Method, device and storage medium for optimizing sensor layout of transmission tower vibration monitoring

Abstract

This invention discloses a method, device, and storage medium for optimizing the layout of vibration monitoring sensors for power transmission towers. The method divides each type of part of the tower into multiple nodes, calculates the maximum equivalent stress, root mean square of equivalent stress, stress gradient, peak acceleration, and root mean square of acceleration for each node over a time history. Based on the calculation results of each node over the time history, multiple candidate nodes are selected, and these candidate nodes are sorted in descending order according to a comprehensive importance score to obtain a candidate node importance ranking table. Based on the comprehensive importance score and spatial distribution constraints, the initial monitoring point set obtained from the candidate node importance ranking table is iteratively updated to obtain the final monitoring point set. Based on the stress and acceleration calculation results of each node in the final monitoring point set, the sensor type for each node is configured. The device and storage medium are used to implement the method. This invention avoids redundant sensor layout and achieves an optimal balance between monitoring effectiveness and economic cost.

Description

Technical Field

[0001] This invention relates to the field of sensor layout methods, specifically a method, device, and storage medium for optimizing the layout of sensors for monitoring vibration of power transmission towers. Background Technology

[0002] With the rapid development of IoT and sensor technologies, multi-sensor-based online monitoring systems have gradually become the mainstream technology for monitoring the health of power transmission towers. However, existing monitoring systems have the following significant problems in sensor deployment:

[0003] First, the arrangement method lacks scientific basis.

[0004] The placement of sensors in many engineering projects relies heavily on the subjective experience and judgment of engineers. They often choose locations that are easy to install or simply place them evenly at equal heights on the towers, failing to fully consider the actual stress distribution characteristics, dynamic response characteristics, and damage-prone areas of the structure, resulting in unsatisfactory monitoring results.

[0005] Second, the contradiction between economy and effectiveness is prominent.

[0006] In pursuit of comprehensive monitoring coverage, some projects employ a strategy of densely deploying numerous sensors, resulting in high costs for equipment procurement, installation and commissioning, subsequent maintenance, and data transmission and storage. This also generates a large amount of redundant data, increasing the complexity of data processing and analysis. Other projects, constrained by budget, reduce the number of sensors, but improper placement may lead to the omission of critical monitoring areas, failing to effectively capture abnormal vibration responses and early damage signals in the structure.

[0007] Third, the optimization method faces technical bottlenecks.

[0008] Although the academic community has proposed some sensor optimization placement methods based on theories such as modal analysis, effective independence (EI), and modal kinetic energy (MKE), these methods often only consider the single factor of the structure's dynamic characteristics, failing to comprehensively consider static stress distribution, dynamic response characteristics, structural vulnerability, and the feasibility of engineering implementation. Even though some studies have adopted intelligent optimization algorithms such as genetic algorithms and particle swarm optimization, the finite element model of transmission towers typically contains thousands of nodes, making direct optimization calculations on all nodes extremely computationally complex and difficult for practical engineering applications.

[0009] Therefore, there is an urgent need to develop a scientific, economical, and highly operable method for optimizing the layout of transmission tower vibration monitoring sensors, so as to achieve accurate configuration of the monitoring system, reduce engineering costs, and improve monitoring efficiency. Summary of the Invention

[0010] This invention provides a method, device, and storage medium for optimizing the layout of vibration monitoring sensors for power transmission towers, in order to solve the problems of existing power transmission tower sensor layout methods, such as reliance on experience, high cost, and poor performance.

[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0012] The optimization method for the layout of vibration monitoring sensors on power transmission towers is as follows:

[0013] Each type of part of the tower is divided into multiple position units, and each position unit is used as a node. Based on the dynamic response of each node under a set time history and external load, the maximum equivalent stress, stress gradient, peak acceleration, and root mean square acceleration of each node within the time history are calculated.

[0014] Based on the maximum equivalent stress, root mean square acceleration, and stress gradient of each node over the time history, multiple candidate nodes are selected from each node according to multiple screening criteria.

[0015] Calculate the overall importance score for each candidate node, sort the candidate nodes in descending order based on the overall importance score, and form a candidate node importance ranking table from the sorted candidate nodes.

[0016] Based on the comprehensive importance score and the total number of vibration monitoring sensors to be deployed, multiple candidate nodes are selected from the candidate node importance ranking table to form an initial monitoring point set. Then, based on spatial distribution constraints and the comprehensive importance score, the remaining candidate nodes are selected from the candidate node importance ranking table to iteratively update the initial monitoring point set. The iteratively updated initial monitoring point set serves as the final monitoring point set, which simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution. Each candidate node in the final monitoring point set serves as a final monitoring point, and each final monitoring point is used to deploy a vibration monitoring sensor, thereby completing the layout optimization of the vibration monitoring sensor.

[0017] Furthermore, by performing finite element analysis on the tower, a three-dimensional numerical analysis model of the tower and the structural dynamics control equations are established, and each type of part of the tower is discretized into multiple positional elements as nodes in the three-dimensional numerical analysis model.

[0018] Multiple time-varying external loads are applied to the three-dimensional numerical analysis model of the tower to induce a dynamic response at each node. By performing time history analysis on the structural dynamics control equations, dynamic response data of each node at each moment within the set time history are obtained, including acceleration response data and stress tensor response data.

[0019] Based on the stress tensor response data of any node at any time during the entire time history, the maximum equivalent stress, root mean square equivalent stress, and stress gradient of that node during the entire time history are calculated.

[0020] Based on the acceleration response data of any node at any time point throughout the entire time history, the peak acceleration and root mean square acceleration of that node throughout the entire time history are calculated.

[0021] Furthermore, screening criteria one is formed by using the equivalent stress threshold as the judgment criterion, screening criteria two is formed by using the acceleration response threshold as the judgment criterion, and screening criteria three is formed by using the stress gradient threshold as the judgment criterion, thus obtaining multiple screening criteria.

[0022] When the maximum equivalent stress of any node over the entire time history is greater than or equal to the equivalent stress threshold, the result is that the node meets screening criterion one.

[0023] When the root mean square of acceleration of any node over the entire time history is greater than or equal to the acceleration response threshold, the result is that the node satisfies screening criterion two.

[0024] If the stress gradient of any node is greater than or equal to the stress gradient threshold throughout the entire time history, then the node is judged to meet the third screening criterion.

[0025] If any node satisfies at least two of the three screening criteria, then that node is selected as a candidate node.

[0026] Alternatively, calculate the weighted sum of the binary indicator function values ​​of the judgment results of each node under screening criteria one, two, and three, and use this sum as the comprehensive binary indicator function value of the corresponding node; compare the comprehensive binary indicator function value of each node with the set comprehensive screening index threshold, and select the node as a candidate node when the comprehensive binary indicator function value of any node is greater than or equal to the comprehensive screening index threshold.

[0027] Furthermore, based on the stress level score, dynamic response score, stress gradient score, and structural location score of the node, a comprehensive importance scoring system is established. A quantitative comprehensive importance evaluation is performed on each candidate node to obtain a comprehensive importance score for each candidate node; where:

[0028] The stress level score for each candidate node is determined based on the maximum equivalent stress of the corresponding candidate node over the entire time history.

[0029] The dynamic response score of each candidate node is determined based on the root mean square acceleration and peak acceleration of the corresponding candidate node over the entire time history.

[0030] The stress gradient score for each candidate node is determined based on the stress gradient of the corresponding candidate node over the entire time history.

[0031] Each candidate node's structural location is assigned a score, which is determined based on whether the corresponding candidate node belongs to a critical part and the critical part category to which the candidate node belongs;

[0032] Finally, the sum of the stress level score, dynamic response score, stress gradient score, and structural location additional score for each candidate node is calculated to obtain the comprehensive importance score of the corresponding candidate node.

[0033] Furthermore, assuming the total number of vibration monitoring sensors to be deployed is K, the top K candidate nodes are selected from the candidate node importance ranking table in descending order. These K candidate nodes serve as initial monitoring points, forming an initial monitoring point set. The remaining candidate nodes in the candidate node importance ranking table, maintaining their original order, form the remaining candidate node set. Then, using the minimum spatial spacing constraint as the spatial distribution constraint, and based on the minimum spatial spacing constraint and the comprehensive importance score, the remaining candidate nodes are selected from the remaining candidate node set to iteratively update the initial monitoring point set. The iteratively updated initial monitoring point set serves as the final monitoring point set, simultaneously satisfying both maximizing importance and rationalizing spatial distribution. The iterative update process is as follows:

[0034] B1. Currently, two different candidate nodes are randomly selected from the initial monitoring points to form a node pair. It is then determined whether the two candidate nodes selected have been selected before.

[0035] If the two candidate nodes currently selected have both been selected before, then two different candidate nodes will be randomly selected again to form a node pair.

[0036] If either of the two candidate nodes currently selected has not been selected before, then calculate the spatial Euclidean distance between the two candidate nodes.

[0037] B2. Compare the spatial Euclidean distance between the two selected candidate nodes with the minimum spatial spacing constraint;

[0038] If the spatial Euclidean distance is less than the minimum spatial spacing constraint, then the node pair consisting of the two selected candidate nodes is a node pair that violates the spatial constraint, and the subsequent step B3 is executed.

[0039] If the spatial Euclidean distance is greater than or equal to the minimum spatial spacing constraint, return to step B1.

[0040] B3. Compare the comprehensive importance scores of the two candidate nodes in the node pair that violates the spatial distribution constraints. The candidate node with the higher comprehensive importance score is selected as the candidate node to be retained, and the other candidate node with the lower comprehensive importance score is selected as the candidate node to be replaced.

[0041] B4. From the remaining candidate nodes in the remaining candidate node set, select the remaining candidate node with the highest comprehensive importance score as the current candidate node, and determine whether there are any historical candidate nodes that have been added to the initial monitoring point set.

[0042] If there are no previously selected candidate nodes that have been added to the initial monitoring point set, the current selected candidate node is added to the initial monitoring point set to replace the candidate nodes to be replaced in the node pairs that violate the spatial distribution constraints in the initial monitoring point set. The candidate nodes to be retained in the node pairs that violate the spatial distribution constraints are retained in the initial monitoring point set, thus obtaining the updated initial monitoring point set.

[0043] If there are historical candidate nodes that have already been added to the initial monitoring point set, calculate the spatial distance between the current candidate node and each historical candidate node that has already been added to the initial monitoring point set, and compare it with the minimum spatial distance constraint:

[0044] If the spatial distance between the current candidate node and any historical candidate node is less than the minimum spatial distance constraint, then the current candidate node is abandoned. After removing the abandoned candidate node from the remaining candidate node set, step B4 is re-executed to reselect a candidate node.

[0045] If the spatial distance between the current candidate node and all historical candidate nodes is greater than or equal to the minimum spatial distance constraint, then the current candidate node is added to the initial monitoring point set, replacing the candidate nodes to be replaced in the node pairs that violate the spatial distribution constraint in the initial monitoring point set, and the candidate nodes to be retained in the node pairs that violate the spatial distribution constraint are retained in the initial monitoring point set, thus obtaining the updated initial monitoring point set;

[0046] The selected candidate nodes that have been added to the initial monitoring point set are removed from the remaining candidate node set, thus obtaining the updated remaining candidate node set;

[0047] B5. Repeat steps B1-B4 above to iteratively update the initial monitoring point set until the spatial Euclidean distance between any two nodes in the updated initial monitoring point set is greater than or equal to the minimum spatial spacing constraint, then stop the iteration.

[0048] The initial set of monitoring points at the point where iteration stops is output as the final set of monitoring points.

[0049] Furthermore, it also includes: after obtaining the final monitoring point set that simultaneously satisfies the maximization of importance and the rationalization of spatial distribution, dividing the tower structure into multiple segments along the height direction; based on the total number of final monitoring points belonging to each segment in the final monitoring point set, obtaining the monitoring coverage rate of each segment and the overall spatial coverage of each segment; and then constructing a tower spatial coverage uniformity index based on the monitoring coverage rate of each segment and the overall spatial coverage of each segment.

[0050] When the uniformity index of pole and tower spatial coverage is less than or equal to the preset threshold of uniformity index, the final monitoring point set is the final monitoring point set that meets the spatial coverage requirements.

[0051] When the pole and tower spatial coverage uniformity index is greater than the preset coverage uniformity index threshold, the final monitoring point set is determined not to meet the spatial coverage requirements. At this time, the final monitoring point set is rebalanced. The rebalance adjustment process is as follows: at least one final monitoring point in the final monitoring point set located in the segment with high monitoring coverage is replaced with a candidate node in the candidate node importance ranking table located in the segment with insufficient monitoring coverage, while maintaining the spatial distribution constraints of the replaced final monitoring point set during the replacement process. The above replacement adjustment is repeated until the adjusted pole and tower spatial coverage uniformity index is less than or equal to the preset coverage uniformity index threshold, thus obtaining a final monitoring point set that meets the spatial coverage requirements. When the pole and tower spatial coverage uniformity index is less than or equal to the preset coverage uniformity index threshold, the current final monitoring point set is taken as the final monitoring point set that meets the spatial coverage requirements.

[0052] Each candidate node in the final monitoring point set that meets the spatial coverage requirements is used as a final monitoring point, and each final monitoring point is used to deploy vibration monitoring sensors, thereby completing the layout optimization of vibration monitoring sensors.

[0053] Furthermore, the uniformity index of the pole space coverage As shown in the following formula:

[0054]

[0055] in, Total number of segments; The monitoring coverage rate of the segment at the l-th altitude; Overall spatial coverage of each section;

[0056] As shown in the following formula:

[0057]

[0058] In the formula: The number of vibration monitoring sensors actually deployed in the section at the l-th height is the number of vibration monitoring sensors that need to be allocated to each final monitoring point in the section at the l-th height, based on the final monitoring point set. Let be the total number of candidate nodes within the segment at height l. Based on the candidate node importance ranking table, and using the spatial coordinate information of each candidate node in the ranking table, the total number of all candidate nodes whose spatial coordinate information belongs to the segment at the l-th altitude is obtained. ;

[0059] As shown in the following formula:

[0060] .

[0061] Furthermore, it also includes: calculating the root mean square of the equivalent stress of each node over the time history; and determining the type of vibration monitoring sensor that can be configured for each final monitoring point based on the maximum equivalent stress, root mean square of the equivalent stress, peak acceleration, and root mean square of acceleration of each final monitoring point in the set time history. The process is as follows:

[0062] Let the maximum equivalent stress at any i-th final monitoring point be... The root mean square of the equivalent stress is When satisfied or When the i-th final monitoring point is selected, a strain sensor from the vibration monitoring sensors can be configured, where... The yield strength of the tower material;

[0063] Let the peak acceleration of any i-th final monitoring point be... The root mean square of acceleration is When satisfied or When the i-th final monitoring point is selected, an acceleration sensor from the vibration monitoring sensors can be configured; where It is the acceleration due to gravity. The acceleration response threshold;

[0064] When the maximum equivalent stress at any i-th final monitoring point Root mean square of acceleration ,satisfy and When the i-th final monitoring point is configured with both strain and acceleration sensors, it can be configured simultaneously.

[0065] An electronic device includes a processor and a memory, wherein program instructions in the memory are read and executed by the processor to perform the above-described method for optimizing the layout of transmission tower vibration monitoring sensors.

[0066] A storage medium storing program instructions, which, when read and executed, perform the aforementioned method for optimizing the layout of transmission tower vibration monitoring sensors.

[0067] This invention innovatively proposes a node importance evaluation system that integrates stress, dynamics, and location features. Combined with an adaptive iterative optimization algorithm based on spatial distribution constraints, it achieves scientific, systematic, and economical sensor placement, solving the problems of traditional methods that rely on experience, are costly, and have unsatisfactory results. Compared to existing single-feature optimization methods such as EI and MKE, this invention simultaneously considers multi-dimensional information such as static stress distribution, dynamic vibration response, stress gradient concentration, and key structural components. Multi-condition envelope analysis ensures the comprehensiveness of the monitoring scheme, and the spatial constraint iterative algorithm avoids redundant sensor placement, achieving an optimal balance between monitoring effectiveness and economic cost.

[0068] Compared with the prior art, the advantages of the present invention are:

[0069] (1) High scientific rigor: The invention innovatively establishes a node importance evaluation system that integrates stress-dynamic-location characteristics. Compared with traditional methods such as EI and MKE that only consider a single dynamic characteristic, this invention takes into account static stress distribution, dynamic vibration response, stress gradient concentration and key part characteristics, comprehensively reflects the monitoring value of nodes, and ensures the pertinence and effectiveness of sensor placement.

[0070] (2) Good economic efficiency: By using multiple screening criteria, the global optimization problem of thousands of nodes is transformed into a local optimization problem of dozens of nodes, which significantly reduces the computational complexity; the iterative algorithm with spatial distribution constraints avoids redundant sensor deployment, and can significantly reduce the number of sensors while ensuring the monitoring effect, thereby reducing the system construction and maintenance costs.

[0071] (3) Wide applicability: Spatial coverage evaluation and rebalancing adjustment mechanism ensures the global coverage performance of the monitoring system and avoids omission of key areas.

[0072] (4) High operability: The algorithm process is clear and easy to implement in engineering; the sensor type matching and parameter configuration methods are clear and can directly guide on-site installation and system debugging. Attached Figure Description

[0073] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0074] Figure 2This is a flowchart of the spatial distribution constraint iterative algorithm in an embodiment of the present invention. Detailed Implementation

[0075] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0076] This embodiment discloses a method for optimizing the layout of vibration monitoring sensors on transmission towers. The method optimizes the arrangement of vibration monitoring sensors that need to be installed on the tower. The types of vibration monitoring sensors include strain sensors (such as resistance strain gauges and fiber optic strain sensors) and acceleration sensors (such as piezoelectric or MEMS accelerometers). This embodiment uses a straight angle steel tower used in a transmission line as an example. The tower is 70 m high, located in a wind-prone area, and its main material is Q345 steel (yield strength 345 MPa). The tower is subjected to various loads, including wind load, icing load, and temperature stress. Figure 1 As shown, the method process in this embodiment is as follows:

[0077] Step S1: Establish a three-dimensional numerical analysis simulation model of the tower. In the three-dimensional numerical analysis model, each type of part of the tower is divided into multiple positional elements. All positional elements of each type of part are used as nodes. Calculate the dynamic response data of each node over the entire set time history. Based on the dynamic response data of each node over the entire time history, calculate the maximum equivalent stress, root mean square of equivalent stress, stress gradient, peak acceleration, and root mean square of acceleration for each node over the entire time history.

[0078] Specifically, in this embodiment, a three-dimensional numerical analysis model of the tower is established by performing finite element analysis on the tower based on its geometric configuration parameters, material mechanical properties, and boundary constraints using finite element software. The geometric configuration parameters of the tower include the total tower height, the height of each tower segment, the length of the crossarm, the cross-sectional dimensions of the main and diagonal members, the spatial coordinates of the component connection nodes, and the topological connection relationships of each component. The material mechanical properties of the tower include the elastic modulus, Poisson's ratio, density, yield strength, and damping ratio. The boundary constraints include the constraint method at the connection between the tower base and the foundation, the displacement constraints of each support node, and the location and mode of load application.

[0079] In this embodiment, the tower is divided into multiple parts based on its geometric configuration using finite element analysis software. In the established three-dimensional numerical analysis model of the tower, each part is discretized into multiple positional elements, thus obtaining all positional elements for each part, with each positional element serving as a node. The various parts of the tower include: tower top crossarm connection parts, conductor suspension point and insulator connection parts, structural corners or abrupt changes in cross-section, main and auxiliary material connection parts, parts with changes in force direction, main tower material parts, diagonal and horizontal material parts, and tower foot foundation connection parts. Among these, the tower top crossarm connection parts, conductor suspension point and insulator connection parts, structural corners or abrupt changes in cross-section, main and auxiliary material connection parts, and parts with changes in force direction are considered key parts.

[0080] In this embodiment, the structural dynamics control equations of the tower are constructed based on the three-dimensional numerical analysis model using finite element analysis software, as shown in the following formula:

[0081]

[0082] In the formula: , , These are the overall mass matrix, damping matrix, and stiffness matrix of the tower structure, respectively. , , These are the displacement, velocity, and acceleration response vectors of the node, respectively. Let be the total external load vector acting on the tower structure at time t. The total external load vector is composed of wind load, icing load, and temperature stress load.

[0083] Finite element analysis using finite element software was employed to apply various time-varying external loads (including wind load, icing load, and temperature stress load) to the three-dimensional numerical analysis model of the tower over a set time history [0, T]. This caused dynamic responses at each node. The Newmark-β method was then used in the finite element software to perform time history analysis on the constructed structural dynamics control equations, obtaining dynamic response data for each node at various moments within the set time history [0, T]. This included data for any node i at any time t within the entire time history [0, T]. k Displacement response data u at time t i (t k ), speed response data i (t k ), acceleration response data i (t k ), stress tensor response data σ i (t k), where k=1,2,3,…,N t N t This represents the total number of moments contained within the entire time span [0, T].

[0084] Furthermore, through finite element analysis using finite element software, and based on the Von Mises yield criterion, the equivalent stress response data of each node at each moment within the entire time history [0, T] are calculated, as shown in the following formula:

[0085]

[0086] In the formula: For any node i, during any time t in the entire time history [0, T] k Equivalent stress response data at time t; σ1, σ2, and σ3 are the values ​​of node i at time t. k The three principal stress components at time t are the three normal stress components of the stress tensor at that time in the principal stress coordinate system.

[0087] In this embodiment, the maximum equivalent stress, root mean square of equivalent stress, stress gradient, peak acceleration, and root mean square of acceleration of each node in the time history [0, T] are calculated by finite element analysis using finite element software, based on the equivalent stress response data and acceleration response data of each node at each moment in the entire time history [0, T].

[0088] The formula for calculating the maximum equivalent stress of each node within the time history [0, T] is as follows:

[0089]

[0090] In the formula: It represents the maximum equivalent stress of any node i over the entire time course [0, T].

[0091] The root mean square of the equivalent stress at each node within the time history [0, T] is used to characterize the degree of stress fluctuation. The formula for calculating the root mean square of the equivalent stress is as follows:

[0092]

[0093] In the formula: Let represent the root mean square of the equivalent stress at any node i over the entire time course [0, T].

[0094] The formula for calculating the stress gradient of each node within the time history [0, T] is as follows:

[0095]

[0096] In the formula: This represents the stress gradient of any node i over the entire time history [0, T]. Let i be the set of neighboring nodes of node i; Let be the spatial distance between node i and any adjacent node j; Let be the maximum equivalent stress of any adjacent node j of node i during the entire time history [0, T].

[0097] The formula for calculating the peak acceleration of each node within the time history [0, T] is as follows:

[0098]

[0099] In the formula: Let represent the peak acceleration of any node i over the entire time course [0, T].

[0100] The formula for calculating the root mean square acceleration of each node within the time history [0, T] is as follows:

[0101]

[0102] In the formula: Let represent the root mean square of the acceleration of any node i over the entire time course [0, T].

[0103] Therefore, in this embodiment, by performing finite element analysis on the tower, the maximum equivalent stress, root mean square of equivalent stress, stress gradient, peak acceleration, and root mean square of acceleration of each node of the tower within the time history [0, T] are obtained, providing a data basis for subsequent screening and evaluation. Furthermore, in this embodiment, the spatial coordinate information and component type information (representing the tower part category to which the corresponding node belongs) of each node are obtained through finite element software.

[0104] Step S2: Based on the maximum equivalent stress, root mean square acceleration, and stress gradient of each node in the time history [0, T] obtained in Step S1, judge each node according to the set multiple screening criteria, and select multiple nodes as candidate nodes according to the judgment results of each node under the multiple screening criteria, and form a candidate monitoring node set from each node.

[0105] In this embodiment, to reduce the complexity of subsequent optimization calculations, all nodes of the three-dimensional numerical analysis model are screened to establish a candidate monitoring node set. The screening is based on multiple screening criteria to ensure that the selected candidate nodes include both high-stress vulnerable areas and dynamically sensitive locations with significant vibration responses.

[0106] The multiple screening criteria in this embodiment include screening criteria one, two, and three, which are specifically described below:

[0107] (A1) Screening criterion one, which is based on stress level as the judgment criterion for each node.

[0108] Set equivalent stress threshold The equivalent stress threshold Based on the yield strength of the tower material Confirmed, as shown in the following formula:

[0109]

[0110] In the formula: The stress screening coefficient is set to 0.3.

[0111] Using equivalent stress threshold As the first criterion for selection, the maximum equivalent stress of each node within the time history [0, T] is compared with the equivalent stress threshold. Compare the maximum equivalent stress at any node i within the time history [0, T]. satisfy If the condition is met, then the result is that node i satisfies screening criterion one.

[0112] (A2) Screening criterion two is based on dynamic response as the judgment benchmark for each node.

[0113] Set acceleration response threshold Acceleration response threshold Based on the root mean square horizontal of the average acceleration of the tower structure Confirmed, as shown in the following formula:

[0114]

[0115]

[0116] In the formula: This represents the total number of nodes in the three-dimensional numerical analysis model of the tower. The dynamic screening coefficient is set to 1.3.

[0117] acceleration response threshold As the second screening criterion, the root mean square of acceleration of each node in the time history [0, T] is compared with the acceleration response threshold. Compare the root mean square acceleration of any node i within the time history [0, T]. satisfy If the condition is met, then the result is that node i satisfies screening criterion two.

[0118] The second screening criterion is based on the root mean square value of acceleration rather than the peak value, which can more accurately reflect the continuous vibration energy level of the node and avoid the random influence of instantaneous peak values.

[0119] (A3) The third screening criterion is based on the stress gradient as the judgment criterion for each node.

[0120] Set stress gradient threshold Stress gradient threshold The stress gradient of all nodes is determined based on its statistical characteristics, as shown in the following formula:

[0121]

[0122] In the formula: The average stress gradient is obtained by calculating the arithmetic mean of the stress gradients of all nodes in the time history [0, T]. The standard deviation of the stress gradient; The weighting coefficient is 0.8.

[0123] With stress gradient threshold As the third criterion for selection, the stress gradient of each node in the time history [0, T] is compared with the stress gradient threshold. Row comparison. When the stress gradient of any node i in the time history [0, T] satisfy If the condition is met, then the result is that node i satisfies the third screening criterion.

[0124] In this embodiment, after obtaining the judgment results of each node under multiple screening criteria, namely screening criteria one, two, and three, candidate nodes are directly selected from each node based on the judgment results; or a comprehensive binary indicator function value is calculated based on the judgment results, and then candidate nodes are selected from each node based on the comprehensive binary indicator function value.

[0125] In this embodiment, during direct screening, the decision to retain a node as a candidate node is made based on the judgment results of each node under screening criteria one, two, and three. If any node i satisfies at least two of the screening criteria under screening criteria one, two, and three, then node i is selected as a candidate node. Thus, multiple nodes are selected from each node as candidate nodes, and these candidate nodes form a candidate monitoring node set S. cand ={n1,n2,…,n M}, where M is the total number of candidate nodes, n1, n2, ..., n M These are the candidate nodes.

[0126] In this embodiment, when filtering based on the comprehensive binary indicator function value, the comprehensive binary indicator function value of the corresponding node is calculated according to the judgment results under multiple filtering criteria for each node. Then, the comprehensive binary indicator function value of each node is used as the comprehensive filtering index value, and filtering is performed based on the comprehensive filtering index value of each node. Specifically, based on the judgment results of any node i under filtering criteria one, two, and three, a binary indicator function I is constructed for the judgment results of node i under filtering criteria one, two, and three, respectively. σ (i), I a (i), I G (i). Among them, I σ (i) is a binary indicator function representing the judgment result of node i under screening criterion one. When node i satisfies screening criterion one, I σ (i) is 1, otherwise I σ (i) is 0. I a (i) is a binary indicator function representing the result of node i under screening criterion two. When node i satisfies screening criterion two, I a (i) is 1, otherwise I a (i) is 0. I G (i) is a binary indicator function representing the result of node i under screening criterion three. When node i satisfies screening criterion three, I G (i) is 1, otherwise I G (i) is 0.

[0127] Then, based on the binary indicator function of each node's judgment result under multiple screening criteria, i.e., the binary indicator function of the judgment result under screening criteria one, two, and three, the weighted sum of the binary indicator function values ​​of the judgment result under screening criteria one, two, and three is calculated as the comprehensive binary indicator function value of the corresponding node, as shown in the following formula:

[0128]

[0129] In the formula: Let i be the value of the composite binary indicator function for any node i; , , These are the weight coefficients of the binary indicator functions representing the judgment results under screening criteria one, two, and three, respectively, satisfying... + + =1, , , The preferred value is =0.5、 =0.3、 =0.2.

[0130] Finally, the comprehensive binary indicator function value of each node is used as the comprehensive screening index value, and the comprehensive screening index value of each node is compared with the set comprehensive screening index threshold Ф. th The comparison is performed when the combined binary indicator function value of any node i is... satisfy ≥Ф th If a node i is selected as a candidate node, then the selected nodes are further selected as candidate nodes, and the candidate monitoring node set S is formed from these candidate nodes. cand ={n1,n2,…,n M}, where M is the total number of candidate nodes, n1, n2, ..., n M These are the candidate nodes.

[0131] In this embodiment, the comprehensive screening index threshold Ф th The value of 0.65 is chosen to ensure that the final selected candidate nodes perform well in at least two main selection criteria, or perform well in all three selection criteria, thereby ensuring the monitoring value of the final candidate nodes.

[0132] Step S3: Quantitatively evaluate the importance of each candidate node in the candidate monitoring node set obtained in Step S2 to obtain a comprehensive importance score for each candidate node. Then, based on the comprehensive importance score, sort the candidate nodes in the candidate monitoring node set in descending order to obtain a candidate node importance ranking table.

[0133] In this embodiment, a comprehensive importance scoring system is established based on the feature information of the node, such as stress level score, dynamic response score, stress gradient score, and structural location additional score, to quantitatively evaluate the comprehensive importance of each candidate node and obtain the comprehensive importance score of each candidate node, which serves as a quantitative indicator of the monitoring value of each candidate node.

[0134] For any candidate node i, its comprehensive importance score It consists of four components, as shown in the following formula:

[0135]

[0136] In the formula: Score the stress level of candidate node i; Score the dynamic response of candidate node i; The stress gradient score for candidate node i is calculated. Add a score to the structural position of candidate node i.

[0137] Among them, the stress level score of any candidate node i Based on the maximum equivalent stress of candidate node i within the time history [0, T] Confirmed, as shown in the following formula:

[0138]

[0139] In the formula: The stress level score represents the material's yield strength. This stress level score reflects the principle that the higher the stress level at each candidate node, the greater the risk of fatigue damage and the higher its monitoring value.

[0140] Dynamic response score of any candidate node i The root mean square acceleration and peak acceleration of candidate node i within the time history [0, T] are determined by a comprehensive consideration, as shown in the following formula:

[0141]

[0142] In the formula: This is the weighting coefficient, set to 0.6; The response score for candidate node i is determined based on the root mean square of acceleration. The response score for candidate node i is determined based on peak acceleration. and The following formulas are shown respectively:

[0143]

[0144]

[0145] In the formula: For any candidate node other than candidate node i The root mean square of the acceleration over the time interval [0, T]; For any candidate node other than candidate node i The peak acceleration within the time history [0, T]. Where j is the candidate node number in the candidate monitoring node set, and j≠i.

[0146] Regions with large stress gradients are stress concentration areas and potential crack initiation sites; therefore, the stress gradient score for any candidate node i... The stress gradient of candidate node i within the time history [0, T] is determined as shown in the following equation:

[0147]

[0148] In the formula: For any candidate node other than candidate node i Stress gradient over time [0, T].

[0149] Additional score for the structural position of any candidate node i This is an additional score given to candidate nodes in key parts of the tower based on engineering experience and statistical patterns of structural damage. The specific additional score for the structural location is... The determination of key locations is based on whether the candidate node belongs to a critical component and the category of critical component to which the candidate node belongs. Key components include tower top crossarm connections, conductor suspension points and insulator connections, structural corners or abrupt changes in cross-section, connections between main and auxiliary materials, and changes in stress direction. Additional scoring is shown in the following formula:

[0150]

[0151] In the formula: The number of categories for key components in the tower;

[0152] It is a binary indicator function, when candidate node When not a critical part The value is 0 when the candidate node is... Belongs to any number When it comes to critical parts The value is 1, as shown in the following formula:

[0153]

[0154] This is an additional score for the m-th type of critical component, specifically determined as follows: when the critical component to which node i belongs is a crossarm connection component at the top of the tower, then... Take 15; when the critical part of candidate node i is a conductor suspension point or an insulator connection point, then Take 12; when the critical part to which candidate node i belongs is a structural corner or abrupt cross-sectional change, then Take 10; when the key part to which candidate node i belongs is a part connecting main material and auxiliary material, then Then take 8; when the critical part to which candidate node i belongs is a part with a change in force direction, then Take 6.

[0155] Therefore, by using a comprehensive scoring system, combining the stress level score, dynamic response score, stress gradient score, and structural location additional score of any candidate node i, the comprehensive importance score R of candidate node i is obtained. i The candidate monitoring node set S cand All candidate nodes are sorted in descending order of their overall importance scores to obtain the candidate node importance ranking table S. ranked ={n (1) ,n (2),…,n (M)}, where n (1) , n (2) ,…, n (M) are the candidate nodes sorted according to the comprehensive importance score, and M is the total number of candidate nodes.

[0156] Step S4: Based on the comprehensive importance scores of each candidate node, combined with the total number of vibration monitoring sensors to be arranged, select multiple candidate nodes from the candidate node importance ranking table obtained in step S3 as the initial monitoring points respectively, and form an initial monitoring point set by each initial monitoring point. Then, based on the spatial distribution constraint and the comprehensive importance score, select the remaining candidate nodes from the candidate node importance ranking table to iteratively update the initial monitoring point set, thereby obtaining a final monitoring point set that simultaneously satisfies the maximization of importance and the rationalization of spatial distribution, and each candidate node in the final monitoring point set is used as the final monitoring point.

[0157] In this embodiment, considering comprehensively the importance of each candidate node and the rationality of the spatial distribution during the arrangement of vibration monitoring sensors, select multiple candidate nodes that simultaneously meet the two goals of maximizing importance and rationalizing spatial distribution from each candidate node as the final monitoring points, thereby obtaining a final monitoring point set.

[0158] Specifically, in this embodiment, according to the budget constraint and sensor resources of the monitoring system, determine the total number K (K << M) of vibration monitoring sensors (types include strain sensors and acceleration sensors) to be arranged. In this embodiment, K is taken as 12. Select the first K candidate nodes in the sorting order of the comprehensive importance score from the candidate node importance ranking table Sranked, and use the total of K selected candidate nodes as the initial monitoring points respectively, and form an initial monitoring point set S by each initial monitoring point. init . And, form the remaining candidate node set S by keeping the order unchanged of the remaining (K + 1) - th to M - th candidate nodes in the candidate node importance ranking table Sranked. rm .

[0159] Then, in order to avoid over - concentration or omission of key areas in the arrangement of vibration monitoring sensors, define the minimum spatial spacing constraint d min as the spatial distribution constraint, and based on the minimum spatial spacing constraint d min check the spatial distribution rationality of each initial monitoring point in the initial monitoring point set S init , and select the remaining candidate nodes from the remaining candidate node set S rm according to the comprehensive importance score to update the initial monitoring point set S init , thereby iteratively updating the initial monitoring point set S initThrough iterative updates, the final monitoring point set S is obtained. final Minimum spatial spacing constraint d min The value is determined based on the characteristic dimensions of the tower, and is taken as 4% of the tower height. In this embodiment, the tower height is 70m, therefore the minimum spatial spacing constraint d min Take 2.8 m.

[0160] like Figure 2 As shown, for the initial monitoring point set S init Perform iterative updates to determine the final monitoring point set S final The process is as follows:

[0161] B1. Currently, starting from the initial monitoring point set S init Two distinct candidate nodes are randomly selected to form a node pair. Let the two selected candidate nodes be candidate node n. i and n j And determine the two candidate nodes n currently selected. i and n j Has it been selected before?

[0162] If the two candidate nodes n selected at the moment i and n j If all nodes have already been selected, then two different candidate nodes will be randomly selected to form a node pair.

[0163] If the two candidate nodes n selected at the moment i and n j If any one of them has not been selected before, then calculate the candidate node n to be selected. i and candidate node n j Spatial Euclidean distance As shown in the following formula:

[0164]

[0165] In the formula: , , Candidate node n i The x, y, and z axis coordinates are obtained by finite element software in step S1; , , Candidate node n j The x, y, and z axis coordinates are obtained by finite element software in step S1.

[0166] B2. Select candidate node n i and n j Spatial Euclidean distance With minimum spatial spacing constraint d min Compare them.

[0167] When the comparison result is d ij <d min When this happens, candidate node n is considered... i and candidate node n j The spatial locations of candidate nodes n are too close, resulting in monitoring redundancy. i and candidate node n j Candidate node n does not meet spatial distribution constraints i and candidate node n j The resulting node pair violates the spatial constraints, and the subsequent step B3 is executed.

[0168] When the comparison result is d ij d min When this happens, candidate node n is considered... i and candidate node n j The spatial location satisfies the spatial distribution constraints, and the process returns to step B1.

[0169] B3. Compare candidate nodes n among node pairs that violate spatial distribution constraints. i and candidate node n j Overall Importance Score R i and R j Two candidate nodes n in a pair of nodes that violate spatial distribution constraints i and n j The candidate node with the highest overall importance score is selected as the candidate node to be retained (n). keep Two candidate nodes n in a pair of nodes that violate spatial distribution constraints i and n j The candidate node with the lower overall importance score is selected as the replacement candidate node n. rep .

[0170] B4. Currently, from the remaining candidate node set S rm Among the remaining candidate nodes, the remaining candidate node with the highest comprehensive importance score is selected as the current candidate node, and it is determined whether there is one that has already been added to the initial monitoring point set S. init The historical candidate nodes.

[0171] If it did not exist before and has already been added to the initial monitoring point set S init If the historical candidate nodes are selected, then the current candidate node will be added to the initial monitoring point set S. init In the middle, replace the initial monitoring point set S init Candidate nodes n to be replaced in node pairs that violate spatial distribution constraints rep And the initial monitoring point set S initAmong the node pairs that violate spatial distribution constraints, the candidate node n to be retained is... keep Thus, the updated initial monitoring point set S is obtained. init .

[0172] If it already exists and has been added to the initial monitoring point set S init If the historical candidate nodes are selected, then the current candidate node and the nodes already added to the initial monitoring point set S are calculated. init The spatial distance between each historical candidate node, and the minimum spatial spacing constraint d. min Comparison:

[0173] If the spatial distance between the current selected candidate node and any historical selected candidate node is less than the minimum spatial spacing constraint d min If the current candidate node is not selected, then the node selected in the remaining candidate node set S is discarded. rm After discarding the currently selected candidate nodes, step B4 is executed again to reselect candidate nodes.

[0174] If the spatial distance between the current selected candidate node and all historical selected candidate nodes is greater than or equal to the minimum spatial spacing constraint d min Then, the currently selected candidate node will be added to the initial monitoring point set S. init In the middle, replace the initial monitoring point set S init Candidate nodes n to be replaced in node pairs that violate spatial distribution constraints rep And the initial monitoring point set S init Among the node pairs that violate spatial distribution constraints, the candidate node n to be retained is... keep Thus, the updated initial monitoring point set S is obtained. init .

[0175] In the remaining candidate node set S rm Remove those already added to the initial monitoring point set S init The selected candidate nodes are used to obtain the updated set of remaining candidate nodes S. rm .

[0176] B5. Repeat steps B1-B4 above to iteratively update the initial monitoring point set S. init Until the updated initial monitoring point set S init The spatial Euclidean distance between any two nodes is greater than or equal to the minimum spatial spacing constraint d. min If the iteration stops, the iteration process ensures that the final solution achieves an optimal balance between importance and spatial distribution.

[0177] Output the initial set of monitoring points S when the iteration stops. init As the final monitoring point set S finalThe final monitoring point set S final Each candidate node in the dataset is used as a final monitoring point, and the final monitoring point set S is... final As shown in the formula below:

[0178]

[0179] Therefore, in step S4 of this embodiment, based on the candidate node importance ranking table obtained in step S3, a final monitoring point set that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution can be obtained. Each final monitoring point (i.e., candidate node) in this final monitoring point set is used to configure vibration monitoring sensors, and the spatial coordinates of each final monitoring point have been obtained in step S1.

[0180] As an improvement to step S4, this embodiment also constructs a tower spatial coverage uniformity index when arranging vibration monitoring sensors according to the final monitoring point set. Based on the tower spatial coverage uniformity index, the spatial coverage of the final monitoring point set that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution is further evaluated. The final monitoring points in the final monitoring point set are adjusted according to the evaluation results to obtain a final monitoring point set that meets the spatial coverage requirements. Each final monitoring point in the final monitoring point set that meets the spatial coverage requirements is used to configure vibration monitoring sensors. The spatial coverage evaluation process is as follows:

[0181] To evaluate the spatial coverage effect of the final monitoring point set, this embodiment defines a uniformity index for the spatial coverage of the towers when vibration monitoring sensors are arranged according to the final monitoring point set.

[0182] The tower structure is divided into a total of N along the height direction. z In this embodiment, there are N segments. z =5, the height of each segment is ΔH=H tower / N z =14m, where H tower H represents the tower height in this embodiment. tower =70m. When deploying vibration monitoring sensors according to the final monitoring point set, the monitoring coverage rate of the l-th height segment is defined as the ratio of the number of vibration monitoring sensors actually deployed in that segment to the total number of candidate nodes in that segment, as shown in the following formula:

[0183]

[0184] In the formula: The number of vibration monitoring sensors actually deployed in the section at the l-th height is the number of vibration monitoring sensors that need to be allocated to each final monitoring point in the section at the l-th height, based on the final monitoring point set. Let be the total number of candidate nodes within the segment at height l. Based on the candidate node importance ranking table, and using the spatial coordinate information of each candidate node in the ranking table, the total number of all candidate nodes whose spatial coordinate information belongs to the segment at the l-th altitude is obtained. .

[0185] When defining the overall spatial coverage of each section when arranging vibration monitoring sensors according to the final monitoring point set, As shown in the following formula:

[0186]

[0187] The coefficient of variation is the uniformity index of tower spatial coverage when vibration monitoring sensors are arranged according to the final monitoring point set. As shown in the following formula:

[0188]

[0189] The smaller the CV value of the pole space coverage uniformity index, the more uniform the distribution of vibration monitoring sensors along the height direction is when the vibration monitoring sensors are arranged according to the final monitoring point set.

[0190] Set the coverage uniformity index threshold CV th In this embodiment, CV th Set the value to 0.5. Compare the pole / tower spatial coverage uniformity index CV with the coverage uniformity index threshold CV. th Compare them.

[0191] If CV CV th This indicates that when vibration monitoring sensors are arranged according to the final monitoring point set, if the vibration monitoring sensors are evenly distributed along the height direction, then the final monitoring point set will meet the spatial coverage requirements.

[0192] If CV > CV th When the coefficient of variation (CV) exceeds 0.5, it indicates that the distribution of monitoring points along the tower height is uneven, with at least one height segment having a significantly lower monitoring coverage rate than the average, potentially leading to insufficient monitoring coverage in that segment. In this case, the final monitoring point set is rebalanced. The rebalancing process involves replacing at least one final monitoring point located in a segment with higher coverage with a candidate node from the candidate node importance ranking table located in a segment with insufficient coverage, while maintaining the minimum spatial spacing constraint in the adjusted final monitoring point set. This replacement process is repeated until the adjusted spatial coverage uniformity index (CV) is less than or equal to the previous value. th This yields the final set of monitoring points that meet the spatial coverage requirements.

[0193] Step S5: Based on the maximum equivalent stress, root mean square equivalent stress, peak acceleration, and root mean square acceleration of each final monitoring point (i.e., candidate node) in the final monitoring point set obtained in Step S4 over the set time history, determine the type of vibration monitoring sensor that can be configured for each final monitoring point. The final monitoring point set is either the final monitoring point set obtained in Step S4 that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution, or the final monitoring point set obtained in Step S4 that satisfies the spatial coverage requirements. The specific process is as follows:

[0194] For any i-th final monitoring point in the final monitoring point set, if the maximum equivalent stress of the i-th final monitoring point... Root mean square of equivalent stress ,satisfy or Then, the i-th final monitoring point can be configured with a strain sensor (such as a resistance strain gauge or fiber optic strain sensor) from the vibration monitoring sensors. The installation direction of the strain sensor should be consistent with the principal stress direction of the i-th final monitoring point, and the principal stress direction angle θ of the i-th final monitoring point should be... i It can be extracted from the numerical analysis results of the finite element method in step S1.

[0195] For any i-th final monitoring point in the final monitoring point set, if the peak acceleration of the i-th final monitoring point is... Root mean square of acceleration ,satisfy or ,in If the acceleration is due to gravity, then the i-th final monitoring point can be configured with an acceleration sensor (such as a piezoelectric or MEMS acceleration sensor) from the vibration monitoring sensors. The acceleration sensor should be configured for triaxial measurement to capture the vibration response of the i-th final monitoring point in the x, y, and z directions.

[0196] For any i-th final monitoring point in the final monitoring point set, if the i-th final monitoring point simultaneously satisfies the characteristics of high stress and high dynamic response, that is, the maximum equivalent stress of the i-th final monitoring point... Root mean square of acceleration ,satisfy and Then, the i-th final monitoring point can be configured with a comprehensive monitoring unit that integrates strain and acceleration sensors for measurement, so as to realize the synchronous monitoring of static stress and dynamic vibration.

[0197] In this embodiment, after determining the type of vibration monitoring sensor that can be configured for each final monitoring point, the sampling frequency and range parameters of the corresponding type of vibration monitoring sensor are then configured.

[0198] Specifically, for strain gauge sensors, the range configuration is as follows: The sampling frequency is configured as f s =50 Hz (for static monitoring) or f s =200 Hz (for dynamic monitoring), resolution configured as follows .

[0199] For accelerometers, the measurement range is configured to ±5g, and the sampling frequency is configured to f. s =500 Hz (f s ≥2f max , where f max (The highest mode of interest for the structure), with a resolution of 0.001g.

[0200] Therefore, according to the method and process of this embodiment, the optimized layout of each final monitoring point on the tower for arranging vibration monitoring sensors is obtained, as well as the type of vibration monitoring sensor that can be configured for each final monitoring point, and the sampling frequency and range parameters of each type of vibration monitoring sensor. When arranging vibration monitoring sensors on the tower, the corresponding type of vibration monitoring sensor is arranged according to the spatial coordinates of each final monitoring point, and the sampling frequency and range parameters of the corresponding type of vibration monitoring sensor are configured.

[0201] This embodiment also discloses an electronic device, which includes a processor and a memory. The processor can read and execute program instructions stored in the storage medium of the memory. These program instructions include a finite element analysis module, a candidate node screening module, a comprehensive importance quantitative evaluation module, a final monitoring point set generation module, and a sensor configuration module. Wherein:

[0202] When the program instructions are read and run, the finite element analysis module executes the finite element analysis process of step S1 of the above-mentioned transmission tower vibration monitoring sensor layout optimization method through finite element software, so as to obtain the maximum equivalent stress, root mean square of equivalent stress, stress gradient, peak acceleration, and root mean square of acceleration of each node in the whole time history.

[0203] When the program instructions are read and run, the candidate node screening module executes the calculation and judgment process of multiple screening criteria in step S2 of the above-mentioned transmission tower vibration monitoring sensor layout optimization method, so as to screen multiple nodes from each node as candidate nodes to form a candidate monitoring node set.

[0204] When the program instructions are read and run, the comprehensive importance quantitative evaluation module executes the comprehensive importance score calculation process and sorting process in step S3 of the above-mentioned transmission tower vibration monitoring sensor layout optimization method to obtain a candidate node importance ranking table.

[0205] When the program instructions are read and executed, the final monitoring point set generation module performs the process of sorting and selecting the initial monitoring point set from the candidate node importance ranking table in step S4 of the above-mentioned transmission tower vibration monitoring sensor layout optimization method, as well as the iterative update process of the initial monitoring point set, to obtain the final monitoring point set that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution. The final monitoring point set generation module also performs the process of calculating the tower spatial coverage uniformity index of the final monitoring point set that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution in step S4 of the above-mentioned transmission tower vibration monitoring sensor layout optimization method, the process of comparing and evaluating the spatial coverage of the final monitoring point set, and the process of adjusting the final monitoring point set, to obtain the final monitoring point set that meets the spatial coverage requirements.

[0206] When the program instructions are read and run, the sensor configuration module executes the comparison and judgment process of each final monitoring point in the final monitoring point set in step S5 of the above-mentioned transmission tower vibration monitoring sensor layout optimization method, so as to determine the type of vibration monitoring sensor that can be configured for each final monitoring point.

[0207] Therefore, when the program instructions in the memory's storage medium are read and run by the processor, steps S1-S5 of the above-mentioned method for optimizing the layout of transmission tower vibration monitoring sensors are executed.

[0208] This embodiment also discloses a storage medium storing program instructions, including the aforementioned finite element analysis module, candidate node screening module, comprehensive importance quantitative evaluation module, final monitoring point set generation module, and sensor configuration module. When the program instructions in the storage medium are read and run by one or more processors, steps S1-S5 of the above-described transmission tower vibration monitoring sensor layout optimization method are executed.

[0209] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. These embodiments are merely descriptions of preferred embodiments and are not intended to limit the scope or concept of the invention. The specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. Such combinations, as long as they do not violate the spirit of the present invention, should also be considered as part of this disclosure. To avoid unnecessary repetition, the present invention will not further describe the various possible combinations.

[0210] This invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this invention and without departing from the design idea of ​​this invention, all modifications and improvements made by those skilled in the art to the technical solutions of this invention should fall within the protection scope of this invention. The technical content for which protection is sought in this invention has been fully described in the claims.

Claims

1. A method for optimizing the layout of vibration monitoring sensors for power transmission towers, characterized in that, The process is as follows: Each type of part of the tower is divided into multiple position units, and each position unit is used as a node. Based on the dynamic response of each node under a set time history and external load, the maximum equivalent stress, stress gradient, peak acceleration, and root mean square acceleration of each node within the time history are calculated. Based on the maximum equivalent stress, root mean square acceleration, and stress gradient of each node over the time history, multiple candidate nodes are selected from each node according to multiple screening criteria. Calculate the overall importance score for each candidate node, sort the candidate nodes in descending order based on the overall importance score, and form a candidate node importance ranking table from the sorted candidate nodes. Based on the comprehensive importance score and the total number of vibration monitoring sensors that need to be deployed, multiple candidate nodes are selected from the candidate node importance ranking table to form an initial monitoring point set; Then, based on spatial distribution constraints and comprehensive importance scores, the remaining candidate nodes are selected from the candidate node importance ranking table to iteratively update the initial monitoring point set. The iteratively updated initial monitoring point set is used as the final monitoring point set that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution. Each candidate node in the final monitoring point set is used as a final monitoring point, and each final monitoring point is used to arrange vibration monitoring sensors, thereby completing the layout optimization of vibration monitoring sensors.

2. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to claim 1, characterized in that, By performing finite element analysis on the tower, a three-dimensional numerical analysis model of the tower and the structural dynamics control equations are established. In the three-dimensional numerical analysis model, each type of part of the tower is discretized into multiple positional elements as nodes. Multiple time-varying external loads are applied to the three-dimensional numerical analysis model of the tower to induce a dynamic response at each node. By performing time history analysis on the structural dynamics control equations, dynamic response data of each node at each moment within the set time history are obtained, including acceleration response data and stress tensor response data. Based on the stress tensor response data of any node at any time during the entire time history, the maximum equivalent stress, root mean square equivalent stress, and stress gradient of that node during the entire time history are calculated. Based on the acceleration response data of any node at any time point throughout the entire time history, the peak acceleration and root mean square acceleration of that node throughout the entire time history are calculated.

3. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to claim 1, characterized in that, The first screening criterion is formed by using the equivalent stress threshold as the judgment criterion, the second screening criterion is formed by using the acceleration response threshold as the judgment criterion, and the third screening criterion is formed by using the stress gradient threshold as the judgment criterion. Thus, multiple screening criteria are obtained. When the maximum equivalent stress of any node over the entire time history is greater than or equal to the equivalent stress threshold, the result is that the node meets screening criterion one. When the root mean square of acceleration of any node over the entire time history is greater than or equal to the acceleration response threshold, the result is that the node satisfies screening criterion two. If the stress gradient of any node is greater than or equal to the stress gradient threshold throughout the entire time history, then the node is judged to meet the third screening criterion. If any node satisfies at least two of the three screening criteria, then that node is selected as a candidate node. Alternatively, calculate the weighted sum of the binary indicator function values ​​of the judgment results of each node under screening criteria one, two, and three, and use this sum as the comprehensive binary indicator function value of the corresponding node; compare the comprehensive binary indicator function value of each node with the set comprehensive screening index threshold, and select the node as a candidate node when the comprehensive binary indicator function value of any node is greater than or equal to the comprehensive screening index threshold.

4. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to claim 1, characterized in that, A comprehensive importance scoring system is established based on the stress level score, dynamic response score, stress gradient score, and structural location score of each node. A quantitative comprehensive importance evaluation is performed on each candidate node to obtain its comprehensive importance score. Wherein: The stress level score for each candidate node is determined based on the maximum equivalent stress of the corresponding candidate node over the entire time history. The dynamic response score of each candidate node is determined based on the root mean square acceleration and peak acceleration of the corresponding candidate node over the entire time history. The stress gradient score for each candidate node is determined based on the stress gradient of the corresponding candidate node over the entire time history. Each candidate node's structural location is assigned a score, which is determined based on whether the corresponding candidate node belongs to a critical part and the critical part category to which the candidate node belongs; Finally, the sum of the stress level score, dynamic response score, stress gradient score, and structural location additional score for each candidate node is calculated to obtain the comprehensive importance score of the corresponding candidate node.

5. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to claim 1, characterized in that, Let K be the total number of vibration monitoring sensors to be deployed. The top K candidate nodes are selected from the candidate node importance ranking table in descending order. These K candidate nodes serve as initial monitoring points, forming an initial monitoring point set. The remaining candidate nodes in the candidate node importance ranking table, in their original order, form the remaining candidate node set. Then, using the minimum spatial spacing constraint as the spatial distribution constraint, and based on the minimum spatial spacing constraint and the comprehensive importance score, the remaining candidate nodes are selected from the remaining candidate node set to iteratively update the initial monitoring point set. The iteratively updated initial monitoring point set serves as the final monitoring point set, simultaneously satisfying both maximizing importance and rationalizing spatial distribution. The iterative update process is as follows: B1. Currently, two different candidate nodes are randomly selected from the initial monitoring points to form a node pair. It is then determined whether the two candidate nodes selected have been selected before. If the two candidate nodes currently selected have both been selected before, then two different candidate nodes will be randomly selected again to form a node pair. If either of the two candidate nodes currently selected has not been selected before, then calculate the spatial Euclidean distance between the two candidate nodes. B2. Compare the spatial Euclidean distance between the two selected candidate nodes with the minimum spatial spacing constraint; If the spatial Euclidean distance is less than the minimum spatial spacing constraint, then the node pair consisting of the two selected candidate nodes is a node pair that violates the spatial constraint, and the subsequent step B3 is executed. If the spatial Euclidean distance is greater than or equal to the minimum spatial spacing constraint, return to step B1. B3. Compare the comprehensive importance scores of the two candidate nodes in the node pair that violates the spatial distribution constraints. The candidate node with the higher comprehensive importance score is selected as the candidate node to be retained, and the other candidate node with the lower comprehensive importance score is selected as the candidate node to be replaced. B4. From the remaining candidate nodes in the remaining candidate node set, select the remaining candidate node with the highest comprehensive importance score as the current candidate node, and determine whether there are any historical candidate nodes that have been added to the initial monitoring point set. If there are no previously selected candidate nodes that have been added to the initial monitoring point set, the current selected candidate node is added to the initial monitoring point set to replace the candidate nodes to be replaced in the node pairs that violate the spatial distribution constraints in the initial monitoring point set. The candidate nodes to be retained in the node pairs that violate the spatial distribution constraints are retained in the initial monitoring point set, thus obtaining the updated initial monitoring point set. If there are historical candidate nodes that have already been added to the initial monitoring point set, calculate the spatial distance between the current candidate node and each historical candidate node that has already been added to the initial monitoring point set, and compare it with the minimum spatial distance constraint: If the spatial distance between the current candidate node and any historical candidate node is less than the minimum spatial distance constraint, then the current candidate node is abandoned. After removing the abandoned candidate node from the remaining candidate node set, step B4 is re-executed to reselect a candidate node. If the spatial distance between the current candidate node and all historical candidate nodes is greater than or equal to the minimum spatial distance constraint, then the current candidate node is added to the initial monitoring point set, replacing the candidate nodes to be replaced in the node pairs that violate the spatial distribution constraint in the initial monitoring point set, and the candidate nodes to be retained in the node pairs that violate the spatial distribution constraint are retained in the initial monitoring point set, thus obtaining the updated initial monitoring point set; The selected candidate nodes that have been added to the initial monitoring point set are removed from the remaining candidate node set, thus obtaining the updated remaining candidate node set; B5. Repeat steps B1-B4 above to iteratively update the initial monitoring point set until the spatial Euclidean distance between any two nodes in the updated initial monitoring point set is greater than or equal to the minimum spatial spacing constraint, then stop the iteration. The initial set of monitoring points at the point where iteration stops is output as the final set of monitoring points.

6. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to claim 1, characterized in that, Also includes: After obtaining the final monitoring point set that simultaneously satisfies the requirements of maximizing importance and rationalizing spatial distribution, the tower structure is divided into multiple segments along the height direction. Based on the total number of final monitoring points belonging to each segment in the final monitoring point set, the monitoring coverage rate of each segment and the overall spatial coverage of each segment are obtained. Then, based on the monitoring coverage rate of each segment and the overall spatial coverage of each segment, a tower spatial coverage uniformity index is constructed. When the uniformity index of pole and tower spatial coverage is less than or equal to the preset threshold of uniformity index, the final monitoring point set is the final monitoring point set that meets the spatial coverage requirements. When the pole and tower spatial coverage uniformity index is greater than the preset coverage uniformity index threshold, the final monitoring point set is determined to not meet the spatial coverage requirements. At this time, the final monitoring point set is rebalanced. The rebalance adjustment process is as follows: at least one final monitoring point in the final monitoring point set located in the segment with high monitoring coverage is replaced with a candidate node in the candidate node importance ranking table located in the segment with insufficient monitoring coverage, while maintaining the spatial distribution constraints of the replaced final monitoring point set during the replacement process. The above replacement adjustment is repeated until the adjusted pole and tower spatial coverage uniformity index is less than or equal to the preset coverage uniformity index threshold, thus obtaining a final monitoring point set that meets the spatial coverage requirements. When the pole and tower spatial coverage uniformity index is less than or equal to the preset coverage uniformity index threshold, the current final monitoring point set is taken as the final monitoring point set that meets the spatial coverage requirements. Each candidate node in the final monitoring point set that meets the spatial coverage requirements is used as a final monitoring point, and each final monitoring point is used to deploy vibration monitoring sensors, thereby completing the layout optimization of vibration monitoring sensors.

7. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to claim 6, characterized in that, The uniformity of spatial coverage of the tower As shown in the following formula: in, Total number of segments; The monitoring coverage rate of the segment at the l-th altitude; Overall spatial coverage of each section; As shown in the following formula: In the formula: The number of vibration monitoring sensors actually deployed in the section at the l-th height is the number of vibration monitoring sensors that need to be allocated to each final monitoring point in the section at the l-th height, based on the final monitoring point set. Let be the total number of candidate nodes within the segment at height l. Based on the candidate node importance ranking table, and using the spatial coordinate information of each candidate node in the ranking table, the total number of all candidate nodes whose spatial coordinate information belongs to the segment at the l-th altitude is obtained. ; As shown in the following formula: 。 8. The method for optimizing the layout of vibration monitoring sensors for transmission towers according to any one of claims 1-7, characterized in that, Also includes: The root mean square of the equivalent stress at each node over the time history is calculated. Based on the maximum equivalent stress, root mean square of the equivalent stress, peak acceleration, and root mean square of acceleration at each final monitoring point in the final monitoring point set over the entire set of time history, the type of vibration monitoring sensor that can be configured at each final monitoring point is determined, as follows: Let the maximum equivalent stress at any i-th final monitoring point be... The root mean square of the equivalent stress is When satisfied or When the i-th final monitoring point is selected, a strain sensor from the vibration monitoring sensors can be configured, where... The yield strength of the tower material; Let the peak acceleration of any i-th final monitoring point be... The root mean square of acceleration is When satisfied or When the i-th final monitoring point is selected, an acceleration sensor from the vibration monitoring sensors can be configured; where It is the acceleration due to gravity. The acceleration response threshold; When the maximum equivalent stress at any i-th final monitoring point Root mean square of acceleration ,satisfy and When the i-th final monitoring point is configured with both strain and acceleration sensors, it can be configured simultaneously.

9. An electronic device comprising a processor and a memory, characterized in that, The program instructions in the memory are read and executed by the processor to perform the transmission tower vibration monitoring sensor layout optimization method as described in any one of claims 1-8.

10. A storage medium storing program instructions, characterized in that, When the program instructions are read and run, the method for optimizing the layout of transmission tower vibration monitoring sensors as described in any one of claims 1-8 is executed.

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