Three-dimensional sensor optimal arrangement method and system based on genetic algorithm

Abstract

The invention discloses a three-dimensional sensor optimal arrangement method and system based on a genetic algorithm, and relates to the technical field of three-dimensional sensor optimal arrangement, and the method comprises the following steps: building a finite element model; selecting a proper number of candidate measuring points by combining the size of a grid unit in the finite element model, and determining a to-be-selected measuring point set of the sensor; the vibration mode extraction number is determined, and modal information extraction is carried out; based on the mode number selection of the Fisher information matrix, the Fisher information matrix of the structure is constructed by utilizing the mode information of each measuring point of the structure obtained through mode analysis calculation; constructing a three-dimensional sensor optimization arrangement model, and establishing the sensor optimization arrangement model based on a three-dimensional modal confidence criterion; and solving the three-dimensional sensor optimization arrangement model by using a genetic algorithm to obtain a sensor arrangement scheme. According to the method, the arrangement position of the sensor is optimized, so that the recognition precision of the structural modal parameters is remarkably improved, and accurate capture of key modal information is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional sensor optimization layout, and in particular to a three-dimensional sensor optimization layout method and system based on genetic algorithm. Background Art

[0002] As the scale and complexity of modern engineering structures continue to increase, the importance of structural health monitoring (SHM) in ensuring structural safety, extending service life and reducing maintenance costs has become increasingly prominent. Sensor layout is a key link in structural health monitoring. By reasonably arranging sensors, the vibration response data of the structure can be effectively captured, so as to accurately identify the modal parameters of the structure and achieve comprehensive monitoring and evaluation of the structural state. However, most current studies focus on the optimization of single-degree-of-freedom (single-dimensional) sensor layout. Although these studies have achieved certain results in simple structures or two-dimensional planar structures, they have obvious limitations when dealing with complex three-dimensional structures. Single-degree-of-freedom sensor layout schemes often cannot fully capture the multi-degree-of-freedom vibration characteristics in three-dimensional structures, resulting in a decrease in modal identification accuracy and even missing key modal information.

[0003] In addition, in the past, the sensor layout optimization process usually relied on expert experience or simple trial and error methods. The above methods may be effective when the data volume is small or the structure is simple, but with the increasing complexity of modern engineering structures, especially in the sensor layout of three-dimensional structures, traditional methods have gradually revealed their limitations. On the one hand, the vibration modes of complex structures are more diverse, and the variables that need to be considered in sensor layout have also increased significantly, making it difficult for manual experience to cover all possible situations; on the other hand, the sharp increase in data volume also makes it difficult for traditional optimization methods to process and analyze. Summary of the invention

[0004] In view of the problems existing in the existing three-dimensional sensor optimization arrangement and system based on genetic algorithm, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for optimizing the arrangement of three-dimensional sensors based on a genetic algorithm, which comprises the following steps:

[0007] Establish finite element model;

[0008] According to the mesh unit size in the finite element model, an appropriate number of candidate measuring points are selected to determine the sensor candidate measuring point set;

[0009] Determine the number of vibration modes to be extracted and extract modal information;

[0010] Modal number selection based on the Fisher information matrix. Using the modal information of each measurement point of the structure obtained by modal analysis, construct the Fisher information matrix of the structure;

[0011] Construct a three-dimensional sensor optimal placement model. Based on the three-dimensional modal confidence criterion, establish a sensor optimal placement model;

[0012] Use the genetic algorithm to solve the three-dimensional sensor optimal placement model to obtain the sensor placement scheme.

[0013] As a preferred scheme of the three-dimensional sensor optimal placement method based on the genetic algorithm of the present invention, wherein: the method for extracting modal information includes the multiple Ritz vector method, the Lanczos method, and the subspace iteration method;

[0014] Among them, the multiple Ritz vector method is used for the vibration mode analysis of small structures, specifically for vibration mode analysis of less than 10 orders;

[0015] The Lanczos method is used for extracting high-order modes of large and complex structures, specifically for vibration mode analysis of more than 20 orders;

[0016] The subspace iteration method is used for extracting the vibration mode analysis of medium-sized structures, specifically for vibration mode analysis of more than 10 and less than 20 orders.

[0017] As a preferred scheme of the three-dimensional sensor optimal placement method based on the genetic algorithm of the present invention, wherein: calculate the Fisher information matrix reflecting the test sensitivity of the modal vibration mode. The Fisher information matrix is expressed as,

[0018] Q = Φ T Φ;

[0019] In the formula, Q is the modal vibration mode matrix;

[0020] When the target modal number is m (m < n), considering the orthogonality of the modal vibration modes, it can also be expressed as:

[0021]

[0022] As a preferred scheme of the three-dimensional sensor optimal placement method based on the genetic algorithm of the present invention, wherein: calculate the change rate of the 2-norm of the Fisher information matrix between two adjacent orders. The 2-norm of the previous order of the Fisher information matrix is denoted as ||Q i ||2, then the change rate between its previous i-th order and the previous i + 1-th order can be expressed as:

[0023]

[0024] Where n is the total number of modes; i is the number of modes used in the calculation (1≤i≤n-1).

[0025] As a preferred solution of the three-dimensional sensor optimization arrangement method based on genetic algorithm described in the present invention, based on the three-dimensional modal confidence criterion, the three-directional translational degrees of freedom of the node are regarded as a unit, and the expression is:

[0026]

[0027] In the formula, C i,j is the element in the i-th row and j-th column of the three-dimensional modal assurance criterion TMAC, Q i,j It is the element in the i-th row and j-th column of the Fisher information matrix.

[0028] As a preferred solution of the genetic algorithm-based three-dimensional sensor optimization layout method of the present invention, minimizing the maximum non-diagonal element of the TMAC matrix is ​​used as the optimization target of the sensor layout, and the expression is:

[0029]

[0030] As a preferred solution of the three-dimensional sensor optimization layout method based on genetic algorithm described in the present invention, when solving the three-dimensional sensor optimization layout model, the population is first initialized, and the maximum value of the diagonal elements of the TMAC matrix is ​​minimized based on the three-dimensional modal confidence criterion design. One or two crossover points are randomly selected from the parent individuals, and some positions of the offspring are exchanged to ensure that the integers in each position are not repeated. Two positions in an individual are randomly selected, and the integers in the corresponding positions are exchanged to increase the diversity of the population. An elite retention strategy is used to directly retain the individuals with the highest fitness values ​​in the previous generation to the next generation, and the remaining individuals are generated through selection, crossover, and mutation. The maximum number of iterations is set, and the algorithm stops when the number of iterations is reached.

[0031] In a second aspect, an embodiment of the present invention provides a three-dimensional sensor optimization arrangement system based on a genetic algorithm, which includes a grid division module, an optimization algorithm module, and a three-dimensional modeling module;

[0032] The meshing module is used to select an appropriate number of candidate measuring points in combination with the mesh unit size in the finite element model;

[0033] The optimization algorithm module is used to construct a three-dimensional sensor optimization layout model, and use a genetic algorithm to solve the model to obtain a sensor layout plan;

[0034] The three-dimensional modeling module is used to construct a three-dimensional sensor optimization layout model.

[0035] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned genetic algorithm-based three-dimensional sensor optimization layout method is implemented.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned genetic algorithm-based three-dimensional sensor optimization arrangement method is implemented.

[0037] The beneficial effects of the present invention are as follows: by optimizing the arrangement positions of sensors, the recognition accuracy of structural modal parameters is significantly improved, ensuring accurate capture of key modal information; under the premise of ensuring monitoring effects, the number of sensors and arrangement costs are reasonably reduced, achieving efficient use of resources; it can effectively handle multi-degree-of-freedom vibration characteristics in complex three-dimensional structures, and improve the applicability of structural health monitoring systems in complex application scenarios; through the global optimization capability of genetic algorithms, a feasible arrangement scheme is quickly found; compared with traditional manual experience arrangement methods, the design time of sensor arrangement is greatly shortened, and the efficiency of engineering implementation is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0039] Figure 1 Schematic diagram of the three-dimensional sensor optimization layout method based on genetic algorithm.

[0040] Figure 2 This is the ROC value curve of the first 30 modes of a certain structure based on the three-dimensional sensor optimization layout method based on genetic algorithm.

[0041] Figure 3 Schematic diagram of the genetic algorithm for the three-dimensional sensor optimization arrangement method based on genetic algorithm.

[0042] Figure 4 Schematic diagram of the TMAC matrix for the three-dimensional sensor optimization layout method based on genetic algorithm.

[0043] Figure 5 Schematic diagram of genetic algorithm iterative convergence of the genetic algorithm-based three-dimensional sensor optimization layout method. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0048] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] Example 1

[0051] Reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a three-dimensional sensor optimization arrangement method based on a genetic algorithm, comprising the following steps:

[0052] S1. Establish a finite element model.

[0053] Using general finite element software, a finite element model of the proposed sensor structure is established according to the design and construction drawings.

[0054] S2. Based on the mesh unit size in the finite element model, select an appropriate number of candidate measuring points and determine the sensor candidate measuring point set.

[0055] S3. Determine the number of vibration modes to be extracted and perform modal information extraction.

[0056] 2. The methods of modal information extraction include multiple Ritz vector method, Lanczos method and subspace iteration method;

[0057] The multiple Ritz vector method is used for the vibration mode analysis of small structures, specifically for vibration mode analysis of less than 10 orders;

[0058] The Lanczos method is used to extract high-order modes of large and complex structures, specifically more than 20 order vibration mode analysis;

[0059] The subspace iteration method is used to extract the order mode analysis of the medium-sized structure, specifically the order mode analysis greater than 10 and less than 20.

[0060] Select an appropriate mode extraction method according to the actual situation of the sensor structure to be arranged;

[0061] Theoretically, the number of modes can be extracted according to the number of degrees of freedom of the structure. However, in practical applications, the modal order usually does not exceed 30. As the modal order increases, the influence of factors such as noise during data testing will become more significant. In addition, the vibration mode participation coefficient of low-order modes is often higher than that of high-order modes. Therefore, the first 10 to 30 modes are selected as the basic data for subsequent analysis. Table 1 shows the first 30 modal frequencies of a structure.

[0062] Table 1 The first 30 modal frequencies of a structure

[0063]

[0064] S4. Based on the selection of the modal number of the Fisher information matrix, the Fisher information matrix of the structure is constructed using the modal information of each measuring point of the structure obtained by modal analysis.

[0065] Calculate the Fisher information matrix of the test sensitivity of the response structure modal vibration shape. The Fisher information matrix is ​​expressed as,

[0066] Q = Φ T Φ;

[0067] Where Q is the modal shape matrix;

[0068] When the number of target modes is m (m < n), considering the orthogonality of modal shapes, it can also be expressed as:

[0069]

[0070] Calculate the change rate of the 2-norm of the Fisher information matrix between two adjacent orders. The 2-norm of the previous order of the Fisher information matrix is denoted as ||Q i ||₂, then the change rate between its previous i-th order and the previous i + 1-th order can be expressed as:

[0071]

[0072] Where n is the total number of modes; i is the number of modes used in the calculation, 1 ≤ i ≤ n - 1.

[0073] According to the change of ROC, select a suitable one as the index to evaluate the influence of the number of target modes. Figure 2 It is the change situation diagram of the ROC of a certain structure. Taking this diagram as an example, the ROC value changes greatly from 0 to 10 orders, changes gently from 11 to 29 orders, with a small change amplitude, and is near zero value, indicating that the modes after the 11th order have little influence on the tie rod. That is, the modal information of the first 10 orders can basically accurately reflect the structural characteristics of the entire tie rod. Therefore, selecting the first 10 orders of modes for analysis can already meet the requirements of sensor optimal placement.

[0074] S5. Construct a three-dimensional sensor optimal placement model and establish a sensor optimal placement model based on the three-dimensional modal confidence criterion.

[0075] Based on the three-dimensional modal confidence criterion, take the translational degrees of freedom in three directions of a node as a unit, and the expression is:

[0076]

[0077] Where C i,j is the element in the i-th row and j-th column of the three-dimensional modal confidence criterion TMAC, and Q i,j is the element in the i-th row and j-th column of the Fisher information matrix.

[0078] The value of the non-diagonal element in the TMAC matrix indicates the correlation of the modal shapes of each order of nodes. The smaller the value of the non-diagonal element, the more linearly independent and the better the independence of the modal shapes of each order of the selected nodes.

[0079] Minimize the maximum non-diagonal element of the TMAC matrix as the optimization goal of sensor placement, and the expression is:

[0080]

[0081] S6. Use a genetic algorithm to solve the three-dimensional sensor optimization layout model and obtain a sensor layout plan.

[0082] When solving the three-dimensional sensor optimization layout model, the population is initialized first. Based on the three-dimensional modal confidence criterion design, the maximum value of the diagonal elements of the TMAC matrix is ​​minimized. One or two crossover points are randomly selected from the parent individuals, and some positions of the offspring are exchanged to ensure that the integers in each position are not repeated. Two positions in an individual are randomly selected, and the integers in the corresponding positions are exchanged to increase the diversity of the population. Using the elite retention strategy, the individuals with the highest fitness values ​​in the previous generation are directly retained to the next generation, and the remaining individuals are generated through selection, crossover, and mutation. The maximum number of iterations is set, and the algorithm stops when the number of iterations is reached.

[0083] The whole process is as follows Figure 3 The parameter settings of the genetic algorithm are shown in Table 2.

[0084] Table 2 Genetic algorithm parameter settings

[0085]

[0086] Chromosome encoding: integer encoding is used to represent the position of the sensor. Assuming there are n measurement points to be selected in the structure, and m sensors need to be arranged (for example, m = 7), then the gene of each individual (solution) is represented as an integer vector of length m, such as [p1, p2, ..., pm], where pi represents the position number of the i-th sensor, corresponding to a certain position coordinate on the structure;

[0087] Population initialization: Randomly generate individuals in the population, each individual consists of m random integers, representing the location of the sensor. Population size: N (usually between 30-100). For each individual, generate a vector [p1, p2, ..., pm] consisting of m different integers, each integer is randomly selected from 1-n and is not repeated;

[0088] Fitness function design: Based on the three-dimensional modal confidence criterion, the goal is to minimize the maximum value of the diagonal elements of the TMAC matrix:

[0089]

[0090] Selection operation: Use roulette wheel selection (the selection probability is proportional to the fitness value, and individuals with higher fitness values ​​have a greater probability of being selected) to select individuals from the population for reproduction;

[0091] Crossover operation: Perform a crossover operation on the selected individuals to generate new individuals.

[0092] One or two crossover points were randomly selected among the parent individuals.

[0093] Swap some of the positions of the offspring to ensure that the integers in each position are not repeated.

[0094] Mutation operation: Randomly select two positions in an individual and exchange the integers at the corresponding positions to increase the diversity of the population.

[0095] Population update: Using the elite retention strategy, the individuals with the highest fitness value in the previous generation are directly retained to the next generation, and the remaining individuals are generated through selection, crossover, and mutation.

[0096] Stop iteration: Set the maximum number of iterations. The algorithm stops when the number of iterations is reached.

[0097] Output the optimal solution: When the algorithm terminates, the individual with the highest fitness value is output, which is the optimized three-dimensional sensor layout solution. At the same time, the TMAC matrix and iterative convergence information are output, such as Figure 4 and Figure 5 shown.

[0098] In summary, by optimizing the layout of sensors, the recognition accuracy of structural modal parameters is significantly improved, ensuring accurate capture of key modal information; under the premise of ensuring monitoring effect, the number of sensors and layout costs are reasonably reduced, achieving efficient use of resources; it can effectively handle multi-degree-of-freedom vibration characteristics in complex three-dimensional structures, and improve the applicability of structural health monitoring systems in complex application scenarios; through the global optimization capability of genetic algorithms, feasible layout plans can be quickly found; compared with traditional manual experience layout methods, the design time of sensor layout is greatly shortened, and the efficiency of engineering implementation is significantly improved.

[0099] Example 2

[0100] On the basis of the first embodiment, this embodiment further provides a three-dimensional sensor optimization arrangement system based on a genetic algorithm, including a grid division module, an optimization algorithm module, and a three-dimensional modeling module;

[0101] The meshing module is used to select an appropriate number of candidate measuring points in combination with the mesh unit size in the finite element model;

[0102] The optimization algorithm module is used to construct a three-dimensional sensor optimization layout model, and use a genetic algorithm to solve the model to obtain a sensor layout plan;

[0103] The three-dimensional modeling module is used to construct a three-dimensional sensor optimization layout model.

[0104] This embodiment also provides a computer device, which is suitable for the case of a three-dimensional sensor optimization layout method based on a genetic algorithm, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the three-dimensional sensor optimization layout method based on a genetic algorithm as proposed in the above embodiment.

[0105] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0106] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the arrangement of three-dimensional sensors based on a genetic algorithm as proposed in the above embodiment is implemented.

[0107] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A three-dimensional sensor optimization arrangement method based on genetic algorithm, characterized in that: including the following steps, establishing a finite element model; combining the mesh element size in the finite element model, selecting an appropriate number of candidate measurement points, and determining the set of candidate measurement points for the sensor; determining the number of vibration modes to be extracted and performing modal information extraction; selecting the number of modes based on the Fisher information matrix, and using the modal information of each measurement point of the structure calculated by modal analysis to construct the Fisher information matrix of the structure; constructing a three-dimensional sensor optimal placement model, and establishing a sensor optimal placement model based on the three-dimensional modal confidence criterion; using the genetic algorithm to solve the three-dimensional sensor optimal placement model to obtain the sensor placement scheme.

2. The method for optimizing the placement of three-dimensional sensors based on a genetic algorithm as claimed in claim 1, characterized in that: The methods for modal information extraction include the multiple Ritz vector method, the Lanczos method, and the subspace iteration method; Among them, the multiple Ritz vector method is used for the vibration mode analysis of small structures, specifically for vibration mode analysis of less than 10 orders; The Lanczos method is used to extract the high-order modes of large and complex structures, specifically for vibration mode analysis of more than 20 orders; The subspace iteration method is used to extract the vibration mode analysis of medium-sized structures, specifically for vibration mode analysis of more than 10 and less than 20 orders.

3. The method for optimizing the placement of three-dimensional sensors based on a genetic algorithm as claimed in claim 2, characterized in that: Calculate the Fisher information matrix that reflects the test sensitivity of the structural modal vibration mode. The Fisher information matrix is expressed as Q=Φ T F; where Q is the modal vibration mode matrix; When the number of target modes is m (m < n), considering the orthogonality of the modal vibration modes, it can also be expressed as:

4. The method for optimizing the placement of three-dimensional sensors based on a genetic algorithm as claimed in claim 3, characterized in that: Calculate the rate of change of the 2-norm of the Fisher information matrix at two adjacent orders. The first-order 2-norm of the Fisher information matrix is ​​recorded as ||Q i ||2, then the rate of change on the first i orders and the first i+1 orders can be expressed as: where n is the total number of modes; i is the number of modes used in the calculation, 1 ≤ i ≤ n - 1.

5. The method for optimizing the placement of three-dimensional sensors based on a genetic algorithm as claimed in claim 4, characterized in that: Based on the three-dimensional modal confidence criterion, the three translational degrees of freedom of the node are taken as a unit, and the expression is: In the formula, C i,j is the element in the i-th row and j-th column of the three-dimensional modal assurance criterion TMAC, Q i,j It is the element in the i-th row and j-th column of the Fisher information matrix.

6. The method for optimizing the placement of three-dimensional sensors based on a genetic algorithm as claimed in claim 5, characterized in that: Minimizing the maximum non-diagonal element of the TMAC matrix is used as the optimization goal for sensor placement, and the expression is:

7. The method for optimizing the placement of three-dimensional sensors based on a genetic algorithm as claimed in claim 6, characterized in that: When solving the three-dimensional sensor optimal placement model, first perform population initialization, design based on the three-dimensional modal confidence criterion to minimize the maximum value of the diagonal elements of the TMAC matrix, randomly select one or two crossover points among the parent individuals, perform partial position exchange on the offspring to ensure that the integers at each position do not repeat, randomly select two positions in an individual, exchange the integers at the corresponding positions, increase the diversity of the population, use the elitist retention strategy, directly retain the individual with the highest fitness value in the previous generation to the next generation, and the remaining individuals are generated through selection, crossover, and mutation. Set the maximum number of iterations, and the algorithm stops after reaching the number of iterations.

8. A three-dimensional sensor optimization arrangement system based on genetic algorithm, based on the three-dimensional sensor optimization arrangement method based on genetic algorithm according to any one of claims 1 to 7, characterized in that: including a mesh division module, an optimization algorithm module, and a three-dimensional modeling module; The mesh division module is used to combine the mesh element size in the finite element model and select an appropriate number of candidate measurement points; The optimization algorithm module is used to construct a three-dimensional sensor optimal placement model and use the genetic algorithm to solve the model to obtain the sensor placement scheme; The three-dimensional modeling module is used to construct a three-dimensional sensor optimal placement model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, it implements the steps of the three-dimensional sensor optimal placement method based on the genetic algorithm according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the three-dimensional sensor optimal placement method based on the genetic algorithm according to any one of claims 1 to 7.

Images