacent sensors in the pipeline, and realize the monitoring and control of long-distance pipelines based on the estimated data. However, this method is too dependent on the setting accuracy of the estimation model, and when performing data estimation, it involves the calculation of massive data, which requires too high a computing power of the processor. Setting up a high-performance processor will also increase the system setting cost. How to reduce the system cost while ensuring the system monitoring accuracy is an urgent problem to be solved in the monitoring and control of long-distance pipelines.

[0004] In addition, for long-distance pipeline monitoring and control systems, that is, oil and gas long-distance pipeline systems, the development and design costs are too high, and for the existing old long-distance pipeline monitoring and control systems, system upgrades are too difficult. How to find a universal and easy-to-setup long-distance pipeline monitoring and control system is also one of the problems that urgently need to be solved in long-distance pipeline monitoring and control systems.

[0005] Finally, since the long-distance pipeline laying area is too large and the installation environment is relatively complex, when the sensor fails, it is also necessary to consider the issue of facilitating timely replacement. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a long-distance pipeline safety monitoring method, device, system and electronic equipment, aiming to solve at least one of the above technical problems.

[0007] In the first aspect, the technical solution of the present invention to solve the above technical problem is as follows: a safety monitoring method for a long-distance pipeline, the method comprising:

[0008] S1, obtaining a pipeline topology map corresponding to a long-distance pipeline, and calculating an influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map;

[0009] S2, sorting the influence weight values ​​of each pipe section, selecting key pipeline nodes from each pipe section according to the sorting result and a preset influence weight threshold, and determining the number N of the key pipeline nodes;

[0010] S3, determining, according to the controller model, a calculation load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculation load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor;

[0011] S4, determining the operating load when the controller is at optimal performance according to a pre-established performance-operating load curve of the controller, and determining a total calculated load value Z corresponding to the operating load;

[0012] S5, calculating N1 pairs of target sensors and the number of estimated data N2 according to the number N of key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z;

[0013] S6, according to the ranking result of the influence weight value of each pipe section, the N1 pairs of target sensors are set in the corresponding key pipeline nodes, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, so as to realize the safety monitoring of the long-distance pipeline.

[0014] The beneficial effects of the present invention are as follows: by comprehensively considering the pipe network topology, the geographical structure corresponding to the installation position of each pipe section, the environmental parameters and the hardware parameters of the controller (calculated load value X, calculated load value Y and calculated load total value Z), the number of sensors to be set (N1 pairs of target sensors) and the layout position of each pair of sensors in the pipe network are determined, which can save the sensor setting cost while ensuring the monitoring accuracy of the system (oil and gas long-distance pipeline system); at the same time, the number of sensors determined according to the operating capacity of the controller is more in line with the actual application scenario of the system (oil and gas long-distance pipeline system), and the number of sensors set and the operating load of the controller are balanced, ensuring the optimal monitoring accuracy of the system (oil and gas long-distance pipeline system) under the best operating conditions.

[0015] Based on the above technical solution, the present invention can also be improved as follows.

[0016] Furthermore, the method further comprises:

[0017] S7, receiving, by the controller, collected data of a single pair of target sensors in each key pipeline node, performing estimation processing according to each of the collected data, and obtaining at least one estimated data, each of the estimated data being predicted collected data of another pipeline node in the long-distance pipeline except each of the key pipeline nodes;

[0018] S8, sending each of the collected data and each of the estimated data to the cloud-based digital twin model, so that the cloud-based digital twin model can calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data to determine whether a leakage fault occurs in the long-distance pipeline.

[0019] Furthermore, in S1, the influence weight value of each pipe section is calculated according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map, including:

[0020] For each pipe section, the geographical structure of the installation location of the pipe section is obtained, the terrain complexity of the pipe section is determined according to the geographical structure corresponding to the installation location of each pipe section, and the terrain influence factor of the pipe section is determined according to the terrain complexity;

[0021] For each pipe section, historical meteorological data corresponding to the installation location of the pipe section is obtained, historical environmental data of the pipe section is determined based on the historical meteorological data corresponding to the installation location of each pipe section, and the environmental impact factor of the pipe section is determined based on the historical environmental data;

[0022] For each pipe section, determining the importance factor of the pipe section according to the degree of association between the pipe section and other pipe sections in the pipeline topology diagram;

[0023] For each pipe section, the influence weight value of the pipe section is determined by a pre-trained neural network model based on the terrain influence factor, the environmental influence factor and the importance factor.

[0024] Furthermore, in S2, according to the sorting result and the preset impact weight threshold, the key pipeline nodes are selected from each section, and the number N of the key pipeline nodes is determined, including:

[0025] Determine the original number of sensors corresponding to the pipeline topology according to a preset first correspondence relationship and the pipeline topology, wherein the first correspondence relationship is a correspondence relationship between different pipeline topologies and the number of corresponding sensors;

[0026] Determining a preset impact weight threshold corresponding to the pipeline topology map according to the most original number and the pre-trained margin calculation model;

[0027] According to the sorting results, the pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold are determined as the key pipeline nodes, and the number of pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold is determined as the number N of key pipeline nodes.

[0028] Furthermore, for each pair of sensors, the pressure sensor and the flow sensor are both self-generating sensors, and a turbine is respectively provided in the pressure sensor and the flow sensor, so that the pressure sensor and the flow sensor can realize self-generated power supply by utilizing the fluid in the pipeline.

[0029] Furthermore, for each pair of sensors, the pressure sensor and the flow sensor are fixed by magnetic attraction, and are arranged or repositioned by a pipeline robot.

[0030] Furthermore, after the cloud digital twin model receives each collected data and each estimated data, it also includes:

[0031] For each pair of sensors, fault monitoring is performed on the pair of sensors based on the fluctuation degree of the collected data of the adjacent pair of sensors compared with the collected data of the pair of sensors.

[0032] In a second aspect, in order to solve the above technical problems, the present invention also provides a safety monitoring device for a long-distance pipeline, the device comprising:

[0033] An acquisition module is used to acquire a pipeline topology map corresponding to a long-distance pipeline, and calculate an influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map;

[0034] The key pipeline node determination module is used to sort the influence weight values ​​of each section of the pipeline, select key pipeline nodes from each section of the pipeline according to the sorting result and the preset influence weight threshold, and determine the number N of the key pipeline nodes;

[0035] A load value determination module, used to determine, according to the controller model, a calculated load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculated load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor;

[0036] A total load value determination module, used to determine the operating load when the controller is at the best performance according to the pre-established performance-operation load curve of the controller, and determine the calculated total load value Z corresponding to the operating load;

[0037] A calculation module, used for calculating N1 pairs of target sensors and the number of estimated data N2 according to the number N of the key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z;

[0038] The safety monitoring module is used to set the N1 pairs of target sensors in the corresponding key pipeline nodes according to the sorting results of the influence weight values ​​of each section of the pipeline, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, so as to realize the safety monitoring of the long-distance pipeline.

[0039] In a third aspect, in order to solve the above technical problems, the present invention also provides a long-distance pipeline safety monitoring system, comprising a plurality of pairs of sensors, a controller and a cloud server, each pair of sensors comprising a pressure sensor and a flow sensor; wherein each pair of sensors is arranged inside a corresponding key pipeline node, and is used to collect data in the corresponding key pipeline node and send it to the controller;

[0040] The controller is used to perform estimation processing according to the received collected data in each key pipeline node, obtain estimated data, and the estimated data is the predicted collected data of other pipeline nodes in the long-distance pipeline except each key pipeline node, and send each collected data and each estimated data to the cloud server;

[0041] The cloud server is used to calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data through a pre-trained digital twin model, and determine whether a leakage failure occurs in the long-distance pipeline.

[0042] In a fourth aspect, in order to solve the above-mentioned technical problem, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the long-distance pipeline safety monitoring method of the present application is implemented.

[0043] In a fifth aspect, in order to solve the above-mentioned technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the long-distance pipeline safety monitoring method of the present application is implemented.

[0044] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments of the present invention are briefly introduced below.

[0046] Figure 1 A schematic flow chart of a safety monitoring method for a long-distance pipeline provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the structure of a safety monitoring system for a long-distance pipeline provided by one embodiment of the present invention;

[0048] Figure 3 A schematic structural diagram of a safety monitoring device for a long-distance pipeline provided by one embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0051] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0052] The solution provided by the embodiment of the present invention can be applied to any application scenario where long-distance pipeline safety monitoring is required. The solution provided by the embodiment of the present invention can be executed by any electronic device, for example, it can be a user's terminal device, including at least one of the following: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart vehicle-mounted device, and the electronic device can run an oil and gas long-distance pipeline monitoring and control system.

[0053] The embodiment of the present invention provides a possible implementation method, such as Figure 1 As shown, a flowchart of a long-distance pipeline safety monitoring method is provided. The scheme can be executed by any electronic device, for example, a terminal device, or a terminal device and a server. For the convenience of description, the method provided by the embodiment of the present invention will be described below by taking the terminal device as the execution subject as an example. Figure 1 The method may include the following steps:

[0054] S1, obtaining a pipeline topology map corresponding to a long-distance pipeline, and calculating an influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map;

[0055] S2, sorting the influence weight values ​​of each pipe section, selecting key pipeline nodes from each pipe section according to the sorting result and a preset influence weight threshold, and determining the number N of the key pipeline nodes;

[0056] S3, determining, according to the controller model, a calculation load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculation load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor;

[0057] S4, determining the operating load when the controller is at the best performance according to the pre-established performance-operating load curve of the controller, and determining the total calculated load value Z corresponding to the operating load;

[0058] S5, calculating N1 pairs of target sensors and the number of estimated data N2 according to the number N of key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z;

[0059] S6, according to the ranking result of the influence weight value of each pipe section, the N1 pairs of target sensors are set in the corresponding key pipeline nodes, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, so as to realize the safety monitoring of the long-distance pipeline.

[0060] Through the method of the present invention, the pipe network topology map, the geographical structure corresponding to the installation position of each pipe section, the environmental parameters and the hardware parameters of the controller (calculated load value X, calculated load value Y and calculated load total value Z) are comprehensively considered to determine the number of sensors to be set (N1 pairs of target sensors) and the layout positions of each pair of sensors in the pipe network, which can save the sensor setting cost while ensuring the system monitoring accuracy; at the same time, the number of sensors is determined according to the operating capacity of the controller. This setting method is more in line with the actual application scenario of the system, balances the number of sensors to be set and the operating load of the controller, and ensures the optimal monitoring accuracy of the system under the best operating conditions.

[0061] The solution of the present invention is further described below in conjunction with the following specific embodiments. In this embodiment, a safety monitoring method for a long-distance pipeline is provided, which may include the following steps:

[0062] S1, obtaining a pipeline topology map corresponding to a long-distance pipeline, and calculating an influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map;

[0063] Among them, the pipeline topology map is a topology map used to describe the position of each section of each pipeline (including long-distance pipelines) in the pipeline network and the positional relationship between each section of the pipeline. The pipeline topology map can be determined based on the following methods: obtaining the pipeline laying design drawing, and extracting the pipeline topology map based on the pipeline laying design drawing.

[0064] For each pipe section, the size of the influence weight value corresponding to the pipe section represents the importance of the pipe section to the long-distance pipeline among all the pipe sections.

[0065] Geographical structures and environmental parameters can affect the safety of corresponding pipe sections. For example, different geographical structures and / or different environmental parameters can cause different degrees of corrosion damage to pipe sections, affecting the safe transmission of long-distance pipelines. When determining the impact weight value of each pipe section, the location, geographical structure and environmental parameters of each pipe section are taken into consideration, so that the impact weight value of each pipe section can be determined more accurately. Each impact weight value can be represented by a value from 0 to 1.

[0066] Optionally, in the above S1, the influence weight value of each pipe section is calculated according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map, including:

[0067] S10, for each pipe section, obtaining the geographical structure of the installation location of the pipe section, determining the terrain complexity of the pipe section according to the geographical structure corresponding to the installation location of each pipe section, and determining the terrain influence factor a of the pipe section according to the terrain complexity;

[0068] The geographical structure may be a stratigraphic structure, a mountain, a hill, a river or other geographical structure. Different geographical structures are assigned a certain terrain complexity value, and then the terrain influence factor is determined according to each terrain complexity value.

[0069] S11, for each pipe section, obtaining historical meteorological data corresponding to the installation position of the pipe section, determining historical environmental data of the pipe section according to the historical meteorological data corresponding to the installation position of each pipe section, and determining an environmental impact factor b of the pipe section according to the historical environmental data;

[0070] Among them, the historical meteorological data can be whether it is rainy or dry, etc., to determine the environmental parameters such as temperature and humidity at the installation location of each pipe section, and then the environmental influencing factors can be determined based on the determined environmental parameters.

[0071] S12, for each pipe section, determining an importance factor c of the pipe section according to the degree of association between the pipe section and other pipe sections in the pipeline topology diagram;

[0072] S13, for each pipe section, based on the terrain influence factor, environmental influence factor and importance factor, determine the influence weight value d of the pipe section through a pre-trained neural network model.

[0073] The neural network model can determine and output the influence weight value d of the section of the pipeline based on the terrain influence factor a, the environmental influence factor b, and the importance factor c. Specifically, the neural network model determines which of the terrain influence factor a, the environmental influence factor b, and the importance factor c of each section of the pipeline has a higher influence on the section of the pipeline according to the historical operation data of the system (the long-distance oil and gas pipeline system), and assigns a higher weight value to the factor. For example, at the location of the section of the pipeline, the terrain influence is greater, and the terrain influence factor a is assigned a higher weight value;

[0074] It should be noted that the system in the present application includes two systems, one of which refers to the oil and gas long-distance pipeline system, that is, the system that does not adopt the long-distance pipeline safety monitoring method of the present application, that is, the system in the background technology. The other refers to the long-distance pipeline safety monitoring system in the present application, that is, the system that adopts the long-distance pipeline safety monitoring method of the present application.

[0075] Specifically, d = a*i+b*j+c*k;

[0076] Among them, i is the weight value corresponding to the terrain impact factor, j is the weight value corresponding to the environmental impact factor, and i is the weight value corresponding to the importance factor;

[0077] S2, sorting the influence weight values ​​of each pipe section, selecting key pipeline nodes from each pipe section according to the sorting result and a preset influence weight threshold, and determining the number N of the key pipeline nodes;

[0078] Among them, the key pipeline node refers to a representative pipeline section in each section, for example, a pipeline section whose influence weight value is not less than a preset influence weight threshold.

[0079] Optionally, the influence weight values ​​may be sorted, for example, from large to small, or from small to large, and the present application does not limit the sorting method.

[0080] Optionally, in the above S2, according to the sorting result and the preset influence weight threshold, the key pipeline nodes are selected from each section, and the number N of the key pipeline nodes is determined, including:

[0081] S20, determining the original number of sensors corresponding to the pipeline topology according to a preset first correspondence relationship and the pipeline topology, wherein the first correspondence relationship is a correspondence relationship between different pipeline topologies and the number of corresponding sensors;

[0082] The first corresponding relationship can be determined based on the historical operation data of the system (long-distance oil and gas pipeline system), and the corresponding relationship between different pipeline topologies and the number of corresponding sensors can be extracted based on the historical operation data, that is, different pipeline topology scales require different numbers of sensors to be set. As the scale of the pipeline topology increases, the number of corresponding sensors increases accordingly, and as the scale of the pipeline topology decreases, the number of corresponding sensors decreases accordingly.

[0083] The most original number is the number of sensors required to be set up for the pipeline topology corresponding to the long-distance pipeline. In the present application, the sensors are set up in pairs, and the most original number refers to the logarithm.

[0084] S21, determining a preset impact weight threshold corresponding to the pipeline topology map according to the most original number and the pre-trained margin calculation model;

[0085] Among them, the margin calculation model sets a margin percentage for the number of sensors, and the number of sensors (initial number) that need to be set corresponding to the pipeline topology is determined based on the determined number of sensors (the most original number) plus the number of sensors calculated by the margin percentage. The number of sensors that need to be set is for the scale of the pipeline topology. To ensure the optimal monitoring accuracy of the system (long-distance oil and gas pipeline system), it is necessary to monitor based on the collected data of the most original number of sensors; after determining the most original number, the margin calculation model determines the output preset influence weight threshold with reference to the system empirical parameters; the reference system empirical parameters are system correction parameters extracted by learning and updating based on the operating data of multiple long-distance pipeline safety production linkage control systems (long-distance oil and gas pipeline systems).

[0086] Among them, the margin percentage set by the margin calculation model is based on the empirical value, and it is determined that the number of sensors originally determined needs a certain amount of adjustment. The margin percentage is the value that needs to be adjusted. The number of sensors determined by the margin calculation model (initial number) = the most original number determined according to the pipeline topology diagram + empirical value. Then the margin calculation model determines the output preset impact weight threshold based on (initial number) and refers to the system empirical parameters.

[0087] S22, according to the sorting result, determine the pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold as the critical pipeline nodes, and determine the number of pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold as the number N of critical pipeline nodes.

[0088] Among them, a key pipeline node represents a pipe section.

[0089] S3, determining, according to the controller model, a calculation load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculation load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor;

[0090] Among them, the operating capabilities of controllers of different models may be different. The operating capabilities of the controller can be accurately determined based on the controller model. In the present application scheme, the operating capabilities of the controller are characterized based on the calculated load value X, and the calculated load value X is a parameter that characterizes the basic properties of the controller.

[0091] Specifically, the above-mentioned calculated load value X means the operating load value that the controller needs to occupy when preprocessing the collected data of a single pair of sensors; inside the key pipeline node where sensors need to be set, a pressure sensor and a flow sensor are bound and set, and the collected data of a single pair of sensors includes the collected data of a pressure sensor and the collected data of a flow sensor at the same time;

[0092] The calculation load value Y refers to the calculation load that the controller needs to provide when processing the collected data collected by a single pair of pressure sensors. Based on the calculation load value X and the calculation load value Y, the processing capacity of the controller can be determined.

[0093] Specifically, the specific meaning of the above-mentioned calculated load value Y is that the controller needs to estimate the collected data (pressure and flow) in the surrounding associated pipe sections based on the collected data collected by the pressure sensor and the flow sensor in the single pair of sensors (the pressure collected by the pressure sensor and the flow collected by the flow sensor). The operating load value required for the process of extrapolating the collected data in the surrounding associated pipe sections based on the association model is the calculated load value Y.

[0094] Optionally, the above-mentioned controller can be a regional controller for a certain area in the pipeline network or the main controller of the long-distance pipeline safety production linkage control system. It can be used as a controller for a certain area alone, or a combined regional controller for multiple pipelines, or an overall pipeline network controller according to needs, that is, it can be flexibly set according to the computing power of the regional controller or the main controller.

[0095] S4, determining the operating load when the controller is at optimal performance according to a pre-established performance-operating load curve of the controller, and determining a total calculated load value Z corresponding to the operating load;

[0096] The above performance-operation load curve can be determined based on the historical operation data of the controller. The curve is used to reflect the relationship between different load values ​​and different performances. Based on the curve, the operation load at the best performance of the controller can be determined. The best performance can be characterized based on parameters such as the operation speed. For example, the fastest operation speed.

[0097] S5, calculating N1 pairs of target sensors and the number of estimated data N2 according to the number N of key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z;

[0098] Specifically, the number of key pipeline nodes N = N1 + N2;

[0099] Calculate the total load value Z = N1*X+N2*Y;

[0100] S6, according to the ranking result of the influence weight value of each pipe section, the N1 pairs of target sensors are set in the corresponding key pipeline nodes, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, so as to realize the safety monitoring of the long-distance pipeline.

[0101] In each key pipeline node, sensors are arranged in pairs. If a pair of sensors is arranged in a key pipeline node, then N1 pairs of target sensors can be arranged in N1 key pipeline nodes according to the sorting result.

[0102] Optionally, for each pair of sensors, the pressure sensor and the flow sensor are both self-generating sensors, and a turbine is respectively provided in the pressure sensor and the flow sensor, so that the pressure sensor and the flow sensor can realize self-generated power supply by utilizing the fluid in the pipeline.

[0103] Optionally, for each pair of sensors, the pressure sensor and the flow sensor are fixed by magnetic attraction and arranged or repositioned by a pipeline robot.

[0104] Optionally, for each key pipeline node, a terrain monitoring device can be added outside the key pipeline node to monitor the terrain changes outside the key pipeline node in real time, and monitor the environmental changes outside the key pipeline node based on meteorological data. When the terrain changes and meteorological changes outside a key node pipeline are too large and exceed their respective corresponding specified thresholds, the key pipeline nodes in the pipeline network can be recalculated, that is, the key pipeline nodes can be reselected, and the positions of sensors can be modified and arranged nearby using pipeline robots to achieve system transformation without stopping production.

[0105] Optionally, for each pair of sensors, the pressure sensor and the flow sensor each include a GPS module and a 5G communication module, wherein, for any sensor in the single pair of sensors, the sensor realizes the transfer and transmission of collected data through the 5G communication module; and the setting position and data collection time of the sensor are sent to the corresponding 5G communication module through the GPS module for data integration. When GPS positioning is used, the line transmission response time difference can be eliminated.

[0106] Optionally, the method further includes:

[0107] S7, receiving, by the controller, collected data of a single pair of target sensors in each key pipeline node, performing estimation processing according to each of the collected data, and obtaining at least one estimated data, each of the estimated data being predicted collected data of another pipeline node in the long-distance pipeline except each of the key pipeline nodes;

[0108] The collected data corresponding to the key pipeline nodes can be used to estimate the collected data of the adjacent non-pipeline nodes. The specific implementation method is: for two adjacent key pipeline nodes, based on the collected data corresponding to the two key pipeline nodes, the collected data corresponding to the pipeline nodes between the two key pipeline nodes can be estimated.

[0109] S8, sending each of the collected data and each of the estimated data to the cloud-based digital twin model, so that the cloud-based digital twin model can calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data to determine whether a leakage fault occurs in the long-distance pipeline.

[0110] Among them, the cloud-based digital twin model refers to the digital twin model set up on a cloud server.

[0111] In the present application, one implementation method of calculating and monitoring the pressure and flow of the long-distance pipeline based on the received collected data and estimated data to determine whether a leakage fault occurs in the long-distance pipeline is:

[0112] For each pipe section corresponding to the long-distance pipeline, if the pressure corresponding to the pipe section is greater than a first set value, and / or the flow corresponding to the pipe section is greater than a second set value, it is determined that a leakage fault occurs in the pipe section; otherwise, it is determined that no leakage fault occurs in the pipe section.

[0113] Optionally, after the cloud digital twin model receives each collected data and each estimated data, it further includes:

[0114] For each pair of sensors, fault monitoring can also be performed on the pair of sensors based on the collected data of the adjacent pair of sensors of the pair of sensors and the degree of fluctuation of the collected data of the pair of sensors (the degree of fluctuation can be reflected based on the difference in pressure and flow corresponding to the pair of sensors and the adjacent pair of sensors of the pair of sensors).

[0115] Specifically, there should not be too large a difference between the pressure and flow in the pipeline, that is, the degree of fluctuation should not be too large; after the cloud-based digital twin model receives the collected data and estimated data from the pressure sensor and flow sensor, it makes a horizontal comparison of the collected data and the estimated data. When it is found that the numerical change of the collected data or estimated data corresponding to the adjacent pair of sensors of the pair of sensors exceeds the deviation threshold, it is determined that a pipeline leak has occurred at that location or a sensor failure has occurred, and an alarm signal is output to the management terminal for an alarm prompt.

[0116] The solution of the present invention has the following beneficial effects:

[0117] 1. The system (safety monitoring system for long-distance pipelines) comprehensively considers the pipeline network topology, the geographical structure and environmental parameters corresponding to the installation location of the pipeline, and the hardware parameters of the controller to determine the number of sensors to be set and their layout locations in the pipeline network, which can save the cost of sensor setting while ensuring the system monitoring accuracy;

[0118] 2. The number of sensors is determined according to the operating capacity of the controller. This setting method is more in line with the actual application scenario of the system, balancing the number of sensor settings and the operating load of the controller, ensuring the optimal monitoring accuracy of the system under the best operating conditions, and can be modified according to different settings of the system, which improves the versatility and applicability of the system;

[0119] 3. The sensors use self-generated pressure sensors and flow sensors, and use 5G communication modules to realize the transfer transmission of collected data, which reduces the difficulty of system layout. In addition, magnetic sensors are used, which are easy to replace when the sensors fail. In addition, when the terrain and climate changes are monitored, pipeline robots can be used to adjust and replace the sensor settings in time, which improves the system monitoring accuracy without affecting the production progress.

[0120] 4. Use digital twin models to simulate and monitor data, perceive sensor and pipeline leakage failures in real time, and ensure safe operation of the system;

[0121] 5. The system can be used as a regional controller for a certain pipe network, or a combined regional controller for multiple pipe networks, or a controller for the entire pipe network, according to needs. That is, it can be flexibly set according to the computing power of the regional controller or the main controller, which improves its applicability in the renovation of old pipe networks.

[0122] Based on Figure 1The present invention also provides a long-distance pipeline safety monitoring system, which can also be called an oil and gas long-distance pipeline monitoring and control system or a long-distance pipeline safety production linkage control system based on the industrial Internet. Figure 2 The safety monitoring system of the long-distance pipeline includes multiple pairs of sensors, a controller and a cloud server, each pair of sensors includes a pressure sensor and a flow sensor; wherein each pair of sensors is arranged inside a corresponding key pipeline node, and is used to collect data in the corresponding key pipeline node and send it to the controller;

[0123] The controller is used to perform estimation processing according to the received collected data in each key pipeline node, obtain estimated data, and the estimated data is the predicted collected data of other pipeline nodes in the long-distance pipeline except each key pipeline node, and send each collected data and each estimated data to the cloud server;

[0124] The cloud server is used to calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data through a pre-trained digital twin model, and determine whether a leakage failure occurs in the long-distance pipeline.

[0125] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a safety monitoring device 20 for a long-distance pipeline, such as Figure 3 As shown in , the safety monitoring device 20 for the long-distance pipeline may include an acquisition module 210, a key pipeline node determination module 220, a load value determination module 230, a total load value determination module 240, a calculation module 250 and a safety monitoring module 260, wherein:

[0126] The acquisition module 210 is used to acquire a pipeline topology map corresponding to the long-distance pipeline, and calculate the influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map;

[0127] The key pipeline node determination module 220 is used to sort the influence weight values ​​of each pipeline section, select key pipeline nodes from each pipeline section according to the sorting result and a preset influence weight threshold, and determine the number N of the key pipeline nodes;

[0128] A load value determination module 230, for determining, according to the controller model, a calculated load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculated load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor;

[0129] The total load value determination module 240 is used to determine the operating load when the controller is at the best performance according to the pre-established performance-operating load curve of the controller, and determine the calculated total load value Z corresponding to the operating load;

[0130] A calculation module 250, configured to calculate N1 pairs of target sensors and the number of estimated data N2 according to the number N of key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z;

[0131] The safety monitoring module 260 is used to set the N1 pairs of target sensors in the corresponding key pipeline nodes according to the sorting results of the influence weight values ​​of each section of the pipeline, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, thereby realizing safety monitoring of the long-distance pipeline.

[0132] Optionally, the device further comprises:

[0133] An estimation module, used for receiving the collected data of a single pair of target sensors in each key pipeline node through the controller, performing estimation processing according to each of the collected data, and obtaining at least one estimated data, each of which is the predicted collected data of another pipeline node in the long-distance pipeline except each of the key pipeline nodes;

[0134] The fault monitoring module is used to send each of the collected data and each of the estimated data to the cloud-based digital twin model, so as to calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data through the cloud-based digital twin model to determine whether a leakage fault occurs in the long-distance pipeline.

[0135] Optionally, when the acquisition module 210 calculates the influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map, it is specifically used to:

[0136] For each pipe section, the geographical structure of the installation location of the pipe section is obtained, the terrain complexity of the pipe section is determined according to the geographical structure corresponding to the installation location of each pipe section, and the terrain influence factor of the pipe section is determined according to the terrain complexity;

[0137] For each pipe section, historical meteorological data corresponding to the installation location of the pipe section is obtained, historical environmental data of the pipe section is determined based on the historical meteorological data corresponding to the installation location of each pipe section, and the environmental impact factor of the pipe section is determined based on the historical environmental data;

[0138] For each pipe section, determining the importance factor of the pipe section according to the degree of association between the pipe section and other pipe sections in the pipeline topology diagram;

[0139] For each pipe section, the influence weight value of the pipe section is determined by a pre-trained neural network model based on the terrain influence factor, the environmental influence factor and the importance factor.

[0140] Optionally, when the key pipeline node determination module 220 selects key pipeline nodes from each pipe section according to the sorting result and the preset influence weight threshold, and determines the number N of the key pipeline nodes, it is specifically used to:

[0141] Determine the original number of sensors corresponding to the pipeline topology according to a preset first correspondence relationship and the pipeline topology, wherein the first correspondence relationship is a correspondence relationship between different pipeline topologies and the number of corresponding sensors;

[0142] Determining a preset impact weight threshold corresponding to the pipeline topology map according to the most original number and the pre-trained margin calculation model;

[0143] According to the sorting results, the pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold are determined as the key pipeline nodes, and the number of pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold is determined as the number N of key pipeline nodes.

[0144] Optionally, for each pair of sensors, the pressure sensor and the flow sensor are both self-generating sensors, and a turbine is respectively provided in the pressure sensor and the flow sensor, so that the pressure sensor and the flow sensor can realize self-generated power supply by utilizing the fluid in the pipeline.

[0145] Optionally, for each pair of sensors, the pressure sensor and the flow sensor are fixed by magnetic attraction and arranged or repositioned by a pipeline robot.

[0146] Optionally, after the cloud digital twin model receives each collected data and each estimated data, the device further includes:

[0147] The sensor fault monitoring module is used to perform fault monitoring on each pair of sensors based on the fluctuation degree of the collected data of the adjacent pair of sensors compared with the collected data of the pair of sensors.

[0148] The safety monitoring device for a long-distance pipeline in an embodiment of the present invention can execute the safety monitoring method for a long-distance pipeline provided in an embodiment of the present invention, and its implementation principle is similar. The actions performed by each module and unit in the safety monitoring device for a long-distance pipeline in each embodiment of the present invention correspond to the steps in the safety monitoring method for a long-distance pipeline in each embodiment of the present invention. For the detailed functional description of each module of the safety monitoring device for a long-distance pipeline, please refer to the description in the corresponding safety monitoring method for a long-distance pipeline shown in the previous text, which will not be repeated here.

[0149] Among them, the above-mentioned long-distance pipeline safety monitoring device can be a computer program (including program code) running in a computer device, for example, the long-distance pipeline safety monitoring device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present invention.

[0150] In some embodiments, the safety monitoring device for a long-distance pipeline provided in an embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the safety monitoring device for a long-distance pipeline provided in an embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the safety monitoring method for a long-distance pipeline provided in an embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0151] In other embodiments, the safety monitoring device for long-distance pipelines provided by the embodiments of the present invention may be implemented in a software manner. Figure 3 A safety monitoring device for a long-distance pipeline stored in a memory is shown, which may be software in the form of a program and a plug-in, and includes a series of modules, including an acquisition module 210, a key pipeline node determination module 220, a load value determination module 230, a total load value determination module 240, a calculation module 250 and a safety monitoring module 260, for implementing the safety monitoring method for a long-distance pipeline provided in an embodiment of the present invention.

[0152] The modules involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a module does not limit the module itself in some cases.

[0153] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0154] In an alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0155] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0156] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0157] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0158] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0159] The electronic device may also be a terminal device. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0160] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0161] According to another aspect of the present invention, 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 a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the implementation modes of the above-mentioned various embodiments.

[0162] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0163] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0164] The computer-readable storage medium provided by the embodiment of the present invention 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 of computer-readable storage media may include, but are not limited to, 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 the present invention, 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.

[0165] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0166] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

Claims

1. A safety monitoring method for a long-distance pipeline, characterized in that: include: S1, obtaining a pipeline topology map corresponding to a long-distance pipeline, and calculating an influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map; S2, sorting the influence weight values ​​of each pipe section, selecting key pipeline nodes from each pipe section according to the sorting result and a preset influence weight threshold, and determining the number N of the key pipeline nodes; S3, determining, according to the controller model, a calculation load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculation load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor; S4, determining the operating load when the controller is at optimal performance according to a pre-established performance-operating load curve of the controller, and determining a total calculated load value Z corresponding to the operating load; S5, calculating N1 pairs of target sensors and the number of estimated data N2 according to the number N of key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z; S6, according to the ranking result of the influence weight value of each pipe section, the N1 pairs of target sensors are set in the corresponding key pipeline nodes, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, so as to realize the safety monitoring of the long-distance pipeline.

2. The method according to claim 1, characterized in that The method further comprises: S7, receiving, by the controller, collected data of a single pair of target sensors in each key pipeline node, performing estimation processing according to each of the collected data, and obtaining at least one estimated data, each of the estimated data being predicted collected data of another pipeline node in the long-distance pipeline except each of the key pipeline nodes; S8, sending each of the collected data and each of the estimated data to the cloud-based digital twin model, so that the cloud-based digital twin model can calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data to determine whether a leakage fault occurs in the long-distance pipeline.

3. The method according to claim 1, characterized in that In S1, the influence weight value of each pipe section is calculated according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map, including: For each pipe section, the geographical structure of the installation location of the pipe section is obtained, the terrain complexity of the pipe section is determined according to the geographical structure corresponding to the installation location of each pipe section, and the terrain influence factor of the pipe section is determined according to the terrain complexity; For each pipe section, historical meteorological data corresponding to the installation location of the pipe section is obtained, historical environmental data of the pipe section is determined based on the historical meteorological data corresponding to the installation location of each pipe section, and the environmental impact factor of the pipe section is determined based on the historical environmental data; For each pipe section, determining the importance factor of the pipe section according to the degree of association between the pipe section and other pipe sections in the pipeline topology diagram; For each pipe section, the influence weight value of the pipe section is determined by a pre-trained neural network model based on the terrain influence factor, the environmental influence factor and the importance factor.

4. The method according to claim 1, characterized in that In S2, according to the sorting result and the preset impact weight threshold, the key pipeline nodes are selected from each section, and the number N of the key pipeline nodes is determined, including: Determine the original number of sensors corresponding to the pipeline topology according to a preset first correspondence relationship and the pipeline topology, wherein the first correspondence relationship is a correspondence relationship between different pipeline topologies and the number of corresponding sensors; Determining a preset impact weight threshold corresponding to the pipeline topology map according to the most original number and the pre-trained margin calculation model; According to the sorting results, the pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold are determined as the key pipeline nodes, and the number of pipe sections in each pipe section whose influence weight value is greater than or equal to the preset influence weight threshold is determined as the number N of key pipeline nodes.

5. The method according to claim 1, characterized in that For each pair of sensors, the pressure sensor and the flow sensor are both self-generating sensors, and a turbine is respectively provided in the pressure sensor and the flow sensor, so that the pressure sensor and the flow sensor can realize self-generated power supply by utilizing the fluid in the pipeline.

6. The method according to claim 1, characterized in that For each pair of sensors, the pressure sensor and the flow sensor are fixed by magnetic attraction and arranged or repositioned by a pipeline robot.

7. The method according to claim 2, characterized in that After the cloud digital twin model receives each collected data and each estimated data, it also includes: For each pair of sensors, fault monitoring is performed on the pair of sensors based on the fluctuation degree of the collected data of the adjacent pair of sensors compared with the collected data of the pair of sensors.

8. A safety monitoring device for a long-distance pipeline, characterized in that: include: An acquisition module is used to acquire a pipeline topology map corresponding to a long-distance pipeline, and calculate an influence weight value of each pipe section according to the geographical structure and environmental parameters corresponding to the installation position of each pipe section in the pipeline topology map and the node position in the pipeline topology map; The key pipeline node determination module is used to sort the influence weight values ​​of each section of the pipeline, select key pipeline nodes from each section of the pipeline according to the sorting result and the preset influence weight threshold, and determine the number N of the key pipeline nodes; A load value determination module, used to determine, according to the controller model, a calculated load value X of the controller corresponding to the controller model when processing the collected data collected by the single pair of sensors, and a calculated load value Y when estimating the collected data collected by the single pair of pressure sensors, wherein the single pair of sensors is composed of a single pressure sensor and a single flow sensor; A total load value determination module, used to determine the operating load when the controller is at the best performance according to the pre-established performance-operation load curve of the controller, and determine the calculated total load value Z corresponding to the operating load; A calculation module, used for calculating N1 pairs of target sensors and the number of estimated data N2 according to the number N of the key pipeline nodes, the calculated load value X, the calculated load value Y and the calculated load total value Z; The safety monitoring module is used to set the N1 pairs of target sensors in the corresponding key pipeline nodes according to the sorting results of the influence weight values ​​of each section of the pipeline, so as to obtain the collected data in the corresponding key pipeline nodes based on the N1 pairs of target sensors, so as to realize the safety monitoring of the long-distance pipeline.

9. A safety monitoring system for long-distance pipelines, characterized in that: The system includes multiple pairs of sensors, a controller and a cloud server, each pair of sensors includes a pressure sensor and a flow sensor; wherein each pair of sensors is arranged inside a corresponding key pipeline node, and is used to collect data in the corresponding key pipeline node and send it to the controller; The controller is used to perform estimation processing according to the received collected data in each key pipeline node, obtain estimated data, and the estimated data is the predicted collected data of other pipeline nodes in the long-distance pipeline except each key pipeline node, and send each collected data and each estimated data to the cloud server; The cloud server is used to calculate and monitor the pressure and flow of the long-distance pipeline based on the received collected data and estimated data through a pre-trained digital twin model, and determine whether a leakage failure occurs in the long-distance pipeline.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

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