Pipeline network mechanical response data determination method and device, medium and product

Abstract

The invention discloses a method and equipment for determining mechanical response data of a pipeline network, a medium and a product, and belongs to the technical field of pipelines. The method comprises the following steps: acquiring a reference stress data combination of a pipeline network under a reference environment condition, and determining a finite element model corresponding to the pipeline network; solving the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result; determining at least two real-time monitoring positions of the pipeline network, obtaining change data of current stress of each real-time monitoring position relative to stress of each real-time monitoring position under the reference environment condition, and obtaining a stress change data combination; and on the basis of the reference mechanical response data distribution result, determining a mechanical response data distribution result of the finite element model under the stress change data combination, and taking the mechanical response data distribution result as a target mechanical response data distribution result. According to the technical scheme provided by the invention, the real-time stress response data distribution result of the pipeline network can be accurately determined.

Description

Technical Field

[0001] The present invention relates to the field of pipeline technology, and in particular to a method, device, medium and product for determining pipeline network mechanical response data. Background Art

[0002] During operation, station process pipelines are not only affected by factors such as soil settlement, frost heave / thaw settlement, and vibration within the station, but also by loads transferred from inbound and outbound pipelines, particularly uncontrollable factors such as heavy rain, groundwater levels, and off-site construction. The interaction of factors both within and outside the station subject the station process pipelines to a complex stress state for extended periods. When stress concentration reaches the yield limit of the pipe material, the pipeline may fail, deforming or fracturing. The oil and gas transported by oil and gas pipelines are flammable and explosive. If leaked and encounter a fire source or high temperatures, they are likely to cause fires or even explosions, severely impacting the ecological environment, economic assets, and production safety.

[0003] The stress field and the location of the maximum stress in the station process pipeline are constantly changing. The so-called dangerous sections of current stress monitoring are usually determined based on the manual experience of station operation and maintenance, which often leads to the location of the maximum stress being outside the monitoring range.

[0004] Therefore, how to accurately determine the stress data at each location in the station process pipeline network in real time has become an urgent technical problem to be solved. Summary of the Invention

[0005] The present invention provides a method, device, medium and product for determining the mechanical response data of a pipeline network to solve the technical problem that the existing technology cannot accurately determine the stress distribution results of a station process pipeline network in real time.

[0006] According to one aspect of the present invention, a method for determining pipeline network mechanical response data is provided, comprising:

[0007] Obtaining a reference stress data combination of a pipeline network under reference environmental conditions, and determining a finite element model corresponding to the pipeline network, wherein the reference stress data combination includes reference stress data of some branches in the pipeline network, the some branches being branches under at least two target manifolds;

[0008] Solving the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result of the pipeline network;

[0009] Determining at least two real-time monitoring locations of the pipeline network, obtaining change data of the current stress at each of the real-time monitoring locations compared to the stress at each of the real-time monitoring locations under the reference environmental conditions, and obtaining a stress change data combination including all of the change data at the current moment;

[0010] Based on the reference mechanical response data distribution result, the mechanical response data distribution result of the finite element model under the stress change data combination is determined, and the mechanical response data distribution result is used as the target mechanical response data distribution result of the pipeline network at the current moment.

[0011] According to another aspect of the present invention, there is provided a device for determining pipeline network mechanical response data, comprising:

[0012] an acquisition module, configured to acquire a reference stress data combination of a pipeline network under reference environmental conditions and determine a finite element model corresponding to the pipeline network, wherein the reference stress data combination includes reference stress data of some branches in the pipeline network, where the some branches are branches under at least two target manifolds;

[0013] A reference module, configured to solve the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result of the pipeline network;

[0014] a real-time module, configured to determine at least two real-time monitoring locations of the pipeline network, obtain change data of the current stress at each of the real-time monitoring locations compared to the stress at each of the real-time monitoring locations under the reference environmental conditions, and obtain a stress change data combination including all of the change data at the current moment;

[0015] A result module is used to determine the mechanical response data distribution result of the finite element model under the stress change data combination based on the reference mechanical response data distribution result, and use the mechanical response data distribution result as the target mechanical response data distribution result of the pipeline network at the current moment.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the pipeline network mechanical response data determination method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the pipeline network mechanical response data determination method according to any embodiment of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the method for determining pipeline network mechanical response data according to any one of claims 1 to 7.

[0022] The technical solution provided by an embodiment of the present invention determines a reference mechanical response data distribution result corresponding to a reference stress data combination; determines at least two real-time monitoring locations of a pipeline network, and stress change data at each of the at least two real-time monitoring locations; and, based on the reference mechanical response data distribution result, determines a mechanical response data distribution result of a finite element model under the stress change data combination. This mechanical response data distribution result is the target mechanical response data distribution result of the pipeline network at the current moment. Because the reference stress data combination of the pipeline network under reference environmental conditions has high accuracy, the reference mechanical response data distribution result determined based on the reference stress data combination has high accuracy. In this case, determining the target mechanical response data distribution result by combining the reference mechanical response data distribution result with the stress change data combination can ensure high accuracy of the target mechanical response data distribution result, improve the accuracy of the real-time monitoring locations determined by the user, and provide an accurate simulation basis for inverting the real-time stress and displacement fields of each pipeline in the pipeline network. This facilitates operators to efficiently identify and eliminate safety hazards, thereby improving the safety management level of oil and gas stations.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 is a flow chart of a method for determining pipeline network mechanical response data according to an embodiment of the present invention;

[0026] Figure 2is another flow chart of a method for determining pipeline network mechanical response data according to an embodiment of the present invention;

[0027] Figure 3A 2 is a schematic structural diagram of a pipeline network mechanical response data determination device provided by an embodiment of the present invention;

[0028] Figure 3B 2 is a schematic structural diagram of a pipeline network mechanical response data determination device provided by an embodiment of the present invention;

[0029] Figure 4 It is a structural schematic diagram of an electronic device for implementing the pipeline network mechanical response data determination method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "reference", "current", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Figure 1 This is a flow chart of a method for determining mechanical response data of a pipeline network provided by an embodiment of the present invention. This embodiment is applicable to situations where the distribution results of mechanical response data of a pipeline network are determined accurately and in real time. This method can be executed by a device for determining mechanical response data of a pipeline network. The device can be implemented in the form of hardware and / or software. The device can be configured in a processor of an electronic device. Figure 1 As shown, the method includes:

[0033] S110. Obtain a reference stress data combination of the pipeline network under reference environmental conditions, and determine a finite element model corresponding to the pipeline network. The reference stress data combination includes reference stress data of some branches in the pipeline network, where the branches are branches under at least two target manifolds.

[0034] A pipeline network is a system of interconnected pipes used to transport liquids, gases, or other fluids, such as a station process pipeline network.

[0035] A manifold is a device used to centrally divide or combine flows in a piping network. For example, a flow divider distributes the flow from a main pipe to multiple branches, while a flow combiner brings the flow from multiple branches together into a main pipe.

[0036] Create a finite element model corresponding to the pipeline network based on the pipeline network data. This data includes dimensional information for each component of the pipeline network, such as pipe length, diameter, elbow radius, and the dimensions of the buried header after excavation. The finite element model includes node information, element information, and material constitutive information, and can be saved as an inp file.

[0037] Finite element models are mathematical tools used in numerical simulation and engineering analysis. They solve partial differential equations in mechanics by discretizing complex continua into a finite number of simple units. Discretization involves dividing the continuous computational domain into a finite number of small units (such as triangles and quadrilaterals), connected by nodes. Approximate solutions involve approximating the true solution with simple functions within each unit, ultimately forming a global system of equations by assembling the equations for all units.

[0038] The reference stress data combination can be understood as a basic stress data combination, specifically the stress data combination of the pipeline network under reference environmental conditions. This data can be obtained using a highly accurate stress detector, such as an ultrasonic stress detector. For example, the stress detector performs stress testing at three o'clock positions on a predetermined branch pipe stress concentration section. The three o'clock positions are 0:00, 3:00, and 9:00.

[0039] In one embodiment, a user empirically determines the locations of stress concentration sections in a pipeline network. Stress monitoring equipment is then used to obtain the stress at these locations. This stress serves as the reference stress data for these locations. The combination of reference stress data corresponding to all stress concentration sections is the reference stress data combination. This embodiment allows for rapid acquisition of the reference stress data combination, which is highly reliable.

[0040] In one embodiment, reference stress locations on some branches of a pipeline network are automatically determined. Stress data at each reference stress location is then acquired using stress detection equipment. The combination of reference stresses corresponding to all reference stress locations is used as a reference stress data combination. This embodiment can automatically and quickly determine the reference stress data combination. The reference stress locations can be labeled arbitrarily or automatically based on a pre-trained location recognition model.

[0041] S120. Solve the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result of the pipeline network.

[0042] The predetermined input information is input into the predetermined solver, and the mechanical response data distribution result output by the predetermined solver is used as the reference mechanical response data distribution result. The predetermined solver is an existing solver for the finite element model. In the inversion process, the initial parameters refer to the parameters input into the solver for the first time, such as material properties, boundary conditions, load data, etc. The geometric model is used to define the physical shape and size of the pipeline network, which directly affects the mechanical response data distribution result and must be strictly consistent with the geometric characteristics of the actual pipeline network; the meshing information can be understood as the meshing result, and the meshing is used to discretize the set into finite elements for numerical calculation. In one embodiment, considering that the outer diameter of the pipeline is much larger than the wall thickness, a four-node reduced integral shell unit is used to divide the mesh to improve the accuracy of the meshing information.

[0043] The reference mechanical response data distribution result includes reference mechanical response data at each location in the pipeline network, such as reference stress data, reference displacement data, etc.

[0044] S130. Determine at least two real-time monitoring locations of the pipeline network, obtain change data of the current stress of each real-time monitoring location compared with the stress of each real-time monitoring location under reference environmental conditions, and obtain a stress change data combination including all change data at the current moment.

[0045] In one embodiment, the user identifies predetermined branches that meet predetermined monitoring conditions based on the reference mechanical response data distribution, and uses the stress concentration locations within each predetermined branch that meets the predetermined monitoring conditions as real-time monitoring locations. This embodiment improves the accuracy of the user-determined real-time monitoring locations and provides an accurate simulation foundation for inverting the real-time stress and displacement fields of each pipeline in the pipeline network.

[0046] In one embodiment, the branch pipes that meet predetermined monitoring conditions are automatically determined based on the distribution of reference mechanical response data. For example, branch pipes whose stress exceeds a predetermined threshold are considered to meet the predetermined monitoring conditions. The stress concentration locations in the branch pipes that meet the predetermined monitoring conditions are then used as real-time monitoring locations. This embodiment can speed up the determination of real-time monitoring locations.

[0047] It can be understood that in the above two embodiments, the selection of the real-time monitoring position is determined based on the reference mechanical response data distribution results. The manual selection and automatic determination methods will only affect the speed of determining the real-time monitoring position. In the subsequent finite element solution process, the mechanical response data will be optimized as a whole, and the selection method will not have a significant impact on the accuracy of the target mechanical response data distribution results.

[0048] Stress change data, also referred to as stress change data, can be acquired by monitoring sensors installed at various real-time monitoring locations. These sensors can quickly acquire pipeline stress and / or pipeline displacement with high real-time performance. Examples include string-type strain gauges and / or intelligent universal displacement gauges.

[0049] In one embodiment, for each real-time monitoring location, if it is not a reference stress collection location, reference stress data under reference environmental conditions is obtained. Thus, each real-time monitoring location has corresponding reference stress data and real-time collected stress change data. It will be appreciated that the sum of the reference stress data and the real-time collected stress change data for each real-time monitoring location is the current stress data for each real-time monitoring location.

[0050] S140. Based on the reference mechanical response data distribution result, determine the mechanical response data distribution result of the finite element model under the stress change data combination, and use the mechanical response data distribution result as the target mechanical response data distribution result of the pipeline network at the current moment.

[0051] In a pipeline grid, each manifold is connected to a branch. The stress and / or displacement of a simulated manifold selected from the pipeline network will change with the stress and / or displacement of its associated branches. The real-time monitoring locations on these associated branches are the associated real-time monitoring locations for that simulated manifold. Alternatively, the real-time monitoring locations for all manifolds in the grid can be used as the associated real-time monitoring locations for the simulated manifold.

[0052] In one embodiment, it is considered that the pressure of the station pipeline is controllable, that is, the hoop stress of the pipeline is known and has a sufficient safety margin. The remaining external loads are mainly generated by the settlement and slippage of the manifold. Therefore, the displacement load of the simulated manifold is selected as the design variable (unknown load) in the finite element model. A simulated manifold is selected from the pipeline network and the displacement load of the simulated manifold is used as the design variable. The real-time monitoring positions associated with the simulated manifold are used as the associated real-time monitoring positions of the simulated manifold. During the iterative solution of the finite element model, the stress data corresponding to each associated real-time monitoring position of the simulated manifold in the intermediate mechanical response data distribution results generated in the previous iteration is used as the state variable of the design variable in the current iteration. At the same time, the sum of the reference stress data and the change data of each associated real-time monitoring position of the simulated manifold is used as the verification value of the corresponding state variable of the simulated manifold. The intermediate mechanical response data distribution results in the current iteration are determined by minimizing the relative error between the corresponding state variables and the verification value. If the intermediate mechanical response data distribution results meet the predetermined result conditions, the intermediate mechanical response data distribution results are used as the target mechanical response data distribution results of the pipeline network at the current moment.

[0053] Thus, in this embodiment, one simulated manifold corresponds to one design variable, one design variable corresponds to one or more state variables, and each state variable is matched with a calibration value. The relative error can be expressed as T y =|FT imax -FS imax | / |FT imax |, where FT imax is the current stress data at a real-time monitoring location, FS imax It is the stress data at the associated real-time monitoring position in the intermediate mechanical response data distribution result, and the intermediate mechanical response data distribution result is the mechanical response data distribution result generated during the iterative optimization process of the finite element model.

[0054] Specifically, for the first iteration, the stress data corresponding to each associated real-time monitoring location under the simulated manifold, as measured by the reference mechanical response data distribution, is used as the state variable for the design variable corresponding to the simulated manifold. The sum of the reference stress data and stress change data for each associated real-time monitoring location under the simulated manifold is used as the check value for the state variable. For subsequent iterations, the stress data corresponding to each associated real-time monitoring location under the simulated manifold, as measured by the intermediate mechanical response data distribution determined in the previous iteration, is used as the state variable for the design variable corresponding to the simulated manifold. The sum of the reference stress data and stress change data for each associated real-time monitoring location under the simulated manifold is also used as the check value for the state variable. Since the sum of the reference stress data and stress change data is the stress data for the corresponding real-time monitoring location, the check value can be understood as the measured value, while the state variable is a simulated quantity. The optimization goal is to ensure that the state variable approximates the measured value.

[0055] For each iteration, the relative error between the corresponding state variables and the check values is minimized to bring the state variables closer to the check values, resulting in a mechanical response data distribution result. This mechanical response data distribution result serves as the intermediate mechanical response data distribution result for the current iteration. If the intermediate mechanical response data distribution result meets the predetermined result condition, it is used as the target mechanical response data distribution result for the pipeline network at the current moment. Otherwise, the process returns to the state variable determination step until the intermediate mechanical response data distribution result meets the predetermined result condition. The predetermined result condition means that the difference between the stress data at each associated real-time monitoring location in the intermediate mechanical response data distribution result and the current stress data is within a set error range. The current stress data at any associated real-time monitoring location is equal to the sum of its reference stress data and the stress change data at the current moment.

[0056] In one embodiment, if the target mechanical response data distribution includes a risk segment that meets a predetermined risk condition, the risk segment identifier and warning information corresponding to the predetermined risk condition are transmitted to a predetermined terminal. This embodiment allows users to obtain real-time risk information about the pipeline network through the predetermined terminal, allowing them to take timely countermeasures.

[0057] In one embodiment, after the target mechanical response data distribution result is determined, if it is detected that the stress response data and / or displacement response data of any segment in the target mechanical response data distribution result meets a corresponding predetermined risk condition, a risk indicator corresponding to the predetermined risk condition is added to the segment to update the target mechanical response data distribution result; and the updated target mechanical response data distribution result is displayed in a visualization interface. A segment includes one or more cross sections.

[0058] For example, with respect to the target mechanical response data distribution results, if the axial stress of any pipe exceeds 30% of the pipe material's yield strength, a third-level yellow predetermined indicator is added to that pipe; and the target mechanical response data distribution results, including the third-level yellow predetermined indicator, are displayed in the visualization interface. This third-level yellow predetermined indicator indicates that the pipeline has experienced minor stress or deformation and requires close attention. If the axial stress of any pipe exceeds 50% of the pipe material's yield strength, a second-level orange predetermined indicator is added to that pipe; and the target mechanical response data distribution results, including the second-level orange predetermined indicator, are displayed in the visualization interface. This second-level orange predetermined indicator indicates that the pipeline has experienced significant deformation or stress concentration, requiring further comprehensive assessment based on the actual situation to determine whether measures are necessary. If the axial stress of the pipe exceeds 80% of the pipe material's yield strength, a first-level red predetermined indicator is added to that pipe; and the target mechanical response data distribution results, including the first-level red predetermined indicator, are displayed in the visualization interface. This first-level red predetermined indicator indicates that the pipeline has experienced severe stress concentration or deformation, is highly susceptible to accidents, and requires immediate action.

[0059] In one embodiment, after the updated target mechanical response data distribution result is determined, the updated target mechanical response data distribution result is displayed in the form of a three-dimensional cloud map. Among them, the cloud map is a visualization tool for intuitively displaying the target mechanical response data distribution result. It can reflect the numerical value through color gradient, and quickly locate the maximum value, minimum value and gradient change area. It can be understood that displaying the updated target mechanical response data distribution result in the form of a three-dimensional cloud map can give the digital twin model target force response data real-time stress perception ability and displacement perception ability. Because the real-time stress field or displacement field of the pipeline can be displayed in a three-dimensional visualization form, the real-time stress field and displacement field of the entire pipeline network can be dynamically and intuitively observed, realizing real-time visualization of the overall stress and local stress of the pipeline network.

[0060] In one embodiment, based on the target mechanical response data distribution results of the pipeline network within a predetermined time period, the variation pattern of the mechanical response data of the pipeline network is determined; based on the variation pattern of the mechanical response data, mechanical detection content and early warning information for the pipeline network are generated, and the mechanical detection content includes a monitoring period, stress release timing, and stress release position.

[0061] Specifically, the reservation duration can be set according to the specific situation, such as one week, one month, half a year, or even longer.

[0062] Perform statistical analysis on the target mechanical response data distribution results within a predetermined time period to determine the variation pattern of the mechanical response data of the pipeline network, such as the stress variation pattern and displacement (deformation) variation pattern of each pipeline in the pipeline network; then generate mechanical detection content and early warning information for the pipeline network based on the variation pattern of the mechanical response data. Among them, the mechanical detection content includes but is not limited to the monitoring period, stress release timing and stress release position. The early warning information includes but is not limited to pipeline position information, mechanical response data and the corresponding predetermined risk level range. The pipeline position information includes the pipeline identification and the coordinates of the dangerous section. For example, according to the early warning information, it can be known that the pipeline identified as A has a maximum stress of D and a maximum displacement of E within a range of BC meters after the predetermined starting position, and there is a level F stress risk or a level G displacement risk, wherein the stress range corresponding to the level F stress risk is HI Pa and the level G displacement range is JK mm.

[0063] It can be understood that based on the change law of the mechanical response data of the pipeline network, the change law of the mechanical response data of each real-time monitoring position can be determined, especially the stress state of the maximum stress section and its change law. These change laws can facilitate station operators to efficiently identify and eliminate safety hazards.

[0064] The technical solution provided by an embodiment of the present invention determines a reference mechanical response data distribution result corresponding to a reference stress data combination; determines at least two real-time monitoring locations of a pipeline network, and stress change data at each of the at least two real-time monitoring locations; and, based on the reference mechanical response data distribution result, determines a mechanical response data distribution result of a finite element model under the stress change data combination. This mechanical response data distribution result is the target mechanical response data distribution result of the pipeline network at the current moment. Because the reference stress data combination of the pipeline network under reference environmental conditions has high accuracy, the reference mechanical response data distribution result determined based on the reference stress data combination has high accuracy. In this case, determining the target mechanical response data distribution result by combining the reference mechanical response data distribution result with the stress change data combination can ensure high accuracy of the target mechanical response data distribution result, improve the accuracy of the real-time monitoring locations determined by the user, and provide an accurate simulation basis for inverting the real-time stress and displacement fields of each pipeline in the pipeline network. This facilitates operators to efficiently identify and eliminate safety hazards, thereby improving the safety management level of oil and gas stations.

[0065] Figure 2 This is another flow chart of a method for determining mechanical response data of a pipeline network provided by an embodiment of the present invention. This embodiment is used to refine the determination process of the target mechanical response data distribution result in the above embodiment. Figure 2 As shown, the method includes:

[0066] S210. Obtain a reference stress data combination of the pipeline network under reference environmental conditions, and determine a finite element model corresponding to the pipeline network. The reference stress data combination includes reference stress data of some branches in the pipeline network, where the some branches are branches under at least two target manifolds.

[0067] In one embodiment, in response to a predetermined trigger request, a configuration interface is displayed; reference stress data combination storage information, pipeline network storage information, and genetic optimization configuration information received based on the configuration interface are determined; a reference stress data combination for the pipeline network is obtained based on the reference stress data combination storage information, and the pipeline network data and a finite element model corresponding to the pipeline network data are determined based on the pipeline network storage information.

[0068] The reference mechanical response data storage information includes the storage path and file identifier of the reference mechanical response data; the pipeline network data storage information includes the storage path and file identifier of the pipeline network data.

[0069] Genetic optimization configuration information includes, but is not limited to, the number of individuals in the population, the maximum genetic generation number, the crossover probability, and the mutation probability. The population individual data is the number of individuals required for each iteration; the maximum genetic generation number is the maximum number of iterations; crossover refers to the operation of replacing and recombinating parts of the structure of two parent individuals to generate a new individual; and mutation changes the gene values at certain loci in the individual strings of the population.

[0070] S220. Solve the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result of the pipeline network.

[0071] S230. Determine at least two real-time monitoring locations of the pipeline network, obtain change data of the current stress of each real-time monitoring location compared with the stress of each real-time monitoring location under reference environmental conditions, and obtain a stress change data combination including all change data at the current moment.

[0072] S2401. Select a simulated manifold from the pipeline network and use the displacement load of the simulated manifold as a design variable.

[0073] The simulated manifold is the manifold for which the mechanical response data distribution result is to be obtained.

[0074] The displacement load is a boundary condition used to limit the displacement of the manifold in certain directions. This embodiment does not limit the specific content of the displacement load, and the displacement load in this embodiment can be determined using existing displacement load determination methods.

[0075] S2402: The real-time monitoring location associated with the simulated manifold is used as the associated real-time monitoring location under the simulated manifold.

[0076] In one embodiment, all branches that may contribute to the displacement of the simulated manifold are determined, and the real-time monitoring positions in these branches are used as the associated real-time monitoring positions under the simulated manifold.

[0077] In one embodiment, all real-time monitoring positions are used as associated real-time monitoring positions of the simulated manifold. This embodiment can quickly, simply, and directly determine all associated real-time monitoring positions.

[0078] S2403. The finite element model is iteratively optimized and solved by the solver to obtain the reference mechanical response data distribution results of the pipeline network and the target mechanical response data distribution results under the current stress data of each associated real-time monitoring position. The current stress data of each associated real-time monitoring position is equal to the sum of the stress change data of each real-time monitoring position and the reference stress data.

[0079] The solver is configured to determine a current fitness combination based on the current stress data of each associated real-time monitoring position and the stress data corresponding to the intermediate mechanical response data distribution result generated by each associated real-time monitoring position in the previous iteration round, obtain a current load data combination corresponding to the current fitness combination returned by a predetermined optimization program, determine the intermediate mechanical response data distribution result corresponding to the current load data combination in the current iteration round, and update the current fitness combination based on the intermediate mechanical response data distribution result and the current stress data of each associated real-time monitoring position. If the updated current fitness combination does not meet the predetermined fitness condition, the current load data combination corresponding to the updated current fitness combination returned by the predetermined optimization program is obtained again until the updated current fitness combination meets the predetermined fitness condition, and the mechanical response data distribution result corresponding to the updated current fitness combination is used as the target mechanical response data distribution result at the current moment. The predetermined optimization program is configured to determine an optimal population under the current fitness combination based on a genetic algorithm, where each individual in the optimal population corresponds to an optimal load data combination.

[0080] The solver in this embodiment is an existing finite element model solver.

[0081] The predetermined optimization program may use an existing genetic algorithm toolbox or an existing genetic algorithm code, as long as the genetic algorithm toolbox or the existing genetic algorithm code can provide a general framework for solving nonlinear, multi-model, multi-objective and other complex system optimization problems.

[0082] The number of current fitness values included in the current fitness combination is the same as the number of current stress data points included in the current stress data combination. Specifically, the solver calculates the relative error between the stress data at each associated real-time monitoring position in the intermediate mechanical response data distribution results generated in the previous iteration and the current stress data at the current moment. The combination of the relative errors corresponding to all associated real-time monitoring positions is used as the current fitness combination.

[0083] In one embodiment, the execution steps of the predetermined optimization program include: using binary randomness to determine the population, and mapping the determined population to the actual variable space; obtaining the current fitness combination sent by the solver; checking the current fitness and maximum number of iterations corresponding to each individual in the population, if the convergence accuracy is met or the maximum number of iterations is reached, then determining the optimal population, and outputting the current load data corresponding to the optimal population; otherwise, using a random sampling selection algorithm to select chromosomes to obtain the next generation population; using a single-point crossover algorithm to cross chromosomes, that is, exchanging element values in different individuals; using a mutation algorithm to mutate chromosomes, that is, probabilistically mutating element values in individuals; determining the optimal population in the mutation result, and outputting the current load data corresponding to the optimal population.

[0084] The processor transmits the model file including the finite element model to the solver; the solver parses the model file to obtain the finite element model, and determines the relative error between the stress data at each associated real-time monitoring position in the intermediate mechanical response data distribution result generated in the previous iteration round and the current stress data at the current moment, and uses the combination of the relative errors corresponding to all associated real-time monitoring positions as the current fitness combination, and sends the current fitness combination to the predetermined optimization program; the processor controls the predetermined optimization program to obtain genetic configuration information, receive the current fitness combination, and complete the optimization based on the genetic algorithm according to the genetic configuration information and the current fitness combination. The optimization of the current iteration round is completed, the optimal load data in the population generated by the current iteration round is used as the current load data, and the current load data is sent to the solver; the solver performs the solution of the current iteration round of the finite element model based on the current load data, obtains the intermediate mechanical response data distribution result for the current iteration round, calculates the current fitness combination corresponding to the intermediate mechanical response data distribution result, and sends the current fitness combination to the predetermined optimization program; if the current fitness combination meets the predetermined fitness condition, the intermediate mechanical response data distribution result corresponding to the current fitness combination is used as the target mechanical response data distribution result. If the current fitness combination does not meet the predetermined fitness condition, the current fitness combination is sent to a predetermined optimization program, which performs cross-mutation on the basis of the received current fitness combination on the basis of the previous iteration round, completes the optimization of the current iteration round, and sends the optimal load data in the population generated by the current iteration round as the current load data to the solver; the solver executes the solution of the current iteration round of the finite element model based on the current load data, obtains the intermediate mechanical response data distribution result for the current iteration round, determines whether the intermediate mechanical response data distribution result corresponds to the current fitness combination, and determines whether the current fitness combination meets the predetermined fitness condition. If so, the latest intermediate mechanical response data distribution result is used as the target mechanical response data distribution result; otherwise, return to the step of sending the current fitness combination to the predetermined optimization program until the current fitness combination determined based on the received current load data meets the predetermined fitness condition.

[0085] In short, the optimization of load data is completed through mutual calls between the solver and the predetermined optimization program; the relative error between the stress data at each associated real-time monitoring position in the intermediate mechanical response data distribution results generated by the genetic iteration number and the previous iteration round and the current stress data at the current moment is used to determine whether the optimization process of load data is completed, and the corresponding target mechanical response data distribution results are output at the end of the optimization.

[0086] The technical solution provided by the embodiment of the present invention is that the genetic algorithm can jump out of the local optimal solution and find the global optimal solution in complex and nonlinear problems through population search and random mutation. Therefore, the predetermined optimization program completes the optimization of the load data corresponding to the fitness combination based on the genetic algorithm, thereby improving the accuracy of the load data optimization. In this way, the solver solves the finite element model based on the load data with higher accuracy, which can reduce the iterative rounds of the finite element model solution, improve the speed and accuracy of the finite element model solution, and thus improve the accuracy of the determination of the target mechanical response data.

[0087] Figure 3A Schematic diagram of the structure of the pipeline network mechanical response data determination device provided by the embodiment of the present invention. Figure 3A As shown, the device includes:

[0088] An acquisition module 31 is configured to acquire a reference stress data combination of a pipeline network under reference environmental conditions and determine a finite element model corresponding to the pipeline network, wherein the reference stress data combination includes reference stress data of some branches in the pipeline network, where the some branches are branches under at least two target manifolds;

[0089] A reference module 32 is configured to solve the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result of the pipeline network;

[0090] A real-time module 33 is configured to determine at least two real-time monitoring locations of the pipeline network, obtain change data of the current stress at each of the real-time monitoring locations compared to the stress at each of the real-time monitoring locations under the reference environmental conditions, and obtain a stress change data combination including all of the change data at the current moment;

[0091] The result module 34 is used to determine the mechanical response data distribution result of the finite element model under the stress change data combination based on the reference mechanical response data distribution result, and use the mechanical response data distribution result as the target mechanical response data distribution result of the pipeline network at the current moment.

[0092] In one embodiment, Figure 3B As shown, the device further includes a sending module 35, which is used to:

[0093] In a case where the target mechanical response data distribution result includes a risk segment that meets a predetermined risk condition, the risk segment identifier and warning information corresponding to the predetermined risk condition are sent to a predetermined terminal.

[0094] In one embodiment, the real-time module 33 is specifically configured to:

[0095] determining at least two target locations in the pipeline network that meet predetermined risk monitoring conditions based on the reference mechanical response data distribution result;

[0096] The at least two target positions are used as at least two real-time monitoring positions.

[0097] In one embodiment, the real-time module is further configured to:

[0098] When any of the at least two real-time monitoring positions is not a reference mechanical data acquisition position, obtaining reference stress data of the real-time monitoring position under the reference environmental conditions;

[0099] The Results module is used to:

[0100] Selecting a simulated manifold from the pipeline network, and using the displacement load of the simulated manifold as a design variable;

[0101] The real-time monitoring position associated with the simulated manifold is used as the associated real-time monitoring position under the simulated manifold;

[0102] During the iterative solution of the finite element model, the stress data corresponding to each of the associated real-time monitoring positions under the simulated manifold in the intermediate mechanical response data distribution results generated in the previous iteration round is used as the state variable of the design variable in the current iteration round, and the sum of the reference stress data and the change data at each of the associated real-time monitoring positions under the simulated manifold is used as the verification value of the state variable corresponding to the simulated manifold;

[0103] By minimizing the relative error between the state variables and the verification values that have a corresponding relationship, the intermediate mechanical response data distribution result in the current iteration round is determined. If the intermediate mechanical response data distribution result meets the predetermined result condition, the intermediate mechanical response data distribution result is used as the target mechanical response data distribution result of the pipeline network at the current moment.

[0104] In one embodiment, the result module 34 is specifically configured to:

[0105] Selecting a simulated manifold from the pipeline network, and using the displacement load of the simulated manifold as a design variable;

[0106] The real-time monitoring position associated with the simulated manifold is used as the associated real-time monitoring position under the simulated manifold;

[0107] Iteratively optimizing and solving the finite element model using a solver to obtain a target mechanical response data distribution result of the pipeline network under the reference mechanical response data distribution result and the current stress data of each associated real-time monitoring position, wherein the current stress data of each associated real-time monitoring position is equal to the sum of the stress change data of each real-time monitoring position and the reference stress data;

[0108] The solver is configured to determine a current fitness combination based on the current stress data of each associated real-time monitoring position and the stress data corresponding to the intermediate mechanical response data distribution result generated by each associated real-time monitoring position in the previous iteration round, obtain a current load data combination corresponding to the current fitness combination returned by a predetermined optimization program, determine the intermediate mechanical response data distribution result corresponding to the current load data combination in the current iteration round, and update the current fitness combination based on the intermediate mechanical response data distribution result and the current stress data of each associated real-time monitoring position. If the updated current fitness combination does not meet the predetermined fitness condition, the current load data combination corresponding to the updated current fitness combination returned by the predetermined optimization program is obtained again until the updated current fitness combination meets the predetermined fitness condition, and the mechanical response data distribution result corresponding to the updated current fitness combination is used as the target mechanical response data distribution result at the current moment. The predetermined optimization program is configured to determine an optimal population under the current fitness combination based on a genetic algorithm, where each individual in the optimal population corresponds to an optimal load data combination.

[0109] In one embodiment, the acquisition module 31 is used to display a configuration interface in response to a predetermined trigger request;

[0110] In response to a predetermined trigger request, displaying a configuration interface;

[0111] Determining reference stress data combination storage information, pipeline network storage information, and genetic optimization configuration information received based on the configuration interface;

[0112] Acquiring a reference stress data combination for a pipeline network based on the reference stress data combination storage information, and determining the pipeline network data and a finite element model corresponding to the pipeline network data based on the pipeline network storage information;

[0113] The result module is also used to control the predetermined optimization program to determine the configuration data required in the process of optimizing load data based on the genetic algorithm according to the genetic optimization configuration information. The configuration data includes the number of population individuals, the maximum genetic generation, the crossover probability and the mutation probability.

[0114] In one embodiment, the apparatus further comprises a result analysis module, wherein the result analysis module is configured to:

[0115] determining a variation pattern of the mechanical response data of the pipeline network according to a distribution result of the target mechanical response data of the pipeline network within a predetermined time period;

[0116] Mechanical detection content and early warning information for the pipeline network are generated according to the change rule of the mechanical response data. The mechanical detection content includes a monitoring period, a stress release timing, and a stress release position.

[0117] The technical solution provided by an embodiment of the present invention determines a reference mechanical response data distribution result corresponding to a reference stress data combination; determines at least two real-time monitoring locations of a pipeline network, and stress change data at each of the at least two real-time monitoring locations; and, based on the reference mechanical response data distribution result, determines a mechanical response data distribution result of a finite element model under the stress change data combination. This mechanical response data distribution result is the target mechanical response data distribution result of the pipeline network at the current moment. Because the reference stress data combination of the pipeline network under reference environmental conditions has high accuracy, the reference mechanical response data distribution result determined based on the reference stress data combination has high accuracy. In this case, determining the target mechanical response data distribution result by combining the reference mechanical response data distribution result with the stress change data combination can ensure high accuracy of the target mechanical response data distribution result, improve the accuracy of the real-time monitoring locations determined by the user, and provide an accurate simulation basis for inverting the real-time stress and displacement fields of each pipeline in the pipeline network. This facilitates operators to efficiently identify and eliminate safety hazards, thereby improving the safety management level of oil and gas stations.

[0118] The pipeline network mechanical response data determination device provided in the embodiment of the present invention can execute the pipeline network mechanical response data determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0119] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are provided for example only and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0120] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0122] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining pipeline network mechanical response data.

[0123] In some embodiments, the pipeline network mechanical response data determination method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the pipeline network mechanical response data determination method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the pipeline network mechanical response data determination method in any other suitable manner (e.g., via firmware).

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0129] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0130] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the pipeline network mechanical response data determination method provided in any embodiment of the present application.

[0131] The computer program product may be implemented by writing computer program code for performing the operations of the present invention 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 stand-alone 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 via 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., via the Internet using an Internet service provider).

[0132] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0133] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for determining pipeline network mechanical response data, characterized in that: include: Obtaining a reference stress data combination of a pipeline network under reference environmental conditions, and determining a finite element model corresponding to the pipeline network, wherein the reference stress data combination includes reference stress data of some branches in the pipeline network, the some branches being branches under at least two target manifolds; Solving the finite element model based on the reference stress data combination to obtain a reference mechanical response data distribution result of the pipeline network; Determining at least two real-time monitoring locations of the pipeline network, obtaining change data of the current stress at each of the real-time monitoring locations compared to the stress at each of the real-time monitoring locations under the reference environmental conditions, and obtaining a stress change data combination including all of the change data at the current moment; Based on the reference mechanical response data distribution result, the mechanical response data distribution result of the finite element model under the stress change data combination is determined, and the mechanical response data distribution result is used as the target mechanical response data distribution result of the pipeline network at the current moment.

2. The method according to claim 1, characterized in that After the mechanical response data distribution result is used as the target mechanical response data distribution result of the pipeline network at the current moment, the method further includes: In a case where the target mechanical response data distribution result includes a risk segment that meets a predetermined risk condition, the risk segment identifier and warning information corresponding to the predetermined risk condition are sent to a predetermined terminal.

3. The method according to claim 1, characterized in that Determining at least two real-time monitoring locations of the pipeline network includes: determining at least two target locations in the pipeline network that meet predetermined risk monitoring conditions based on the reference mechanical response data distribution result; The at least two target positions are used as at least two real-time monitoring positions.

4. The method according to claim 1, wherein While determining at least two real-time monitoring locations of the pipeline network, the method further includes: When any of the at least two real-time monitoring positions is not a reference mechanical data acquisition position, obtaining reference stress data of the real-time monitoring position under the reference environmental conditions; The step of determining the mechanical response data distribution result of the finite element model under the stress change data combination based on the reference mechanical response data distribution result, and using the mechanical response data distribution result as the target mechanical response data distribution result of the pipeline network at the current moment, includes: Selecting a simulated manifold from the pipeline network, and using the displacement load of the simulated manifold as a design variable; The real-time monitoring position associated with the simulated manifold is used as the associated real-time monitoring position under the simulated manifold; During the iterative solution of the finite element model, the stress data corresponding to each of the associated real-time monitoring positions under the simulated manifold in the intermediate mechanical response data distribution results generated in the previous iteration round is used as the state variable of the design variable in the current iteration round, and the sum of the reference stress data and the change data at each of the associated real-time monitoring positions under the simulated manifold is used as the verification value of the state variable corresponding to the simulated manifold; By minimizing the relative error between the state variables and the verification values that have a corresponding relationship, the intermediate mechanical response data distribution result in the current iteration round is determined. If the intermediate mechanical response data distribution result meets the predetermined result condition, the intermediate mechanical response data distribution result is used as the target mechanical response data distribution result of the pipeline network at the current moment.

5. The method according to claim 1, wherein The step of determining the mechanical response data distribution result of the finite element model under the stress change data combination based on the reference mechanical response data distribution result, and using the mechanical response data distribution result as the target mechanical response data distribution result of the pipeline network at the current moment, includes: Selecting a simulated manifold from the pipeline network, and using the displacement load of the simulated manifold as a design variable; The real-time monitoring position associated with the simulated manifold is used as the associated real-time monitoring position under the simulated manifold; Iteratively optimizing and solving the finite element model using a solver to obtain a target mechanical response data distribution result of the pipeline network under the reference mechanical response data distribution result and a stress data combination, wherein the stress data combination includes current stress data at each of the associated real-time monitoring positions, and the current stress data at each of the associated real-time monitoring positions is equal to the sum of the stress change data at each of the real-time monitoring positions and the reference stress data; The solver is configured to determine a current fitness combination based on the current stress data of each associated real-time monitoring position and the stress data corresponding to the intermediate mechanical response data distribution result generated by each associated real-time monitoring position in the previous iteration round, obtain a current load data combination corresponding to the current fitness combination returned by a predetermined optimization program, determine the intermediate mechanical response data distribution result corresponding to the current load data combination in the current iteration round, and update the current fitness combination based on the intermediate mechanical response data distribution result and the current stress data of each associated real-time monitoring position. If the updated current fitness combination does not meet the predetermined fitness condition, the current load data combination corresponding to the updated current fitness combination returned by the predetermined optimization program is obtained again until the updated current fitness combination meets the predetermined fitness condition, and the mechanical response data distribution result corresponding to the updated current fitness combination is used as the target mechanical response data distribution result at the current moment. The predetermined optimization program is configured to determine an optimal population under the current fitness combination based on a genetic algorithm, where each individual in the optimal population corresponds to an optimal load data combination.

6. The method according to claim 5, characterized in that The step of obtaining a reference stress data combination of a pipeline network under reference environmental conditions and determining a finite element model corresponding to the pipeline network includes: In response to a predetermined trigger request, displaying a configuration interface; Determining reference stress data combination storage information, pipeline network storage information, and genetic optimization configuration information received based on the configuration interface; Acquiring a reference stress data combination for a pipeline network based on the reference stress data combination storage information, and determining the pipeline network data and a finite element model corresponding to the pipeline network data based on the pipeline network storage information; The iterative optimization and solving of the finite element model by a solver to obtain the target mechanical response data distribution result of the pipeline network under the reference mechanical response data distribution result and the stress data combination also includes: The predetermined optimization program is controlled to determine the configuration data required in the process of optimizing the load data based on the genetic algorithm according to the genetic optimization configuration information. The configuration data includes the number of individuals in the population, the maximum genetic generation, the crossover probability and the mutation probability.

7. The method according to claim 1, characterized in that The obtaining of the target mechanical response data distribution result of the pipeline network at the current moment includes: determining a variation pattern of the mechanical response data of the pipeline network according to a distribution result of the target mechanical response data of the pipeline network within a predetermined time period; Mechanical detection content and early warning information for the pipeline network are generated according to the change rule of the mechanical response data, and the mechanical detection content includes a monitoring period, a stress release timing, and a stress release position.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the pipeline network mechanical response data determination method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the pipeline network mechanical response data determination method according to any one of claims 1 to 7 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the method for determining pipeline network mechanical response data according to any one of claims 1 to 7.

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