Safety risk control and decision-making methods for power grid infrastructure projects

Abstract

The present invention discloses a safety risk control and decision-making method for power grid infrastructure projects, which relates to the technical field of safety risk control for infrastructure projects, and includes the following steps: obtaining independent variable data in the process of laying power lines, simulating the laying of power lines based on a computer simulation model through the independent variable data; setting a number of simulated line tensions, obtaining structural impact data and power impact data in the corresponding computer simulation model; constructing a line tension risk assessment model based on a long-term and short-term neural network according to the structural impact data and power impact data genes; drawing a tension-risk curve diagram according to the output of the line tension risk assessment model and the corresponding simulated line tension; performing data analysis on the tension-risk curve diagram to obtain the best simulated line tension, and applying it as a decision tension to the line laying of power grid infrastructure projects. The potential safety hazard problem caused by poor tension control during the laying of power lines is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of infrastructure project safety risk management and control, and more specifically, to a power grid infrastructure project safety risk management and control and decision-making method. Background Art

[0002] In power grid infrastructure projects, tension control during power line installation is a key factor in ensuring that the wires can operate normally after installation. Reasonable tension control ensures that the wires maintain an appropriate bending radius and height during installation, preventing wire breakage, excessive sagging, or contact between wires and other structures due to excessive tension; by accurately calculating and adjusting the tension, overheating, vibration, and physical damage during line operation can be avoided, thereby ensuring the stability and reliability of the power system.

[0003] For example, the invention patent with publication number CN114580957A discloses a method, system and medium for controlling the process of power grid infrastructure based on digital twin technology. The method for controlling the process of power grid infrastructure based on digital twin technology includes collecting real-time three-dimensional feature points of the current construction progress of each equipment building in the target project; comparing the real-time three-dimensional feature points with the three-dimensional feature points of the spatiotemporal sequence digital twin established according to the construction plan of the target project to control the process of infrastructure. The present invention has the advantages of being practical in engineering, low in cost, accurate and reliable, and can control the progress of the power grid infrastructure process, provide timely and accurate early warning information, reduce the labor intensity and safety risks of manual on-site progress verification, and ensure that the power grid infrastructure site can be constructed as planned.

[0004] For example, the invention patent with publication number: CN117787725A discloses a safety risk management method and system based on knowledge graph, which comprises the following steps: Step 1, collecting power grid infrastructure construction safety data involved in infrastructure accident cases; Step 2, indexing the power grid infrastructure construction safety data obtained in Step 1, and fusing different indexes of text, sound, picture and video data through similarity calculation; Step 3, performing entity extraction on the fused data in Step 2 to obtain entity words included in the above multimodal fusion data, and entity relationships between the above entity words; based on the above entity relationships and the above multimodal fusion data, constructing a knowledge graph for safety management of power grid infrastructure construction with multimodal characteristics; Step 4, constructing risk identification indicators based on the knowledge graph obtained in Step 3, identifying risks of power grid infrastructure construction, and implementing safety risk management.

[0005] The above disclosed technical solutions have at least the following technical problems: if the tension of the power line is too high, it may cause the wire to break or cause excessive load on the supporting structure; if the tension is too low, it may cause the wire to sag excessively, increasing the risk of contact with other structures. The shortcomings of the existing technology in tension control are that there may be accuracy deviations in the calculation and adjustment process, which will cause the actual tension to fail to accurately meet the design requirements, thereby affecting the stability and safety of the line.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a safety risk management and decision-making method for power grid infrastructure projects, which solves the safety hazard problem caused by poor tension control during the laying of power lines through safety risk management.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The method for safety risk control and decision-making of power grid infrastructure projects comprises the following steps: obtaining independent variable data in the process of laying power lines, simulating the laying of power lines based on a computer simulation model through the independent variable data; setting a number of simulated line tensions, obtaining structural influence data and power influence data in the corresponding computer simulation model; constructing a line tension risk assessment model based on a long-term and short-term neural network according to the structural influence data and power influence data genes; drawing a tension-risk curve diagram according to the output of the line tension risk assessment model and the corresponding simulated line tension; performing data analysis on the tension-risk curve diagram to obtain the best simulated line tension, and applying it as a decision tension to the line laying of power grid infrastructure projects.

[0010] In a preferred embodiment, the independent variable data includes but is not limited to environmental conditions, line length, material properties, gravity and climate type; the structural influence data includes stress deformation influence coefficient and structure contact hazard coefficient, and the power influence data includes power transmission efficiency influence coefficient.

[0011] In a preferred embodiment, the specific method for obtaining the stress-deformation influence coefficient is as follows: in a computer simulation model, the power line is discretized into a finite number of line elements, the line elements include two line nodes, and the line nodes include two displacement directions; a local coordinate system is defined based on the displacement direction, and the displacement of the line node is defined by a vector; the longitudinal strain of the line node is calculated based on the displacement gradient according to the displacement of the line node; a linear shape function is set according to the local coordinate system, and a strain-displacement matrix is ​​constructed according to the linear shape function combined with the longitudinal strain; the volume of the line element and the constitutive relationship matrix of the line material are obtained, and the elastic coefficient of each line element is calculated based on the strain-displacement matrix; the elastic coefficient of each line element is used as the elastic force, and the corresponding power line tension of the current computer simulation model is used as the pulling force to calculate a number of deformation variables; the ratio of a number of deformation variables to the change in the elastic coefficient of each line element is calculated to obtain the stress-deformation influence coefficient.

[0012] In a preferred embodiment, the specific method for obtaining the contact hazard coefficient of the analyzed structure is as follows: voxelize the power line simulation model, and divide the power line into multiple cubes, label and obtain a voxel set; count the number of voxels whose distance from the contact object is less than a preset safety distance to obtain the contact object volume; identify and analyze the distribution of contact objects in the voxelized power line model, and calculate the curvature of the contact object distribution; combine the curvature of the contact object distribution and the contact object volume to analyze and calculate the structure contact hazard coefficient.

[0013] In a preferred embodiment, the specific method for obtaining the power transmission efficiency influence coefficient is as follows: set each port of the power line as a node and label it, and label the power line; obtain the output and power demand of the node, the voltage of the node, the current and resistance of the power line, and calculate the power transmission efficiency based on the preset power transmission efficiency calculation formula; according to the power transmission efficiency combined with the node, in a preferred embodiment, according to the output of the line tension risk assessment model and the corresponding simulated line tension, draw a tension-risk curve diagram, specifically: obtain the tension risk assessment coefficient of the output under different tension settings from the tension risk assessment model; establish a rectangular coordinate system, mark the horizontal axis as the line tension value, and the vertical axis as the tension risk assessment coefficient; mark the tension risk assessment coefficient corresponding to each tension value on the rectangular coordinate system to form data points; connect the data points to form a tension-risk curve diagram.

[0014] In a preferred embodiment, the data analysis of the tension-risk curve diagram is performed to obtain the optimal simulated line tension, specifically: several valley values ​​of the tension-risk curve are obtained, and the secondary derivative of each valley value is performed to obtain the degree of smoothness of change; the simulated line tension corresponding to the valley value with the degree of smoothness closest to zero is used as the optimal simulated line tension, and it is used as the decision tension for line laying of power grid infrastructure projects.

[0015] Technical effects and advantages of the power grid infrastructure project safety risk management and decision-making method of the present invention:

[0016] 1. The present invention realizes the accurate evaluation of tension control optimization during the laying of power lines by integrating independent variable data and computer simulation models; specifically, the technology simulates the line behavior under different tension conditions, analyzes the stress deformation influence coefficient, the structure contact hidden danger coefficient and the power transmission efficiency influence coefficient, and provides a detailed scientific basis for line design and construction. This technology can accurately identify and optimize tension settings, prevent potential structural failures and contact hidden dangers, improve power transmission efficiency, and thus enhance the stability and safety of the power system; in addition, it also helps reduce maintenance costs, reduce energy loss, and improve the economic benefits of the overall project.

[0017] 2. This invention uses a long short-term memory neural network (LSTM) to build a line tension risk assessment model, which can accurately integrate stress deformation, contact hazards and power transmission efficiency data, thereby providing accurate risk prediction and optimization suggestions. Using the visualization effect of the tension-risk curve, the optimal tension setting can be clearly identified, significantly improving the safety and operating efficiency of power grid infrastructure projects, while reducing potential safety hazards and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the safety risk management and decision-making method for power grid infrastructure projects of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0020] Embodiment 1, Figure 1 The present invention provides a safety risk management and decision-making method for power grid infrastructure projects, including the following steps:

[0021] S1, obtaining independent variable data in the process of laying power lines, and simulating the laying of power lines based on a computer simulation model through the independent variable data.

[0022] In this embodiment, the independent variable data includes but is not limited to environmental conditions, line length, material properties, gravity, and climate type.

[0023] It should be noted that the independent variable data in the power line laying simulation include environmental conditions, line length, material properties, gravity and climate type. Environmental conditions such as wind speed and temperature affect the tension and stability of the line; line length is directly related to the tension distribution and overall load; material properties determine the elasticity and strength of the line; gravity affects the sag of the conductor; and climate type affects material aging and conductor performance in long-term operation; the combined effect of these independent variable data determines the tension adjustment requirements in the design and actual application of power lines.

[0024] In this embodiment, the power line laying is simulated based on the computer simulation model through the independent variable data, specifically:

[0025] Computer simulation model is a technology that uses computer technology to create a virtual environment to simulate and analyze actual systems; by converting the physical, chemical and engineering characteristics of the real world into mathematical equations and algorithms, computer simulation models are able to run a large number of calculations in a virtual environment, simulate the behavior of the system under different conditions, and provide detailed analysis results. This technology is widely used in engineering design, scientific research and optimization of complex systems, helping to predict and evaluate the performance and stability of the system without actual operation.

[0026] Furthermore, the computer simulation model is used to simulate the laying of power lines, specifically including the following aspects:

[0027] Model building: Construct a mathematical model to simulate the physical characteristics and behavior of the power line; the model creates a virtual representation of the power line, including the geometry of the conductors, material properties, and external loads, based on input independent variable data (such as environmental conditions, line length, material properties, gravity, and climate type);

[0028] Tension calculation: The model uses tension calculation formulas and physical equations to predict line performance under different tension settings; this includes considering the elastic modulus, elongation characteristics of the conductor, and the effects of gravity;

[0029] Environmental impact simulation: Integrate climate types and environmental conditions into the model to simulate line behavior in different environments; for example, simulate the impact of wind speed and temperature changes on line tension and stability;

[0030] Simulation run: Run simulations to generate data on line behavior by setting different line tension values; the simulations take into account line deformation, stress distribution, and power loss under different tension conditions;

[0031] Visualization and Optimization: Simulation results are presented through charts and visualization tools to help identify the best line tension settings; these visualization tools can help engineers understand the model predictions and make further optimizations;

[0032] Computer simulation models convert the physical characteristics and environmental impacts of actual situations into virtual models, allowing the laying process of power lines to be predicted and optimized without on-site construction, providing data support for scientific decision-making. I will not go into details here.

[0033] S2, setting a number of simulated line tensions, and obtaining structural impact data and power impact data in the corresponding computer simulation model.

[0034] In this embodiment, the structural influence data includes a stress deformation influence coefficient and a structure contact hazard coefficient, and the power influence data includes a power transmission efficiency influence coefficient.

[0035] Furthermore, the stress-deformation influence coefficient is an important parameter calculated through finite element analysis, which reflects the stress distribution and deformation degree of power lines under different tension conditions. This coefficient is used to evaluate the stress concentration and deformation of the line during actual laying to identify potential structural hazards. By analyzing these coefficients, it is possible to find problems of excessive stress or excessive deformation caused by poor tension control, thereby optimizing the tension setting and avoiding possible structural failures and safety hazards.

[0036] Specifically, analyzing the stress-deformation influence coefficient has the following advantages for assessing the potential safety hazards caused by poor tension control during the laying of power lines:

[0037] Accurate stress analysis: The stress-deformation influence coefficient calculated by finite element analysis can reveal in detail the stress distribution and deformation of the line under different tensions, thereby accurately identifying potential stress concentration areas;

[0038] Early failure warning: It can identify possible structural problems during the design stage and before construction, and provide early warning and solve potential structural failures caused by improper tension control, thereby improving the overall safety of the system;

[0039] Optimize design parameters: Based on the analysis results, the line tension setting can be optimized to ensure that the stress and deformation of the line are within a safe range under various load conditions, avoiding structural problems caused by excessively high or low tension;

[0040] Improve structural stability: By understanding and controlling the stress-deformation influence coefficient, the structural deformation caused by uneven tension of the line can be reduced, and the stability and long-term reliability of the power line can be improved;

[0041] Reduce maintenance costs: Reduce structural damage and failures caused by improper tension, thereby reducing subsequent maintenance and repair costs and reducing the total cost of the entire project life cycle;

[0042] Enhanced safety: Analyzing the stress-deformation influence coefficient helps to formulate safer construction and operation standards, ensuring that the line will not cause safety accidents due to stress concentration or excessive deformation in actual use;

[0043] Adapt to environmental changes: Provide insights into line performance under different environmental conditions, such as the impact of wind and temperature changes on the line, so as to achieve dynamic adjustment and optimize the design to adapt to the actual operating environment;

[0044] Support decision-making: Provide scientific basis for engineers and decision-makers to help formulate reasonable construction and operation strategies and improve the overall efficiency and safety of power line laying;

[0045] In summary, analyzing the stress-deformation influence coefficient can effectively evaluate the safety hazards caused by poor tension control during the laying of power lines.

[0046] Furthermore, the specific method for obtaining the stress-strain influence coefficient is as follows:

[0047] In the computer simulation model, the power line is discretized into a finite number of line elements, wherein the line element includes two line nodes, and the line node includes two displacement directions;

[0048] Define the local coordinate system based on the displacement direction and use vectors to define the displacement of the line nodes;

[0049] Calculating the longitudinal strain of the line node based on the displacement gradient according to the displacement of the line node;

[0050] A linear shape function is set according to the local coordinate system, and a strain-displacement matrix is ​​constructed according to the linear shape function combined with the longitudinal strain;

[0051] Obtain the volume of the line element and the constitutive relationship matrix of the line material, and calculate the elastic coefficient of each line element based on the strain-displacement matrix;

[0052] The elastic coefficient of each line element is used as elastic force, and the corresponding power line tension of the current computer simulation model is used as tensile force to calculate several deformation variables;

[0053] The ratio of several deformation variables to the change in the elastic coefficient of each line element is calculated to obtain the stress deformation influence coefficient.

[0054] The specific calculation formula of the displacement of the line node is as follows:

[0055]

[0056] The specific calculation formula of the longitudinal strain is as follows:

[0057]

[0058] The linear shape function is specifically:

[0059]

[0060] The specific calculation formula of the strain-displacement matrix is ​​as follows:

[0061]

[0062] The specific calculation formula of the elastic coefficient is as follows:

[0063]

[0064] The specific calculation formula of the deformation variable is as follows:

[0065]

[0066] The specific calculation formula of the stress-strain influence coefficient is as follows:

[0067]

[0068] In the formula, is the displacement of the line node, and is the displacement of the two nodes in the x direction, and is the displacement of the two nodes in the y direction, is the longitudinal strain, is the displacement of the node in the direction, i is 1, 2, 3, 4, and is a linear shape function, is the length of the line element, is the strain-displacement matrix, is the elastic coefficient of the jth line element, is the volume of the jth line element, is the constitutive relation matrix of the circuit material, is the deformation of the jth line element, is the power line tension, is the stress-deformation influence coefficient, is the maximum deformation of the line element, is the minimum deformation of the line element, is the change in elastic coefficient.

[0069] It should be noted that the constitutive relationship matrix of the circuit material is a mathematical tool used to describe the relationship between the stress and strain of the material under the action of external force. It is based on the elastic properties and stress-strain behavior of the material, and converts the strain field into the stress field through the constitutive matrix. In finite element analysis, the constitutive relationship matrix reflects the elastic modulus, Poisson's ratio and other parameters of the material, usually expressed in matrix form, which can accurately describe the deformation response of the material under stress.

[0070] Furthermore, the structural contact hazard coefficient is used to evaluate the contact status and potential hazards between power lines and surrounding structures (such as support poles, insulators, etc.); this coefficient measures the pressure distribution and contact quality of the line in contact with the structure under different tension conditions, helping to identify uneven contact or excessive wear problems that may be caused by poor tension control; by optimizing tension settings and improving designs, contact hazards can be reduced, thereby reducing the risk of structural damage or safety accidents.

[0071] Furthermore, identification of contact problems: the contact pressure and distribution between the line and the supporting structure can be accurately identified to find potential poor contact or excessive wear problems;

[0072] Optimize tension setting: Provide a basis for optimizing line tension, ensure that the contact pressure under various load conditions remains within a safe range, and reduce contact risks caused by improper tension;

[0073] Improve safety: By preventing and resolving contact hazards, reduce friction and wear between lines and structures, and reduce the risk of structural failures and safety accidents;

[0074] Improve maintenance plans: Help develop more effective maintenance and inspection plans, timely intervene in potential contact hazards, and extend the service life of lines and structures;

[0075] Enhance design reliability: Provide data support for design, ensure the stability and reliability of the line under actual operating conditions, and improve the safety and performance of the overall system;

[0076] In summary, analyzing the structure contact hazard coefficient can effectively evaluate the safety hazards caused by poor tension control during the laying of power lines.

[0077] The specific method for obtaining the contact hazard coefficient of the analysis structure is as follows:

[0078] The power line simulation model is voxelized, the power line is divided into multiple cubes, and the voxel sets are labeled and obtained; the number of voxels whose distance to the contact object is less than the preset safety distance is counted to obtain the contact object volume; the distribution of the contact object is identified and analyzed in the voxelized power line model, and the curvature of the contact object distribution is calculated; the contact hazard coefficient of the structure is calculated by combining the curvature of the contact object distribution and the contact object volume analysis;

[0079] The calculation formula of the contact volume is:

[0080]

[0081] The calculation formula of the curvature of the contact distribution is:

[0082]

[0083] The calculation formula of the structure contact hazard coefficient is:

[0084]

[0085] In the formula, is the structure contact hazard coefficient, is the total number of voxels in the voxel set, and c is the voxel index; is the volume of the cth voxel, is the average contact distance of the cth contact object distribution, and e is a natural number.

[0086] Furthermore, the power transmission efficiency impact coefficient is used to evaluate the power transmission efficiency and energy loss of power lines under different tension conditions; it quantifies the impact of line impedance changes on power transmission due to poor tension control, including resistance increase and power loss. By analyzing this coefficient, the tension setting can be optimized to ensure that the power line can transmit electricity efficiently while reducing energy loss and system failures caused by improper tension, thereby improving the stability and reliability of the overall power system.

[0087] Specifically, analyzing the influence coefficient of power transmission efficiency has the following advantages for evaluating the potential safety hazards caused by poor tension control during the laying of power lines:

[0088] Identify efficiency losses: Can reveal increased line impedance due to improper tension control, thereby identifying efficiency losses in power transmission and helping predict possible energy waste;

[0089] Optimize tension setting: Provide a basis for adjusting line tension to ensure optimal power transmission efficiency and reduce power loss and system burden caused by excessively high or low tension;

[0090] Improve system stability: Improve transmission efficiency and reduce power loss by optimizing tension, thereby improving the stability and operational reliability of the overall power system;

[0091] Reduce operating costs: reduce energy loss in power transmission, reduce the operating costs and maintenance costs of power companies, and improve economic benefits;

[0092] Preventing system failures: By monitoring and optimizing power transmission efficiency, the risk of overload and equipment failure caused by power loss is reduced, thereby enhancing system safety;

[0093] In summary, analyzing the influence coefficient of power transmission efficiency can effectively evaluate the safety hazards caused by poor tension control during the laying of power lines.

[0094] Furthermore, the specific method for obtaining the power transmission efficiency influence coefficient is as follows:

[0095] Each port of the power line is set as a node and numbered, and the power line is numbered;

[0096] Obtain the output and power demand of the node, the voltage of the node, the current and resistance of the power line, and calculate the power transmission efficiency based on the preset power transmission efficiency calculation formula;

[0097] The power transmission efficiency influence coefficient is calculated based on the power transmission efficiency and the topological structure between nodes.

[0098] The specific calculation formula of the power transmission efficiency is as follows:

[0099]

[0100] Calculate the power transmission efficiency influence coefficient. The specific calculation method is as follows:

[0101]

[0102] In the formula, is the influence coefficient of power transmission efficiency, is the power transmission efficiency at time x during G time, The preset standard power transmission efficiency; For power transmission efficiency, is the output of the node, is the voltage at the power line port, k is the node number, , is the total number of nodes, is the current of the power line, is the resistance of the power line; x is the number of the power line, , is the total number of power lines.

[0103] This embodiment achieves accurate evaluation of tension control optimization during power line laying by integrating independent variable data and computer simulation models; specifically, the technology simulates line behavior under different tension conditions, analyzes stress deformation influence coefficient, structure contact hidden danger coefficient and power transmission efficiency influence coefficient, and provides a detailed scientific basis for line design and construction. This technology can accurately identify and optimize tension settings, prevent potential structural failures and contact hidden dangers, improve power transmission efficiency, and thus enhance the stability and safety of the power system; in addition, it also helps reduce maintenance costs, reduce energy loss, and improve the economic benefits of the overall project.

[0104] Example 2, S3, constructs a line tension risk assessment model based on long-term and short-term neural networks according to the structural impact data and power impact data genes.

[0105] The line tension risk assessment model is constructed based on the long-term and short-term neural network according to the structural impact data and the power impact data, specifically:

[0106] The obtained stress deformation influence coefficient, structure contact hidden danger coefficient and power transmission efficiency influence coefficient are used to construct a line tension risk assessment model to generate a tension risk assessment coefficient;

[0107] The specific calculation formula of the tension risk assessment coefficient is as follows:

[0108]

[0109] In the formula, is the tension risk assessment coefficient, is the preset proportionality coefficient of the stress-strain influence coefficient, is the preset proportional coefficient of the structure contact hazard coefficient, is the preset proportional coefficient of the power transmission efficiency influence coefficient, is the stress-deformation influence coefficient, is the preset proportional coefficient of the structure contact hazard coefficient, is the power transmission efficiency influence coefficient.

[0110] S4, drawing a tension-risk curve diagram according to the output of the line tension risk assessment model and the corresponding simulated line tension.

[0111] The tension-risk curve diagram is drawn according to the output of the line tension risk assessment model and the corresponding simulated line tension, specifically:

[0112] Obtaining tension risk assessment coefficients of outputs under different tension settings from a tension risk assessment model;

[0113] A rectangular coordinate system is established, with the horizontal axis indicated as the line tension value and the vertical axis indicated as the tension risk assessment coefficient;

[0114] Mark the tension risk assessment coefficient corresponding to each tension value on the rectangular coordinate system to form a data point;

[0115] Connect the data points to form a tension-risk curve.

[0116] S5, performs data analysis on the tension-risk curve diagram to obtain the optimal simulated line tension, which is used as the decision tension for line laying of power grid infrastructure projects.

[0117] The data analysis of the tension-risk curve diagram is performed to obtain the optimal simulated line tension, specifically:

[0118] Obtain several valley values ​​of the tension-risk curve, perform secondary derivatives on each valley value, and obtain the degree of smoothness of change. The simulated line tension corresponding to the valley value with the smoothness of change closest to zero is taken as the optimal simulated line tension, which is used as the decision tension for line laying of power grid infrastructure projects.

[0119] This embodiment uses a long short-term memory neural network (LSTM) to build a line tension risk assessment model, which can accurately integrate stress deformation, contact hazards and power transmission efficiency data to provide accurate risk prediction and optimization suggestions. Using the visualization effect of the tension-risk curve graph, the optimal tension setting can be clearly identified, significantly improving the safety and operating efficiency of power grid infrastructure projects, while reducing potential safety hazards and operating costs.

[0120] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0121] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0122] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0124] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0125] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A safety risk management and decision-making method for power grid infrastructure projects, characterized in that: The steps include: Acquire independent variable data in the process of laying power lines, and simulate the laying of power lines based on a computer simulation model using the independent variable data; Setting several simulated line tensions and obtaining structural impact data and power impact data in the corresponding computer simulation model; The structural impact data include stress deformation impact coefficient and structure contact hidden danger coefficient, and the power impact data include power transmission efficiency impact coefficient; The specific calculation formula of the stress-strain influence coefficient is as follows: In the formula, is the stress-deformation influence coefficient, is the maximum deformation of the line element, is the minimum deformation of the line element, is the change of elastic coefficient; The specific method for obtaining the structure contact hazard coefficient is as follows: voxelize the power line simulation model, divide the power line into multiple cubes, label them and obtain a voxel set; The volume of the contact object is obtained by counting the number of voxels whose distance from the contact object is less than the preset safety distance; Identify and analyze the distribution of contacts in the voxelized power line model and calculate the curvature of the contact distribution; Combine the curvature of the contact distribution and the contact volume to analyze and calculate the contact hazard coefficient of the structure; The calculation formula of the structure contact hazard coefficient is: In the formula, is the structure contact hazard coefficient, is the total number of voxels in the voxel set, and c is the voxel index; is the volume of the cth voxel, is the average contact distance of the cth contact object distribution, e is a natural number, is the contact volume, is the curvature of the contact distribution; Calculate the power transmission efficiency influence coefficient. The specific calculation method is as follows: In the formula, is the power transmission efficiency influence coefficient, is the power transmission efficiency at time x during G time, is the preset standard power transmission efficiency; x is the power line number, , is the total number of power lines; According to the structure impact data and power impact data genes, a line tension risk assessment model is constructed based on long-term and short-term neural networks; The line tension risk assessment model is as follows: In the formula, is the tension risk assessment coefficient, is the preset proportionality coefficient of the stress-strain influence coefficient, is the preset proportional coefficient of the structure contact hazard coefficient, is a preset proportionality coefficient of the power transmission efficiency influence coefficient; Draw a tension-risk curve diagram based on the output of the line tension risk assessment model and the corresponding simulated line tension; Data analysis is performed on the tension-risk curve diagram to obtain the optimal simulated line tension, which is used as the decision-making tension for line laying in power grid infrastructure projects.

2. The method for safety risk management and decision-making of power grid infrastructure projects according to claim 1 is characterized in that: The independent variable data include, but are not limited to, environmental conditions, line length, material properties, gravity, and climate type.

3. The method for safety risk management and decision-making of power grid infrastructure projects according to claim 2 is characterized in that: The specific method for obtaining the stress-deformation influence coefficient is as follows: In the computer simulation model, the power line is discretized into a finite number of line elements, wherein the line element includes two line nodes, and the line node includes two displacement directions; Define the local coordinate system based on the displacement direction and use vectors to define the displacement of the line nodes; Calculating the longitudinal strain of the line node based on the displacement gradient according to the displacement of the line node; A linear shape function is set according to the local coordinate system, and a strain-displacement matrix is ​​constructed according to the linear shape function combined with the longitudinal strain; Obtain the volume of the line element and the constitutive relationship matrix of the line material, and calculate the elastic coefficient of each line element based on the strain-displacement matrix; The elastic coefficient of each line element is used as elastic force, and the corresponding power line tension of the current computer simulation model is used as tensile force to calculate several deformation variables; The ratio of several deformation variables to the change in the elastic coefficient of each line element is calculated to obtain the stress deformation influence coefficient.

4. The method for safety risk management and decision-making of power grid infrastructure projects according to claim 3 is characterized in that: The specific method for obtaining the power transmission efficiency influence coefficient is as follows: Each port of the power line is set as a node and numbered, and the power line is numbered; Obtain the output and power demand of the node, the voltage of the node, the current and resistance of the power line, and calculate the power transmission efficiency based on the preset power transmission efficiency calculation formula; The power transmission efficiency influence coefficient is calculated based on the power transmission efficiency and the topological structure between nodes.

5. The method for safety risk management and decision-making of power grid infrastructure projects according to claim 4 is characterized in that: The tension-risk curve diagram is drawn according to the output of the line tension risk assessment model and the corresponding simulated line tension, specifically: Obtaining tension risk assessment coefficients of outputs under different tension settings from a tension risk assessment model; A rectangular coordinate system is established, with the horizontal axis indicated as the line tension value and the vertical axis indicated as the tension risk assessment coefficient; Mark the tension risk assessment coefficient corresponding to each tension value on the rectangular coordinate system to form a data point; Connect the data points to form a tension-risk curve.

6. The method for safety risk control and decision-making of power grid infrastructure projects according to claim 5 is characterized in that: The data analysis of the tension-risk curve diagram is performed to obtain the optimal simulated line tension, specifically: Obtain several valley values ​​of the tension-risk curve, perform secondary derivatives on each valley value, and obtain the degree of smoothness of change; The simulated line tension corresponding to the valley value with the smoothest variation close to zero is taken as the optimal simulated line tension, and is used as the decision tension for line laying of power grid infrastructure projects.

7. The method for safety risk management and decision-making of power grid infrastructure projects according to claim 6 is characterized in that: The specific calculation formula of the displacement of the line node is as follows: The specific calculation formula of the longitudinal strain is as follows: The linear shape function is specifically: The specific calculation formula of the strain-displacement matrix is ​​as follows: The specific calculation formula of the elastic coefficient is as follows: The specific calculation formula of the deformation variable is as follows: In the formula, is the displacement of the line node, and is the displacement of the two nodes in the x direction, and is the displacement of the two nodes in the y direction, is the longitudinal strain, is the displacement of the node in the direction, i is 1, 2, 3, 4, and is a linear shape function, is the length of the line element, is the strain-displacement matrix, is the elastic coefficient of the jth line element, is the volume of the jth line element, is the constitutive relation matrix of the circuit material, is the deformation of the jth line element, The tension of the power lines.

8. The method for safety risk control and decision-making of power grid infrastructure projects according to claim 7 is characterized in that: The specific calculation formula of the power transmission efficiency is as follows: In the formula, is the output of the node, is the voltage at the power line port, k is the node number, , is the total number of nodes, is the current of the power line, is the resistance of the power line.

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