A method, device, medium and product for optimizing the layout of a power transmission tower optical fiber sensor

Abstract

The application discloses a power transmission tower optical fiber sensor optimization layout method, equipment, medium and product, relates to the technical fields of power transmission tower structure health monitoring, optical fiber sensing monitoring, geological disaster risk identification and sensor optimization layout, and the method comprises the following steps: acquiring basic parameters of a tower to which an optical fiber sensor is to be laid; determining a tower risk state variable based on the basic parameters, and constructing a global risk working condition set; determining a candidate measuring point set based on key parts of the tower; constructing a power transmission tower structure finite element analysis model, solving strain response data corresponding to all candidate measuring points based on load parameters corresponding to the global risk working condition set, and constructing a strain response matrix; calculating comprehensive observability evaluation values of all candidate measuring points; determining a preliminary layout scheme based on the comprehensive observability evaluation values; checking the preliminary layout scheme, and obtaining an optimal layout scheme. The application can improve the tower structure risk monitoring precision and system operation reliability under a complex geological environment.

Description

Technical Field

[0001] This application relates to the fields of structural health monitoring of power transmission towers, fiber optic sensing monitoring, geological disaster risk identification, and optimized sensor deployment, and in particular to a method, equipment, medium, and product for optimized deployment of fiber optic sensors on power transmission towers. Background Technology

[0002] Transmission towers are often located in complex geological environments such as mountainous areas, mining subsidence areas, and slope areas. Mining subsidence can cause vertical settlement and differential deformation of the tower foundation, while landslides can cause horizontal sliding and traction deformation of the foundation. When the displacement of the supports is transferred to the tower structure, additional internal forces will be generated in the main members, diagonal members, cross members, and local connection areas of the tower legs. In severe cases, component yielding, local buckling, tower tilting, or even overall instability may occur.

[0003] Existing methods for monitoring transmission towers mostly employ inclinometers, displacement gauges, settlement monitoring devices, BeiDou positioning devices, video inspections, drone inspections, or a small number of strain sensors. While these methods can reflect changes in the tower's geometric posture, appearance defects, or local strain changes in members, they struggle to answer questions such as "which measuring points best characterize the tower's damage risk," "which measuring points can distinguish between settlement, slippage, conductor load changes, and member damage," and "how to deploy measuring points to cover the main risk modes when the number of points is limited."

[0004] Fiber optic sensors offer advantages such as resistance to electromagnetic interference, high accuracy, ease of multi-point networking, and suitability for long-term online monitoring, making them suitable for monitoring the service status of transmission towers. However, transmission towers are spatial pole structures with numerous members, complex nodes, and diverse stress paths. If fiber optic sensors are deployed solely based on local maximum strain, construction convenience, or empirical locations, it can easily lead to problems such as redundant measurement points, insufficient risk coverage, incomplete temperature compensation, and difficulty in distinguishing different sources of risk.

[0005] Therefore, there is an urgent need for an optimized deployment method for fiber optic sensors on power transmission towers based on risk observability. Summary of the Invention

[0006] The purpose of this application is to provide a method, equipment, medium and product for optimizing the deployment of fiber optic sensors on power transmission towers, which can improve the accuracy of tower structure risk monitoring and the reliability of system operation in complex geological environments.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for optimizing the deployment of fiber optic sensors on power transmission towers, including: Obtain the basic parameters of the fiber optic sensor tower to be deployed; the basic parameters include: structural parameters, load parameters, and environmental parameters.

[0008] Based on the aforementioned basic parameters, the risk state variables of the towers are determined, and a global risk condition set is constructed based on the aforementioned risk state variables.

[0009] Based on the key parts of the tower, a set of candidate measurement points is determined.

[0010] A finite element analysis model of the transmission tower structure is constructed. Based on the global risk case set, corresponding load parameters are applied to the finite element analysis model, and the strain response data corresponding to all candidate measurement points are solved to construct the strain response matrix.

[0011] Based on the strain response matrix, the comprehensive observability evaluation value of each candidate measuring point is calculated.

[0012] A preliminary deployment plan is determined based on the comprehensive observability evaluation value.

[0013] The preliminary deployment plan was verified to obtain the optimal deployment plan.

[0014] Optionally, based on the basic parameters, tower risk state variables are determined, and a global risk condition set is constructed based on the tower risk state variables, specifically including: Based on the aforementioned basic parameters, the risk state variables of the tower are determined; the risk state variables include: uneven settlement of the tower foot, horizontal slippage of the tower foot, degradation coefficient of the three-dimensional constraint stiffness of the foundation, lateral pushing force of the landslide, unbalanced tension of the conductor, and stiffness reduction coefficient of the tower members.

[0015] Based on the aforementioned risk state variables, the following conditions are set: single-support vertical settlement displacement condition, single-support horizontal sliding displacement condition, double-support vertical settlement displacement condition, double-support horizontal sliding displacement condition, three-support vertical settlement displacement condition, three-support horizontal sliding displacement condition, four-support differential displacement condition, four-support oblique sliding condition, and four-support settlement-sliding coupling condition.

[0016] Optionally, the key components of the tower include: the upper main material of the tower foot, the lower diagonal material, the variable cross-section node of the tower body, the root of the crossarm, the conductor hanging point pole, and the tower legs on the disaster-facing and disaster-repelling sides.

[0017] Optionally, based on the strain response matrix, a comprehensive observability evaluation value for each candidate measurement point is calculated, specifically including: Based on the strain response matrix, a comprehensive evaluation index of risk observability for each candidate measuring point is calculated; the comprehensive evaluation index includes: risk sensitivity, risk pattern differentiation, key area coverage, measuring point redundancy, and engineering reliability.

[0018] The comprehensive evaluation index is normalized to obtain the normalized index.

[0019] The normalized indicators are weighted and summed to obtain the comprehensive observability evaluation value of each candidate measurement point.

[0020] Optionally, a preliminary deployment plan is determined based on the comprehensive observability evaluation value, specifically including: Establish mandatory deployment constraint rules; the mandatory deployment constraint rules include: at least one measuring point is deployed at each of the four tower legs to ensure full coverage of basic risks; symmetrical comparative measuring points are deployed in pairs on the disaster-facing and disaster-repelling tower legs; no more than two measuring points of the same type are on the same member; any risk condition is covered by at least two measuring points, establishing dual measuring point redundancy constraints.

[0021] Candidate test points that satisfy the mandatory layout constraint rules are selected as the first candidate test points.

[0022] Several second candidate measurement points are selected from high to low based on the comprehensive observability evaluation value corresponding to the first candidate measurement point.

[0023] Calculate the safety reserve coefficient for each of the second candidate measurement points.

[0024] The second candidate test point whose safety reserve coefficient is less than the preset safety reserve coefficient is selected as the third candidate test point.

[0025] A preliminary deployment plan is determined based on the third candidate measurement point.

[0026] Optionally, the formula for calculating the safety reserve factor is: ; in, The safety reserve coefficient for the location of each candidate measuring point; The critical support displacement of the area where the candidate measuring point is located under the corresponding risk condition; For the predicted or monitored displacement of disaster support.

[0027] Optionally, the preliminary deployment scheme is verified to obtain the optimal deployment scheme, specifically including: The preliminary deployment scheme is verified for risk condition coverage, key area coverage, measurement point redundancy, single-point failure tolerance, and temperature compensation integrity. The risk condition coverage verification checks whether the selected measurement points cover all risk condition types. The key area coverage verification checks whether measurement points cover key areas such as the tower foot area, lower tower section, middle tower section, upper tower section, and crossarm area. The measurement point redundancy verification checks whether the average correlation coefficient between the selected measurement points is lower than a set threshold; if it is higher, there is information redundancy, and some measurement points need to be removed. The single-point failure tolerance verification checks whether other measurement points still cover the same risk condition after any measurement point fails. The temperature compensation integrity verification checks whether the temperature compensation measurement points cover the ambient temperature range of all strain measurement points.

[0028] If any check fails, return to the "Determine the preliminary deployment scheme based on the comprehensive observability evaluation value" step until all checks pass and the optimal deployment scheme is obtained.

[0029] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for optimizing the deployment of fiber optic sensors on power transmission towers.

[0030] Thirdly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for optimizing the deployment of fiber optic sensors on power transmission towers.

[0031] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for optimizing the deployment of fiber optic sensors on power transmission towers.

[0032] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, equipment, medium, and product for optimizing the deployment of fiber optic sensors on power transmission towers. By acquiring the structural, load, and environmental parameters of the tower to be deployed with fiber optic sensors, it can comprehensively and accurately grasp the structural characteristics, external stress conditions, and on-site operating environment of the power transmission tower, providing complete, accurate, and reliable basic data support for subsequent risk variable determination, working condition construction, measurement point selection, and simulation analysis. Based on the basic parameters, the risk state variables of the tower are determined, and a full-domain risk working condition set is constructed based on these variables. This comprehensively covers various potential fault and disaster risk scenarios such as foundation deformation, abnormal stress, and tower component damage, breaking through the limitations of traditional single-working-condition analysis and achieving full-domain and multi-dimensional coverage of tower operation risks. Based on key parts of the tower, a candidate measurement point set is determined, accurately locating the core key areas of the power transmission tower where stress is concentrated, damage is easily caused, and risks are high, eliminating invalid and redundant non-critical measurement point locations, effectively reducing the data calculation volume of subsequent simulation analysis and lowering algorithm computation costs. A finite element analysis model of the transmission tower structure is constructed. Based on the comprehensive risk condition set, corresponding load parameters are applied to the finite element analysis model to solve for the strain response data of all candidate measuring points, thus constructing a strain response matrix. Through refined finite element simulation, the strain response of all candidate measuring points under full risk conditions is quantitatively extracted and systematically integrated. This transforms the physical characteristics of the tower structure's stress and deformation into a quantifiable and analyzable matrix data model, providing standardized and high-precision data support for the subsequent quantitative assessment of the observability of measuring points, ensuring the scientific rigor and objectivity of the quantitative evaluation process. Based on the strain response matrix, the comprehensive observability evaluation value of each candidate measuring point is calculated. A preliminary deployment scheme is determined based on the comprehensive observability evaluation value. This effectively avoids problems such as blind spots in measuring point deployment, information redundancy, and deployment violations, forming a preliminary deployment system that balances theoretical optimality and engineering feasibility. The initial deployment scheme was verified to obtain the optimal deployment scheme. Through iterative correction, monitoring loopholes, redundancy defects, and adaptability issues in the initial deployment scheme were completely eliminated, ultimately resulting in a standardized optimal deployment scheme with full coverage, high fault tolerance, low redundancy, and high adaptability, fully adaptable to the complex operating conditions and long-term monitoring needs of transmission towers. This application completely changes the traditional experience-based, one-sided, and random deployment mode of fiber optic sensors for transmission towers, realizing the scientific, quantitative, refined, and engineering-adaptable deployment of sensor measurement points. It effectively solves the technical problems of incomplete risk coverage, lack of monitoring in key areas, waste of measurement point redundancy, poor fault tolerance, and insufficient environmental adaptability in traditional deployment schemes. It significantly improves the comprehensiveness, accuracy, stability, and reliability of transmission tower structural strain and risk status monitoring. While reducing sensor deployment and maintenance costs, it can accurately capture various potential faults and disaster risks of towers, providing accurate data support for the safe and stable operation of transmission towers, fault early warning, and disaster prevention and mitigation. Its engineering application value and practicality are extremely strong. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is an application environment diagram of an optimized deployment method for fiber optic sensors on power transmission towers according to an embodiment of this application; Figure 2 A flowchart illustrating an optimized deployment method for fiber optic sensors on power transmission towers, provided as an embodiment of this application; Figure 3 A flowchart illustrating an optimized deployment method for fiber optic sensors on power transmission towers, provided as another embodiment of this application; Figure 4 This is a schematic diagram of a transmission tower structure analysis model and candidate optical fiber sensing points provided in an embodiment of this application; Figure 5 This is a schematic diagram of the three-seat operation of the owl tower, drum tower, and wine glass tower provided in one embodiment of this application; Figure 6 A flowchart for evaluating the risk observability of candidate fiber optic sensing points provided in an embodiment of this application; Figure 7 This is a schematic diagram of an optimized fiber optic sensor deployment scheme provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] Existing fiber optic grating sensor deployment schemes for transmission towers rely entirely on the engineering experience of designers, lacking quantitative evaluation criteria and standardized deployment procedures. Considering the actual working conditions of tower geological disaster monitoring, existing technologies suffer from the following objective technical deficiencies, which are also the technical problems that this application urgently needs to solve: 1. There are no quantitative evaluation standards for sensor deployment, and the selection of measurement points is highly subjective.

[0037] Traditional deployment methods determine the location of measuring points based solely on the extreme values ​​of local strain in the poles and the ease of on-site installation. They fail to conduct quantitative assessments by considering the actual risk conditions of the towers and the structural stress transmission patterns. Consequently, they cannot determine the ability of measuring points to perceive tower structural damage and geological disaster displacement risks. This often results in the omission of key risk points and redundant deployment of non-key points, leading to a serious waste of monitoring resources.

[0038] 2. It cannot effectively distinguish multi-source coupled risk conditions and has insufficient risk tracing capabilities.

[0039] Transmission towers face multiple risks during operation, including tower foot settlement, horizontal slippage, foundation stiffness degradation, abnormal conductor loads, and tower component damage. Different risk conditions can trigger strain responses in the tower components from the same source. Existing experience in deploying monitoring points lacks differentiated identification capabilities, making it impossible to distinguish the causes of risks through strain monitoring data and hindering accurate tracing of tower structural risks.

[0040] 3. The monitoring coverage and monitoring point redundancy were not coordinated, resulting in poor adaptability of the monitoring system.

[0041] The existing deployment scheme cannot meet the two major requirements of full coverage of key stress areas and redundant control of measuring points: on the one hand, high-risk stress areas such as tower feet, tower body variable cross section, and crossarm root are prone to monitoring blind spots; on the other hand, a large number of measuring points in the same structural unit have highly overlapping strain responses, resulting in high redundancy of measuring points, which increases the hardware cost and data processing load of the monitoring system.

[0042] 4. Ignoring outdoor site engineering constraints and temperature drift interference results in low accuracy of monitoring data.

[0043] The existing deployment scheme only considers the monitoring needs of the structural stress level, without taking into account the actual engineering constraints such as the accessibility of outdoor installation of towers, fiber optic protection, and extreme weather resistance; at the same time, it lacks a hierarchical standardized temperature compensation deployment scheme, and the fiber optic strain monitoring data is easily affected by environmental temperature drift, resulting in insufficient reliability of long-term online monitoring data.

[0044] 5. The lack of a fault tolerance mechanism for measurement point failures results in insufficient stability of the monitoring system.

[0045] Existing fiber optic monitoring deployment schemes mostly involve independent monitoring at single points, without establishing a linkage between primary and backup monitoring points, nor conducting single-point failure verification. Once a core monitoring point fails, the risk of tower failure in the corresponding area cannot be effectively monitored, and the entire structural health monitoring system is at risk of single-point failure and collapse.

[0046] 6. There is no unified verification process for the deployment plan, and the final monitoring plan lacks reliability verification.

[0047] Existing experience-based deployment methods lack standardized validity verification processes, making it impossible to verify the rationality of deployment schemes from multiple dimensions such as risk condition coverage, key area coverage, redundancy, fault tolerance, and temperature compensation integrity. As a result, the output monitoring deployment schemes have poor implementation and are difficult to directly adapt to tower monitoring projects in complex geological areas such as mountainous areas and mining subsidence areas.

[0048] In summary, this application aims to solve all the above-mentioned technical problems and establish a set of fiber optic sensor optimization deployment methods that take risk observability as the core, quantify the entire process, and take into account both theoretical monitoring performance and on-site engineering feasibility. This will enable the transformation of fiber optic monitoring points on transmission towers from experience-based deployment to risk-oriented quantitative optimization deployment, thereby improving the accuracy of tower structure risk monitoring and the reliability of system operation in complex geological environments.

[0049] The key points of this application are as follows: Key Point 1: The complex deformation of tower foundations caused by various geological disasters such as landslides and mining subsidence is uniformly abstracted and standardized into the support displacement boundary input; at the same time, the first yield response of components, critical displacement characteristic parameters, normalized size evaluation indicators, and component importance weights are integrated to open up the entire chain of application of structural risk perception, disaster resistance rating, tower type selection and matching, and local reinforcement decision-making of members, so as to realize the extension from single measurement point layout optimization to an integrated technical solution for tower disaster risk assessment and engineering treatment.

[0050] Key Point 2: Abandoning the traditional experience-based approach of relying on the extreme values ​​of strain in poles and the convenience of construction, this paper proposes for the first time to use risk observability as the core basis for deployment, focusing on multiple risks such as geological disasters, abnormal loads, and damage to poles, thereby shifting the sensor deployment from "strain-oriented" to "risk identification-oriented".

[0051] Key Point 3: Establish a complete closed-loop system for optimizing the deployment of fiber optic measurement points, sequentially completing the following steps: defining risk variables, modeling multi-source risk conditions, solving the strain response of candidate measurement points, quantitatively evaluating the five-dimensional risk observability, screening for rigid deployment rules, and verifying the effectiveness of multi-dimensional deployment. This forms a fully quantitative, reproducible, and standardized deployment process, filling the gap in existing technologies that lack a complete quantitative deployment system.

[0052] Key Point 4: Construct a risk observability evaluation index system that includes five dimensions: risk sensitivity, risk pattern differentiation, key area coverage, measurement point redundancy, and engineering reliability. Quantify the monitoring capability of each candidate measurement point through a fixed weighting formula to solve the industry pain point of traditional measurement points lacking a unified quantitative scoring standard.

[0053] Key Point 5: Simultaneously consider the theoretical performance of structural monitoring and the actual construction and maintenance constraints on site. Integrate temperature stratification compensation, symmetrical comparison measurement points on the front and back sides of the disaster site, fault-tolerant backup of main and backup measurement points, and single-point failure verification into the deployment process to solve the practical problem that purely theoretical deployment cannot be implemented in engineering projects.

[0054] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Example 1: The optimized deployment method for fiber optic sensors on power transmission towers provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the basic parameters of the fiber optic sensor tower to be deployed to server 104. These basic parameters include structural parameters, load parameters, and environmental parameters. After receiving the basic parameters, server 104 determines the tower's risk state variables based on these parameters and constructs a global risk condition set based on these variables. Based on key parts of the tower, it determines a set of candidate measuring points. It constructs a finite element analysis model of the transmission tower structure, applies corresponding load parameters to the finite element analysis model based on the global risk condition set, solves for the strain response data corresponding to all candidate measuring points, and constructs a strain response matrix. Based on the strain response matrix, it calculates the comprehensive observability evaluation value of each candidate measuring point. Based on the comprehensive observability evaluation value, it determines a preliminary deployment scheme. The preliminary deployment scheme is then verified to obtain the optimal deployment scheme. Server 104 can feed back the obtained optimal deployment scheme to terminal 102. Furthermore, in some embodiments, the optimized deployment method for fiber optic sensors on transmission towers can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly optimize the deployment of fiber optic sensors on transmission towers based on the basic parameters of the towers to be deployed, or server 104 can obtain the basic parameters of the towers to be deployed from the data storage system and optimize the deployment of fiber optic sensors on transmission towers based on these parameters.

[0056] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0057] Example 2: In one exemplary embodiment, such as Figure 2As shown, a method for optimizing the deployment of fiber optic sensors on power transmission towers is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the following steps are included: S1: Obtain the basic parameters of the fiber optic sensor tower to be deployed; the basic parameters include: structural parameters, load parameters and environmental parameters.

[0058] S2: Determine the tower risk state variables based on the basic parameters, and construct a global risk condition set based on the tower risk state variables.

[0059] S3: Determine the set of candidate measurement points based on the key parts of the tower.

[0060] S4: Construct a finite element analysis model of the transmission tower structure. Based on the global risk case set, apply the corresponding load parameters to the finite element analysis model, solve for the strain response data corresponding to all candidate measurement points, and construct the strain response matrix.

[0061] S5: Based on the strain response matrix, calculate the comprehensive observability evaluation value of each candidate measurement point.

[0062] S6: Determine the preliminary deployment plan based on the comprehensive observability evaluation value.

[0063] S7: Verify the preliminary deployment scheme to obtain the optimal deployment scheme.

[0064] Implementing steps S1 to S7 above enables the sensor deployment to no longer be based solely on the magnitude of local strain or empirical location, but rather on the sensitivity of the measuring points to the risk of tower damage, the ability to distinguish different risk modes, the coverage of key risk areas, the non-redundancy between measuring points, and the reliability of engineering installation, thus forming an optimized fiber optic sensor deployment scheme for identifying the risk of transmission tower damage.

[0065] Furthermore, existing evaluations do not adequately consider the placement of predictive sensors. The consequences of yielding of the main material, diagonal members, and localized yielding of transverse members on structural safety are not the same. If only the tower top displacement or the stress threshold monitored by the transmission tower is used as the judgment standard, it may overlook situations where key components have already yielded, and it is difficult to directly determine the overall instability and provide engineering treatment recommendations. In this regard, this application can also indirectly provide an evaluation method that unifies geological disaster support displacement, tower finite element response, component importance, and normalized disaster resistance indicators.

[0066] As an optional implementation, in step S1, the basic parameters specifically include: the cross-sectional dimensions of the members, the node connection method, the tower foot constraint form, the conductor load, and the local weather and geological disaster types.

[0067] As an optional implementation, in step S2, the tower risk state variables are determined based on the basic parameters, and a global risk condition set is constructed based on the tower risk state variables, specifically including: S201: Determine the risk state variables of the tower based on the aforementioned basic parameters; the risk state variables include: uneven settlement of the tower foot, horizontal slippage of the tower foot, degradation coefficient of the three-dimensional constraint stiffness of the foundation, lateral pushing force of the landslide, unbalanced tension of the conductor, and stiffness reduction coefficient of the tower member.

[0068] S202: Based on the aforementioned risk state variables, set the following conditions: single-support vertical settlement displacement, single-support horizontal sliding displacement, double-support vertical settlement displacement, double-support horizontal sliding displacement, three-support vertical settlement displacement, three-support horizontal sliding displacement, four-support differential displacement, four-support oblique sliding, and four-support settlement-sliding coupling.

[0069] As an optional implementation, in step S3, the key parts of the tower include: the upper main material of the tower foot, the lower diagonal material, the tower body variable cross-section node, the root of the crossarm, the conductor hanging point pole, and the tower legs on the disaster-facing and disaster-repelling sides.

[0070] As an optional implementation method, in step S4, a finite element analysis model of the transmission tower structure is built, risk loads of different types and amplitudes are applied, the strain response data corresponding to all candidate measuring points are solved, and the strain response matrix is ​​constructed.

[0071] As an optional implementation, in step S5, based on the strain response matrix, the comprehensive observability evaluation value of each candidate measuring point is calculated, specifically including: S501: Based on the strain response matrix, calculate the comprehensive evaluation index of risk observability for each candidate measuring point; the comprehensive evaluation index includes: risk sensitivity, risk pattern differentiation, key area coverage, measuring point redundancy, and engineering reliability.

[0072] S502: Normalize the comprehensive evaluation index to obtain the normalized index.

[0073] S503: The normalized indicators are weighted and summed to obtain the comprehensive observability evaluation value of each candidate measurement point.

[0074] As an optional implementation, in step S6, a preliminary deployment plan is determined based on the comprehensive observability evaluation value, specifically including: S601: Establish mandatory deployment constraint rules; the mandatory deployment constraint rules include: at least one measuring point is deployed at each of the four tower legs to ensure full coverage of basic risks; symmetrical comparison measuring points are deployed in pairs on the disaster-facing and disaster-repelling tower legs; no more than two measuring points of the same type are on the same member; any risk condition is covered by at least two measuring points, establishing dual measuring point redundancy constraints.

[0075] S602: Select the candidate test points that satisfy the mandatory layout constraint rules as the first candidate test points.

[0076] S603: Select several second candidate measurement points from high to low based on the comprehensive observability evaluation value corresponding to the first candidate measurement point.

[0077] S604: Calculate the safety reserve coefficient for each of the second candidate measurement points.

[0078] S605: The second candidate test point whose safety reserve coefficient is less than the preset safety reserve coefficient is selected as the third candidate test point.

[0079] S606: Determine a preliminary deployment plan based on the third candidate measurement point.

[0080] As an optional implementation, in step S7, the preliminary deployment scheme is checked to obtain the optimal deployment scheme, specifically including: S701: The preliminary deployment scheme is checked for risk condition coverage, key area coverage, measurement point redundancy, single-point failure tolerance, and temperature compensation integrity. The risk condition coverage check verifies whether the selected measurement points cover all risk condition types. The key area coverage check verifies whether measurement points cover key areas such as the tower foot area, lower tower section, middle tower section, upper tower section, and crossarm area. The measurement point redundancy check verifies whether the average correlation coefficient between the selected measurement points is lower than a set threshold. If it is higher than the threshold, there is information redundancy, and some measurement points need to be removed. The single-point failure tolerance check verifies whether other measurement points still cover the same risk condition after any measurement point fails. The temperature compensation integrity check verifies whether the temperature compensation measurement points cover the ambient temperature range of all strain measurement points.

[0081] S702: If any check fails, return to the "Determine the preliminary deployment scheme based on the comprehensive observability evaluation value" step until all checks pass, and obtain the optimal fiber optic sensor deployment scheme including the location of the measuring point, the measurement direction, the temperature compensation method, and the relationship between the main and backup measuring points.

[0082] Compared to existing experience-based deployment techniques for fiber optic sensors on transmission towers, this application offers the following technological advantages by constructing a quantitative evaluation system for risk observability, a standardized multi-source risk condition database, and a full-process closed-loop measurement point verification mechanism: 1. Eliminate reliance on manual experience and achieve quantitative control over the entire process of measuring point deployment. This application abandons the traditional subjective method of measuring point deployment based on the extreme values ​​of strain of members and the convenience of construction. Instead, it uses five quantitative indicators, such as risk sensitivity and risk mode differentiation, as the sole evaluation criteria to unify the evaluation standards for measuring points, eliminate human design bias, and make the sensor deployment scheme reproducible and comparable.

[0083] 2. Accurately distinguish multi-source coupled risks and improve the accuracy of tower risk tracing. For coupled risks caused by different factors of the same strain, such as tower settlement, slippage, foundation stiffness degradation, and abnormal conductor load, the system relies on risk mode discrimination index to quantify and identify differences in strain response at measuring points under different risk conditions. This addresses the pain point that existing measuring points cannot distinguish the source of risk and improves the accuracy of tower structural fault diagnosis in complex geological environments.

[0084] 3. Balancing comprehensive monitoring coverage with redundancy control reduces the construction cost of the monitoring system. On the one hand, it provides full coverage of key risk areas such as tower feet, tower body variable cross-sections, and crossarm roots, eliminating blind spots in structural monitoring; on the other hand, it quantitatively eliminates invalid monitoring points with highly overlapping responses through the redundancy index of monitoring points, reducing the number of monitoring points while ensuring the integrity of monitoring, thereby reducing investment in hardware equipment and the pressure of processing massive amounts of redundant data later.

[0085] 4. Balancing theoretical monitoring performance with practical field engineering considerations, the solution is more feasible. Unlike purely theoretical simulation-based point placement methods, this application incorporates field engineering constraints such as the accessibility of measurement point installation, outdoor fiber optic protection, extreme weather tolerance, and layered temperature compensation into the evaluation system. It also includes a layered temperature compensation measurement point placement strategy to effectively eliminate monitoring errors caused by fiber optic temperature drift and improve the reliability of sensor data during long-term field service.

[0086] 5. An enhanced monitoring system stability is achieved by incorporating fault-tolerant and closed-loop verification mechanisms for measurement points. Redundant design of primary and backup measurement points, along with symmetrical comparison measurement points, and a single-point failure fault-tolerant verification process, prevents localized monitoring failures after core measurement point malfunctions. Simultaneously, multi-level deployment effectiveness closed-loop verification comprehensively validates the rationality of the deployment scheme, ensuring the entire health monitoring system possesses strong anti-interference capabilities and fault self-healing capabilities.

[0087] 6. Adaptable to various tower monitoring scenarios, with wider application versatility. The risk condition database constructed in this application covers all types of fault scenarios, including geological disaster displacement, abnormal conductor load, and structural damage to tower members. It can adapt to the monitoring and deployment needs of transmission towers under different complex conditions such as landslides in mountainous areas, subsidence in mining areas, and strong wind icing. It can directly provide standardized and integrated deployment basis for the design of transmission tower structural health monitoring systems.

[0088] Example 3: like Figure 3As shown, a method for optimizing the deployment of fiber optic sensors on power transmission towers includes the following steps: Step 1: Obtain the structural, load, and environmental parameters of the transmission tower where the sensors will be deployed. Structural parameters include tower type, tower height H, root span B, strut height, member cross-sectional specifications, steel type, material elastic modulus, Poisson's ratio, density, yield strength, node connection method, and tower foot constraint type. Load parameters include conductor and ground wire loads, insulator string loads, icing loads, wind loads, and design load combinations. Environmental parameters include local meteorological conditions, types of geological hazards, and engineering geological survey data.

[0089] In one specific embodiment, the tower to be evaluated is a 110kV straight-line transmission tower with heights varying from 22.1m to 90.5m, and root spans of 5.5m, 16.2m, and 25.2m respectively. The main material is Q345 angle steel with an elastic modulus E = 2.06 × 10⁻⁶. 5 MPa, Poisson's ratio ν=0.3, density ρ=7850kg / m³ 3 The yield strength fy = 345 MPa. The tower feet are connected by anchor bolts, and the foundation type is an extended foundation with a foundation bottom depth of 2.0 m.

[0090] Step Two: Determine the risk state variables of the towers and construct a global risk case set. Risk state variables include uneven settlement at the tower foot Δs, horizontal slippage at the tower foot Δh, the degradation coefficient of the foundation's three-dimensional constraint stiffness k, the lateral pushing force of the landslide P, the unbalanced tension of the conductor T, and the stiffness reduction coefficient β of the members. For each disaster type, single-support, double-support, and triple-support combinations are set up to form at least six basic working conditions. The single-support displacement case simulates the situation where only one tower foot displaces; the double-support displacement case simulates the situation where two tower feet on the same side displace simultaneously; and the triple-support displacement case simulates the situation where all three tower feet displace simultaneously. For mining subsidence, it is abstracted as vertical settlement displacement; for landslides, it is abstracted as horizontal slippage displacement. In actual engineering, it can also be extended to four-support differential displacement, oblique slippage, or settlement-slippage coupled working conditions based on monitoring results. The displacement amplitude for each risk condition is determined based on engineering experience and geological disaster prediction results. The typical range is: settlement displacement 0-100mm, step size 10mm; horizontal sliding displacement 0-200mm, step size 20mm.

[0091] Step 3: Determine the candidate measurement point set for fiber optic sensors. The candidate measurement point locations include key areas such as the upper main members of the tower base, the lower diagonal members, the tower body variable cross-section nodes, the crossarm root, the conductor suspension points, and the tower legs on both the disaster-facing and disaster-repelling sides. Specifically, candidate measurement points include the main members of the four tower legs 300mm from the tower base node plate, the main members at the first variable cross-section node of the tower body, the upper and lower flanges of the main members at the crossarm root, the main members at the conductor suspension points, and the mid-span of the diagonal members on the disaster-facing and disaster-repelling sides. A fiber optic grating strain sensor is installed at each candidate measurement point, and the sensor base is fixed to the surface of the member by welding or bonding. A schematic diagram of the transmission tower structure analysis model and candidate fiber optic sensor points is shown below. Figure 4 As shown.

[0092] In one specific embodiment, for a 110kV straight-line tower, 20 to 24 candidate measurement points are designated at the main materials of the four tower legs, the variable cross-section nodes of the tower body, the root of the crossarm, and the conductor suspension points.

[0093] Step 4: Construct a finite element analysis model of the transmission tower structure, apply displacement / force loads for each risk condition from Step 2, and solve for the strain response data corresponding to all candidate measuring points. In Abaqus, establish a spatial beam element model of the transmission tower, using B31 beam elements to simulate the main members, diagonal members, crossarms, and auxiliary members. Input the member cross-sectional specifications, material density, elastic modulus, Poisson's ratio, and yield strength according to the actual tower structure. For main member intersections and member connection nodes, use common-node connections or elastic coupling constraints to simulate the force and displacement transfer between members under bolted connection conditions. The analysis steps include an initial analysis step and a risk load analysis step. The initial analysis step applies the tower's self-weight and defines initial boundary conditions, while the risk load analysis step applies displacement or force loads for each risk condition. A static general analysis step is used for calculation. During the calculation, support displacements are applied level by level, and the equivalent stress and axial strain values ​​of the members at each candidate measuring point are extracted under each level of displacement. A strain response matrix is ​​constructed, with the matrix rows representing candidate measurement point numbers and the columns representing risk condition numbers. The schematic diagram of the three-seat displacement conditions for the owl tower, drum tower, and wine glass tower is shown below. Figure 5 As shown.

[0094] Step 5: Calculate the comprehensive evaluation index of risk observability for each candidate monitoring point. The evaluation index system includes five dimensions: risk sensitivity, risk pattern discrimination, critical area coverage, monitoring point redundancy, and engineering reliability. The risk sensitivity index reflects the sensitivity of the monitoring point's strain response to changes in the amplitude of risk conditions, i.e., the dispersion of the strain response data under various risk conditions. The risk pattern discrimination index reflects the degree of difference in the mean strain response of the monitoring point under different types of risk conditions; the greater the difference, the better the monitoring point can distinguish different risk sources. The critical area coverage index reflects whether the monitoring point is located in critical risk areas such as the tower foot area, lower section of the tower, middle section of the tower, upper section of the tower, and crossarm area; different areas are assigned different weights according to their impact on structural safety. The monitoring point redundancy index is evaluated by calculating the correlation coefficient between the monitoring point and other candidate monitoring points; the lower the correlation coefficient, the higher the redundancy index, indicating greater independent monitoring value of the monitoring point. The engineering reliability index is comprehensively scored based on the accessibility of the monitoring point's installation location, the difficulty of protecting the fiber optic cable, and the degree of exposure to extreme weather. The flowchart for the risk observability assessment of candidate fiber optic sensing points is as follows: Figure 6 As shown.

[0095] When calculating the coverage index of key areas, the risk weight of each area is determined based on the stress characteristics of the members obtained from finite element analysis. The main members, diagonal members, horizontal members, and auxiliary members play different roles in the structural force transmission path, and their importance varies. This is combined with the results calculated using equation (1). and The normalized deformation resistance index is used as a reference for regional weight allocation.

[0096] The normalization calculation module calculates the deformation resistance index at the height and root opening scales according to equations (1) and (2): , (1); (2); in, The safety reserve coefficient for the location of each candidate measuring point; The critical support displacement of the area where the candidate measuring point is located under the corresponding risk condition; For disaster support displacement obtained through prediction or monitoring; For the height of the tower; For the root to open; This is a normalized resistance to deformation at the height scale; It is a normalized resistance to deformation index under the root-opening scale.

[0097] Specifically, under a certain risky working condition, if a certain area or A smaller value indicates that the area is more prone to damage under the same support displacement conditions, and should be assigned a higher regional risk weight. The candidate measurement points in this area will then receive a higher key area coverage score.

[0098] After normalizing the above five indicators, a weighted sum is obtained to obtain the comprehensive observability evaluation value of each candidate measurement point. The weight coefficients are determined according to the analytic hierarchy process or the expert survey method.

[0099] Step Six: Based on the comprehensive observability evaluation values ​​of each candidate measuring point and in conjunction with the mandatory deployment constraints, a preliminary sensor deployment scheme is selected. The mandatory deployment constraints include: at least one measuring point is deployed at each of the four tower legs to ensure full coverage of basic risks; symmetrical comparative measuring points are deployed in pairs on the disaster-facing and disaster-repelling tower legs; no more than two measuring points of the same type are allowed on the same member; any risk condition is covered by at least two measuring points, establishing a dual-measuring-point redundancy constraint.

[0100] Under the premise of satisfying the above constraints, measuring points are selected from high to low based on the comprehensive observability evaluation value to form a preliminary layout plan. During the screening process, the safety reserve coefficient of each candidate measuring point is calculated using formula (2). For a given candidate measuring point area, its critical support position under the corresponding risk condition is shifted. u cr Disaster support displacement obtained from prediction or monitoring u pre Substituting into equation (2), we obtain the following calculation: value. The smaller the value, the closer the area is to a critical state under the current disaster displacement, indicating a high-risk area. Candidate monitoring points in this area should be given priority in the deployment plan.

[0101] Step 7: Conduct multi-level verification of the initial deployment plan and iteratively optimize the combination of measuring points. Multi-level verification includes: risk condition coverage verification—checking whether the selected measuring points cover all risk condition types; critical area coverage verification—checking whether measuring points cover critical areas such as the tower foot area, lower tower section, middle tower section, upper tower section, and crossarm area; measuring point redundancy verification—checking whether the average correlation coefficient between the selected measuring points is lower than a set threshold. If it is higher than the threshold, there is information redundancy, and some measuring points need to be removed; single-point failure tolerance verification—checking whether other measuring points still cover the same risk condition after any measuring point fails; and temperature compensation integrity verification—checking whether temperature compensation measuring points cover the ambient temperature range of all strain measuring points. If any verification fails, return to Step 6, add the highest-scoring candidate points from the unselected measuring points, and recombine them until all verifications pass. The final output includes the optimal sensor deployment plan, including measuring point location, measurement direction, temperature compensation method, and the relationship between primary and backup measuring points.

[0102] If any of the above five-layer checks fails, return to step six. Add candidate points from the unselected points in descending order of their comprehensive observability evaluation value, then recombine them and perform all five layers of checks again. In each iteration, prioritize adding candidate points with the highest comprehensive score and the lowest redundancy with the already selected points. Repeat this process until all five layers of checks pass, completing the iterative optimization of the point combination. The final output includes the optimal fiber optic sensor deployment scheme, including point location, measurement direction, temperature compensation method, and primary / backup point relationship. A schematic diagram of the optimized fiber optic sensor deployment scheme is shown below. Figure 7 As shown.

[0103] Example 4: Taking a 220kV straight-line transmission tower as an example, the method of this application is used to optimize the deployment of fiber optic sensors.

[0104] The tower is located in a hilly area in southern China. It is 38.5 meters high, with a base span of 9.8 meters. The main material is Q345 angle steel, with an elastic modulus E = 2.06 × 10⁻⁶. 5 The tower has a yield strength of 345 MPa and a crossarm strength of fy = 345 MPa. The diagonal members and crossarms are made of Q235 and Q345 steel, respectively. The tower is located in a gently sloping area covered by residual soil, with a cover layer thickness of approximately 5-8 meters. The underlying bedrock is argillaceous sandstone, and the groundwater is relatively deep. While the surface soil of the slope has good permeability, the deeper residual soil is prone to softening when exposed to water, posing a risk of geological disasters such as landslides and uneven foundation settlement. The area has a subtropical monsoon climate with an average annual rainfall of 1400-1800 mm. The flood season (May to September) accounts for more than 70% of the annual rainfall, with frequent short-duration heavy rainfall and a maximum daily rainfall exceeding 150 mm, making it a high-risk period for landslides.

[0105] After obtaining the tower structure parameters according to step one of Example 3, the risk state variables were determined according to step two of Example 3. The landslide effect was abstracted as horizontal sliding at the tower foot, and the effects of rainfall and mining subsidence were abstracted as uneven settlement at the tower foot. Based on historical rainfall data and geological disaster survey results in the region, four levels of daily rainfall (30mm, 50mm, 70mm, 90mm), four durations of rainfall (1d, 3d, 5d, 7d), and tower foot horizontal sliding displacement (0~150mm, step size 10mm) and uneven settlement displacement (0~80mm, step size 10mm) were set to form a comprehensive risk condition set covering the coupling effect of geological disaster displacement and rainfall infiltration, generating more than 180 sets of calculation conditions.

[0106] Following step three of Example 3, 22 candidate measurement points were identified at various locations: 300mm from the tower leg main member to the tower foot node plate; the main member at the first variable cross-section node of the tower body; the upper and lower flanges of the main member at the root of the crossarm; the main member at the conductor suspension point; and the middle section of the diagonal member of the tower body. A fiber optic strain sensor was planned to be installed at each measurement point. The sensor base was fixed to the surface of the member by welding. Four data lines were arranged from top to bottom on the main members in four directions of the tower body, running along the inner side of the main members and connecting to the photoelectric synchronous demodulation equipment at the tower foot. Real-time data acquisition was performed using data acquisition software. The fiber optic strain sensor has advantages such as strong anti-electromagnetic interference capability, high accuracy, easy networking, and suitability for long-term online field monitoring, meeting the long-term strain monitoring needs of transmission towers under complex climatic conditions.

[0107] Following step four of Example 3, a spatial beam element finite element model of the tower was established in Abaqus. The main members, diagonal members, and crossarms were all simulated using B31 beam elements, with nodes connected via common nodes. The tower feet were set as fixed hinged supports, and the foundation and subgrade were treated as rigid connections. Gravity loads were applied first to complete the initial analysis step, followed by displacement loads for each risk condition in the risk load analysis step. A static general analysis step was used for calculation, with a time length of 1.0, an initial increment step value of 0.01, and a minimum increment step of 1×10⁻¹⁰. -8 The maximum increment step was set to 0.1 to simulate the slow application of foundation displacement in a quasi-static manner. After the calculation was completed, the axial strain values ​​at all 22 candidate measuring points were extracted to construct a 22×180 strain response matrix.

[0108] The five evaluation indicators for each candidate measuring point were calculated according to step five of Example 3. Taking the measuring point 300mm away from the tower foot node plate on the main tower leg as an example, its strain response is significant under horizontal slip and settlement conditions. When the horizontal slip reaches 80mm, the strain increment reaches 350με, ranking first in risk sensitivity. The measuring point at the root of the crossarm is sensitive to changes in conductor load but not to foundation displacement, with a high risk mode discrimination index, effectively distinguishing settlement-type conditions from abnormal conductor load conditions. The strain response of the measuring point at the mid-span of the inclined member in the middle of the tower body has a correlation coefficient of 0.87 with adjacent measuring points, indicating a low redundancy index. After normalization and weighting, the measuring point on the main tower leg has the highest comprehensive score (O=0.91), followed by the measuring point at the root of the crossarm (O=0.84), and the measuring point on the auxiliary member in the middle of the tower body has the lowest score (O=0.38).

[0109] Preliminary screening was conducted according to the mandatory constraint rules in step six of Example 3. At least one measuring point was installed at each of the four tower legs, and each risk condition was covered by at least two measuring points. Simultaneously, the requirements for critical area coverage and measuring point redundancy control were met. Under the premise of satisfying the constraints, 14 strain measuring points were initially selected based on their comprehensive scores from highest to lowest.

[0110] Five-layer verification was conducted according to step seven of Example 3. The risk condition coverage verification showed that the 14 measuring points covered all six risk conditions, with at least two measuring points outputting effective strain signals under each condition. The key area coverage verification confirmed that measuring points were deployed in the tower foot area, lower tower section, middle tower section, upper tower section, and crossarm area, eliminating the monitoring blind spots commonly found in the middle tower section in traditional empirical point deployment schemes. The measuring point redundancy verification found that the strain response correlation coefficient between adjacent measuring points on the tower leg and some measuring points in the variable cross-section area of ​​the tower body exceeded 0.85, indicating information redundancy, which was subsequently merged and optimized. The single-point failure tolerance verification showed that after any measuring point failed, at least one other measuring point still covered the risk condition where that measuring point was located, meeting the tolerance requirements. The temperature compensation integrity verification indicated that temperature compensation measuring points needed to be deployed in the tower leg and middle tower section to eliminate the impact of wavelength drift caused by ambient temperature changes on strain measurement accuracy. After two rounds of iterative optimization, the optimal deployment scheme of 10 strain measuring points and 2 temperature compensation measuring points was finally output.

[0111] A comparison of the proposed solution with traditional empirical deployment methods reveals that the traditional method, which determines measuring points based on the location of the maximum strain extreme value of the members and deploys 18 measuring points, only covers 4 types of risk conditions, leaving a monitoring blind spot in the middle section of the tower. The proposed solution, however, covers all 6 types of risk conditions and 5 key areas with only 10 measuring points, reducing the number of measuring points by 44.4%. Regarding the ability to distinguish risk modes, the measuring points selected in this proposed solution show a significantly improved ability to differentiate strain responses under settlement and slippage conditions compared to the traditional method. Actual measurement data shows that the monitoring data volume of 10 measuring points is reduced by 44% compared to 18 measuring points, significantly reducing the hardware costs and maintenance burden of data acquisition, transmission, storage, and processing.

[0112] As can be seen from the examples, this method can output differentiated fiber optic sensor optimization deployment schemes based on the structural response characteristics of towers under different risk conditions and the five-dimensional observability evaluation index. This effectively reduces the number of measuring points while ensuring the integrity of risk monitoring coverage, thereby improving the economy and reliability of the monitoring system.

[0113] Example 5: In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores the basic parameters of the fiber optic sensor towers to be deployed. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for optimizing the deployment of fiber optic sensors on power transmission towers.

[0114] Figure 8 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0115] Example 6: In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0116] Example 7: In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0117] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0118] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0119] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0120] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the layout of optical fiber sensors for power transmission towers, characterized by, include: Obtain the basic parameters of the fiber optic sensor tower to be deployed; The basic parameters include: structural parameters, load parameters, and environmental parameters; Based on the aforementioned basic parameters, the risk state variables of the towers are determined, and a global risk condition set is constructed based on the aforementioned risk state variables. Based on the key parts of the tower, a set of candidate measurement points is determined; A finite element analysis model of the transmission tower structure is constructed. Based on the global risk case set, corresponding load parameters are applied to the finite element analysis model, and the strain response data corresponding to all candidate measurement points are solved to construct the strain response matrix. Based on the strain response matrix, the comprehensive observability evaluation value of each candidate measuring point is calculated; A preliminary deployment plan is determined based on the comprehensive observability evaluation value; The preliminary deployment plan was verified to obtain the optimal deployment plan.

2. The method for optimizing the deployment of fiber optic sensors on transmission towers according to claim 1, characterized in that, Based on the aforementioned basic parameters, tower risk state variables are determined, and a global risk condition set is constructed based on these tower risk state variables, specifically including: Based on the aforementioned basic parameters, the risk state variables of the tower are determined; the risk state variables include: uneven settlement of the tower foot, horizontal slippage of the tower foot, degradation coefficient of the three-dimensional constraint stiffness of the foundation, lateral pushing force of the landslide, unbalanced tension of the conductor, and stiffness reduction coefficient of the tower members. Based on the aforementioned risk state variables, the following conditions are set: single-support vertical settlement displacement condition, single-support horizontal sliding displacement condition, double-support vertical settlement displacement condition, double-support horizontal sliding displacement condition, three-support vertical settlement displacement condition, three-support horizontal sliding displacement condition, four-support differential displacement condition, four-support oblique sliding condition, and four-support settlement-sliding coupling condition.

3. The method for optimizing the deployment of fiber optic sensors on transmission towers according to claim 1, characterized in that, The key components of the tower include: the upper main material of the tower foot, the lower diagonal material, the variable cross-section node of the tower body, the root of the crossarm, the conductor hanging point, and the tower legs on the disaster-facing and disaster-repelling sides.

4. The method for optimizing the deployment of fiber optic sensors on transmission towers according to claim 1, characterized in that, Based on the strain response matrix, the comprehensive observability evaluation value of each candidate measuring point is calculated, specifically including: Based on the strain response matrix, a comprehensive evaluation index of risk observability for each candidate measuring point is calculated; the comprehensive evaluation index includes: risk sensitivity, risk pattern discrimination, key area coverage, measuring point redundancy, and engineering reliability. The comprehensive evaluation index is normalized to obtain the normalized index; The normalized indicators are weighted and summed to obtain the comprehensive observability evaluation value of each candidate measurement point.

5. The method for optimizing the deployment of fiber optic sensors on transmission towers according to claim 1, characterized in that, Based on the comprehensive observability evaluation value, a preliminary deployment plan is determined, specifically including: Establish mandatory deployment constraint rules; the mandatory deployment constraint rules include: at least one measuring point is deployed at each of the four tower legs to ensure full coverage of basic risks; symmetrical comparative measuring points are deployed in pairs on the disaster-facing and disaster-repelling tower legs; no more than two measuring points of the same type are on the same member; any risk condition is covered by at least two measuring points, establishing dual measuring point redundancy constraints; Candidate measurement points that satisfy the mandatory layout constraint rules are selected as the first candidate measurement points; Several second candidate measurement points are selected from high to low based on the comprehensive observability evaluation value corresponding to the first candidate measurement point; Calculate the safety reserve coefficient for the location of each second candidate measuring point; The second candidate test point whose safety reserve coefficient is less than the preset safety reserve coefficient is selected as the third candidate test point; A preliminary deployment plan is determined based on the third candidate measurement point.

6. The method for optimizing the deployment of fiber optic sensors on transmission towers according to claim 5, characterized in that, The formula for calculating the safety reserve factor is: ； in, The safety reserve coefficient for the location of each candidate measuring point; The critical support displacement of the area where the candidate measuring point is located under the corresponding risk condition; For the predicted or monitored displacement of disaster support.

7. The method for optimizing the deployment of fiber optic sensors on transmission towers according to claim 1, characterized in that, The preliminary deployment plan was verified to obtain the optimal deployment plan, which specifically includes: The preliminary deployment scheme is verified for risk condition coverage, key area coverage, measurement point redundancy, single-point failure tolerance, and temperature compensation integrity. The risk condition coverage verification checks whether the selected measurement points cover all risk condition types. The key area coverage verification checks whether measurement points cover key areas such as the tower foot area, lower tower section, middle tower section, upper tower section, and crossarm area. The measurement point redundancy verification checks whether the average correlation coefficient between the selected measurement points is lower than a set threshold; if it is higher, there is information redundancy, and some measurement points need to be removed. The single-point failure tolerance verification checks whether other measurement points still cover the same risk condition after any measurement point fails. The temperature compensation integrity verification checks whether the temperature compensation measurement points cover the ambient temperature range of all strain measurement points. If any check fails, return to the "Determine the preliminary deployment scheme based on the comprehensive observability evaluation value" step until all checks pass and the optimal deployment scheme is obtained.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the method for optimizing the deployment of fiber optic sensors on transmission towers according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for optimizing the deployment of fiber optic sensors on power transmission towers as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for optimizing the deployment of fiber optic sensors on power transmission towers as described in any one of claims 1-7.

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