A method, device, equipment and storage medium for optimizing a sensor arrangement of a segment erector

Abstract

The application provides a sensor arrangement optimization method, device, equipment and storage medium for a segment erector. It relates to the technical field of sensor layout. The method comprises: establishing a three-dimensional model of a segment erector to which sensors are to be arranged, determining possible installation points of the sensors according to the shape and size of the three-dimensional model; constructing a sensor element library based on the three-dimensional model; wherein the sensor element library is used to determine different types of sensors; determining a spatial effective range of the sensors through simulation analysis according to the field environment and the segment size; constructing an optimization objective function with data precision quality, sensor data processing real-time performance and cost as targets; solving the optimization objective function based on particle swarm optimization to obtain an optimal sensor arrangement scheme. The application can provide a more flexible sensor configuration strategy for the segment erector under different tunnel diameters, curvature radii or complex working conditions.

Description

Technical Field

[0001] This application relates to the field of sensor layout technology, and in particular to a sensor layout optimization method, apparatus, equipment and storage medium for a segment assembly machine. Background Technology

[0002] Current technologies for rapid identification and positioning in tunnel segment assembly machines primarily rely on single sensors or fixed arrangements for target detection and measurement. Common sensor types include monocular or binocular vision, line laser ranging, and infrared ranging. While these sensors have certain advantages in their respective application areas—for example, vision sensors can acquire rich image information, line laser sensors have high-precision contour measurement capabilities, and infrared ranging sensors are relatively stable in complex lighting environments—they still have the following prominent drawbacks in actual tunnel boring machine (TBM) construction: 1. Poor adaptability to segments of different diameters. Currently, many segment assembly machines employ sensor layouts designed only for segments of specific diameters or models. When segment diameters are changed or new construction conditions are implemented, the existing sensor layout may encounter problems such as blind spots, increased measurement errors, or operational inconvenience. Because segments of different diameters vary significantly in size, joint shape, and assembly methods, traditional single-sensor or fixed-layout solutions lack sufficient flexibility and cannot achieve the same measurement accuracy and efficiency in new environments. To adapt to new diameters or assembly requirements, it is often necessary to re-select and redesign the sensor layout, increasing R&D and equipment costs and extending the construction cycle.

[0003] 2. Lack of a systematic, multi-sensor fusion approach The tunnel segment assembly process is affected by multiple factors in the tunnel construction environment, such as dust, vibration, and temperature and humidity changes. A single sensor cannot achieve high reliability and high accuracy detection under all working conditions. On the one hand, a single sensor is prone to data distortion or interruption when faced with complex environmental interference or malfunctions. On the other hand, different types of sensors provide varying information dimensions and accuracy ranges. Reasonable fusion and redundancy design of data from multiple sensors can often significantly improve the overall perception of segment position, orientation, and assembly accuracy. However, current research and application of systematic, multi-sensor fusion technologies are insufficient, mostly relying on single or a small number of sensors, making it difficult to fully leverage the complementary advantages of multi-source information. This limits the potential for improvement in the automation and intelligence of tunnel segment assembly machines.

[0004] 3. It is difficult to balance cost and accuracy. In actual construction, to improve the accuracy of detecting the position and attitude of tunnel segments, traditional methods often require increasing the number of high-precision, high-cost sensors. However, this also leads to a significant increase in system cost, hindering large-scale or widespread adoption. When construction requirements are diverse, continuously stacking the same type of sensors may result in excessive costs, information redundancy, and increased maintenance difficulties. On the other hand, reducing the number of sensors or using lower-precision sensors to lower costs often fails to meet the high-precision identification and assembly requirements of larger diameter tunnel segments, posing safety risks and potential construction quality hazards. Achieving a balance between cost and accuracy under different working conditions remains a challenge, lacking a mature solution.

[0005] Although some research has been conducted on sensor placement and selection for specific tunnel boring machines (TBMs) or segments of specific diameters, a universal and modular design methodology or standard has yet to be established. Most projects typically rely on experience or localized optimizations for one-time layouts. Once adaptation to new construction environments or larger diameter segments is required, a complete overhaul of the design and equipment selection process must begin from scratch. This lack of reusability and versatility not only slows down the adoption of intelligent and automated technologies for segment assembly machines but also limits the industry's overall technological accumulation and innovation. Therefore, developing a systematic and multi-dimensional sensor placement optimization method is crucial for addressing the increasing demands of long urban and intercity tunnel construction. Summary of the Invention

[0006] This application provides a sensor layout optimization method, apparatus, equipment, and storage medium for tunnel segment assembly machines. It addresses the shortcomings of existing sensor layout methods in tunnel segment assembly machines, such as insufficient adaptability, difficulty in balancing detection accuracy and construction efficiency for segments of different diameters, and cost constraints. By systematically summarizing and managing feasible installation points and the detection range of different sensor types, it effectively compensates for the deficiencies of traditional single-sensor types or layout methods. It achieves a better balance between accuracy, speed, and cost based on different diameters and working conditions. Furthermore, this application fully considers the impact of sensor installation location and density on the overall construction process and adaptability to the site environment, thus providing engineers with a better layout scheme and avoiding the waste of computational resources and decreased assembly accuracy caused by blind or repetitive layouts. Compared with existing technologies, this application not only provides a more flexible sensor configuration strategy for tunnel segment assembly machines under different tunnel diameters, radii of curvature, or complex working conditions, but also effectively ensures the smooth progress of the construction process.

[0007] In a first aspect, this application provides a sensor arrangement optimization method for a segment assembly machine, comprising: A three-dimensional model of the segment assembly machine where sensors are to be deployed is established, and the possible installation locations of the sensors are determined based on the shape and size of the three-dimensional model. Based on the aforementioned 3D model, a sensor component library is constructed; wherein, the sensor component library is used to identify different types of sensors; Simulation analysis was conducted based on the site environment and the dimensions of the tunnel lining segments to determine the effective spatial range of the sensors. An optimization objective function is constructed with the goals of data accuracy and quality, real-time sensor data processing, and cost. Based on particle swarm optimization, the objective function is solved to obtain the optimal sensor arrangement scheme.

[0008] In one possible design, a sensor component library is constructed based on the aforementioned three-dimensional model, including: Acquire the performance parameters of various sensors; wherein, the performance parameters include detection accuracy, field of view angle, effective measurement range, resolution, minimum measurement distance, and maximum measurement distance; Establish three-dimensional models of the field of view for different sensors, and clearly mark the boundaries, center, and effective detection distance of the field of view; The performance parameters and 3D field-of-view model data of various sensors are integrated into the component library to obtain the sensor component library.

[0009] In one possible design, simulation analysis is performed based on the site environment and the segment dimensions to determine the effective spatial range of the sensor, including: Using the possible installation locations of sensors as candidate locations, simulation analysis is used to evaluate whether the detection range of different types of sensors at each candidate location can completely cover the key detection area during the segment assembly process and meet the installation accuracy requirements during construction. Locations and sensor types that cannot meet the accuracy requirements or whose detection range does not meet the actual application requirements are eliminated, and the best sensor type and its corresponding best installation space range that meet the specific engineering requirements are selected.

[0010] In one possible design, the optimization objective function is expressed as:

[0011] In the formula, min represents the minimum value function, and G represents the optimization objective function. F ( x , y , z The function representing the contribution of the sensor's spatial location to the data accuracy is denoted as . This represents the objective function for real-time sensor data processing. Let λ1 represent the cost objective function, λ2 represent the weighting coefficient for data accuracy and quality, λ3 represent the weighting coefficient for the real-time performance of sensor data processing, and λ4 represent the weighting coefficient for cost.

[0012] In one possible design, the contribution function of the sensor's spatial location to data accuracy is expressed as:

[0013] In the formula, The spatial coordinates of the target monitoring points for segment assembly. The actual coordinates of the sensor's location. For the first The data precision weighting coefficient for each region represents that region's contribution to the overall data precision requirement. m Indicates along x Number of partitions along the axis n Indicates along y The number of partitions along the axis is used to enumerate the target sub-region that each sensor may be able to act upon.

[0014] In one possible design, the objective function for the real-time performance of the sensor data processing is expressed as:

[0015] In the formula, For the first k The amount of data generated by a sensor per unit time The maximum amount of data that the sensor is allowed to process. p This represents the total number of sensors; The cost objective function is expressed as follows:

[0016] In the formula, φ is the cost adjustment factor. For the first The specific cost of installing sensors within the area includes the price of the sensor itself, installation, and maintenance costs.

[0017] In one possible design, the optimal sensor arrangement is obtained by solving the objective function based on particle swarm optimization. Based on the effective spatial range of the sensor, potential sensor installation locations that meet practical needs are determined as the initial spatial position inputs for the particle swarm optimization (PSO) algorithm. Then, according to the iterative update formula of the PSO algorithm, the particle positions and velocities are continuously updated to gradually approach the global optimum of the objective function. During the iteration process, the historical optimum of each particle and the global optimum of the swarm are updated by comparing the current fitness value with the historical best fitness value. The iterative update formula is expressed as follows:

[0018]

[0019] In the formula, Indicates the first k In the nth iteration s The position vectors of each particle. The updated velocity vector, Pbest s This is the optimal position in the particle's history. Gbest To be the globally optimal position Indicates the first k In the +1st iteration, the... s The position vectors of each particle.

[0020] Secondly, this application provides a sensor arrangement optimization device for a segment assembly machine, the device comprising: The location determination module is configured to create a three-dimensional model of the segment assembly machine where sensors are to be deployed, and determine the possible installation locations of the sensors based on the shape and size of the three-dimensional model. The component library construction module is configured to construct a sensor component library based on the 3D model; wherein the sensor component library is used to determine different types of sensors; The location selection module is configured to perform simulation analysis based on the site environment and the segment size to determine the effective range of the sensor space; The objective function building module is configured to construct and optimize an objective function with data accuracy and quality, real-time sensor data processing performance, and cost as the objectives. The optimal solution determination module is configured to solve the optimization objective function based on particle swarm optimization to obtain the optimal sensor arrangement scheme.

[0021] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the sensor arrangement optimization method for a segment assembly machine as described in the first aspect and various possible designs of the first aspect.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the sensor arrangement optimization method for a segment assembly machine as described in the first aspect and various possible designs of the first aspect.

[0023] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the sensor arrangement optimization method for a segment assembly machine as described in the first aspect and various possible designs of the first aspect.

[0024] The sensor layout optimization method, apparatus, equipment, and storage medium for segment assembly machines provided in this application have at least the following beneficial effects: This application establishes a comprehensive sensor component library and utilizes virtual simulation analysis technology to accurately determine the optimal installation locations for sensors. By combining a multi-objective optimization algorithm to comprehensively consider data accuracy, real-time performance, and economic cost, it significantly improves the versatility and flexibility of the sensor layout scheme. This method effectively solves the problems of insufficient adaptability and difficulty in balancing accuracy and cost in traditional sensor layout schemes. The optimized layout strategy reduces redundant sensor configurations, lowers system costs, and ensures high data accuracy and real-time response performance during assembly, thereby improving the construction quality and efficiency of segment assembly. It has broad engineering application value and promising prospects. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0026] Figure 1 A flowchart illustrating a sensor layout optimization method for a segment assembly machine provided in an embodiment of this application; Figure 2 This is a particle swarm optimization block diagram in a sensor layout optimization method for a segment assembly machine provided in an embodiment of this application; Figure 3 This is a structural diagram of a sensor layout optimization device for a segment assembly machine provided in an embodiment of this application.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0029] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0030] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0031] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0032] This application provides a method for optimizing sensor placement in a segment assembly machine. For example... Figure 1 The diagram shown is a flowchart of a sensor layout optimization method for a segment assembly machine provided in an embodiment of this application. Figure 1 As shown, the sensor layout optimization method for the segment assembly machine includes the following steps S10-S50.

[0033] S10: Establish a 3D model of the segment assembly machine where sensors are to be deployed, and determine the possible installation locations of the sensors based on the shape and size of the 3D model.

[0034] For example, the specific implementation process of step S10 is as follows: First, a 3D model of the segment assembly machine where sensors will be deployed is created. Based on its shape and dimensions, the possible installation locations of the sensors are determined, and their spatial positions are recorded. Using 3D modeling software such as SolidWorks, CATIA, or UG, a complete 3D geometric model of the segment assembly machine is constructed based on the structural design drawings and on-site measurement data. This model must accurately reflect the specific external dimensions, structural layout, and spatial distribution of key functional units of the segment assembly machine. Simultaneously, to meet the needs of sensor monitoring, structural details that may affect the monitoring effect must be clearly defined in the model, such as obstructions, the range of motion of moving parts, wiring channels, and installation interface locations. Based on the 3D model, according to the functional requirements of sensor monitoring during segment assembly, including but not limited to industrial cameras, laser rangefinders, line laser sensors, and displacement sensors, the possible installation locations of various sensors are determined. When selecting these locations, multiple factors must be considered, such as the sensor's monitoring field of view, accessibility, installation stability, and signal transmission stability, to ensure the actual effectiveness of the sensors after installation. After identifying each possible installation point, modeling software is used to mark the spatial coordinates of each point and export the corresponding spatial location data, forming a data table that records the spatial coordinates of each point, providing basic data support for subsequent sensor deployment optimization.

[0035] S20: Based on the 3D model, construct a sensor component library; whereby the sensor component library is used to determine different types of sensors.

[0036] For example, step S20 further constructs a sensor component library based on the 3D model. This component library is used to clearly record the relevant performance parameters of different types of sensors (including but not limited to industrial cameras, line laser sensors, laser rangefinders, and displacement sensors). The specific construction process is as follows: First, based on actual usage requirements and existing sensor products on the market, basic information of various sensors is collected, such as detection accuracy, field of view angle, effective measurement range, resolution, minimum measurement distance, and maximum measurement distance; second, using modeling software such as SolidWorks or dedicated simulation analysis software, 3D models of the field of view of different sensors are established, and the boundaries, center, and effective detection distance of the field of view are clearly marked; finally, the above performance parameters and field of view model data are integrated into the component library to facilitate subsequent simulation analysis, sensor selection, and actual installation layout. The establishment of the component library ensures that the selection of sensors and the determination of installation positions in subsequent steps are based on accurate and comprehensive parameter and model data, thereby improving the rationality and accuracy of the sensor layout scheme.

[0037] S30: Based on the on-site environment and the size of the tunnel lining segments, conduct simulation analysis to determine the effective range of the sensor space.

[0038] For example, the specific implementation process of step S30 is as follows: Based on the actual construction environment's spatial dimensions and the specific tunnel segment dimensions, and combined with the performance parameters and detection ranges of various sensors (such as industrial cameras, line laser sensors, and laser rangefinders) in the component library, a virtual simulation analysis was conducted under the actual application environment. Specifically, the spatial coordinate data of the possible installation points identified in the first part were imported into the analysis software. Through simulation analysis, it was evaluated whether the detection range of different types of sensors at each candidate point could completely cover the key detection areas during the tunnel segment assembly process and meet the installation accuracy requirements during construction. After simulation analysis and accuracy evaluation, points and sensor types that could not meet the accuracy requirements or whose detection range did not meet the actual application needs were eliminated, and the optimal sensor type and its corresponding optimal installation spatial location range that met the specific engineering requirements were selected. Finally, the effective spatial range that met the tunnel segment installation accuracy requirements and could actually accommodate sensor installation in this actual project was determined, providing specific spatial location range data support for the next step of optimization layout analysis.

[0039] S40: Construct an optimization objective function with the goals of data accuracy and quality, real-time sensor data processing, and cost.

[0040] For example, in step S40, an optimization objective function is constructed with the goals of data accuracy and quality, real-time sensor data processing, and cost. Based on actual application requirements and optimization objectives, an optimization objective function G that comprehensively considers data accuracy and quality, real-time sensor data processing, and cost is constructed, expressed as:

[0041] Where F(x,y,z) represents the contribution function of the sensor's spatial position to the data accuracy, defined as:

[0042] In the formula, The spatial coordinates of the target monitoring points for segment assembly. The actual coordinates of the sensor's location. For the first The data precision weighting coefficient for each region represents the contribution of that region to the overall data precision requirement.

[0043] The objective function for real-time sensor data processing is defined as follows:

[0044] In the formula, Let k be the amount of data generated by the k-th sensor per unit time. This represents the maximum amount of data the sensor is allowed to process. This function measures the impact of the amount of data generated by the sensor on the system's real-time performance; the larger the data volume, the longer the processing time, and the more significant the impact on real-time performance.

[0045] The cost objective function is specifically defined as follows:

[0046] Where φ is the cost adjustment factor. For the first The specific cost of installing sensors within the area includes the price of the sensor itself, installation and maintenance costs, etc.

[0047] λ1 is the weighting coefficient for data accuracy and quality; a higher value indicates a higher requirement for data accuracy. λ2 is the weighting coefficient for the real-time performance of sensor data processing, emphasizing the impact of sensor data volume on system real-time performance. λ3 is the weighting coefficient for cost, reflecting the importance of economic cost in sensor deployment schemes.

[0048] S50: Based on particle swarm optimization, the objective function is solved to obtain the optimal sensor arrangement scheme.

[0049] Finally, in step S50, an optimization algorithm is used to find the optimal solution, thereby determining the sensor placement strategy for a specific construction environment or segment type and size. Particle swarm optimization (PSO) is employed for optimization. First, the potential sensor installation points selected in step S30 that meet the actual requirements are used as the initial spatial position inputs for the PSO algorithm particles. Then, according to the iterative update rules of the PSO algorithm, the particle positions and velocities are continuously updated to gradually approach the global optimum of the objective function. During the iteration process, the historical optimum of each particle and the global optimum of the swarm are updated by comparing the current fitness value with the historical optimum. The iterative formula of the PSO algorithm is as follows:

[0050]

[0051] in, This represents the position vector of the s-th particle in the k-th iteration. For the updated velocity vector, Pbest s The particle swarm optimization algorithm terminates after a certain number of iterations or when the objective function converges to the required accuracy. The final particle positions represent the optimal sensor placement scheme under specific construction environments and segment types and sizes. This scheme can serve as the basis for sensor placement in actual on-site construction to achieve optimization goals in monitoring data accuracy, real-time performance, and cost control. By comprehensively considering the structural characteristics of the segment assembly machine and the monitoring performance characteristics of the on-site sensors, the optimal sensor placement positions are determined using an optimization algorithm. This significantly improves the quality and accuracy of monitoring data and sensor utilization, solving the problems of insufficient accuracy and resource waste in traditional placement methods during on-site implementation, and enhancing the overall system's accuracy and economy.

[0052] In summary, this application, by comprehensively analyzing sensor types and feasible locations and considering multi-dimensional performance indicators, helps engineers select a reasonable sensor layout scheme, providing efficient and accurate detection data support for the subsequent assembly process. This method simultaneously considers engineering costs and on-site adaptability, and is of great significance for promoting the construction of large-diameter tunnels and improving construction quality. This invention has broad engineering application prospects and promotional value.

[0053] This application also provides a sensor layout optimization device for a segment assembly machine, such as... Figure 3 As shown, the sensor layout optimization device for the segment assembly machine includes: The location determination module 301 is configured to create a three-dimensional model of the segment assembly machine where the sensors are to be deployed, and determine the possible installation locations of the sensors based on the shape and size of the three-dimensional model. The component library construction module 302 is configured to construct a sensor component library based on the three-dimensional model; wherein the sensor component library is used to determine different types of sensors; The point selection module 303 is configured to perform simulation analysis based on the site environment and the segment size to determine the effective range of the sensor space. The objective function building module 304 is configured to construct an optimized objective function with data accuracy and quality, real-time sensor data processing performance, and cost as objectives. The optimal solution determination module 305 is configured to solve the optimization objective function based on particle swarm optimization to obtain the optimal sensor arrangement scheme.

[0054] In some embodiments, the component library building module is further configured to: Acquire the performance parameters of various sensors; wherein, the performance parameters include detection accuracy, field of view angle, effective measurement range, resolution, minimum measurement distance, and maximum measurement distance; Establish three-dimensional models of the field of view for different sensors, and clearly mark the boundaries, center, and effective detection distance of the field of view; The performance parameters and 3D field-of-view model data of various sensors are integrated into the component library to obtain the sensor component library.

[0055] In some embodiments, the location filtering module is further configured to: Using the possible installation locations of sensors as candidate locations, simulation analysis is used to evaluate whether the detection range of different types of sensors at each candidate location can completely cover the key detection area during the segment assembly process and meet the installation accuracy requirements during construction. Locations and sensor types that cannot meet the accuracy requirements or whose detection range does not meet the actual application requirements are eliminated, and the best sensor type and its corresponding best installation space range that meet the specific engineering requirements are selected.

[0056] In some embodiments, the optimization objective function is expressed as:

[0057] In the formula, min represents the minimum value function, and G represents the optimization objective function. F ( x , y , z The function representing the contribution of the sensor's spatial location to the data accuracy is denoted as . This represents the objective function for real-time sensor data processing. Let λ1 represent the cost objective function, λ2 represent the weighting coefficient for data accuracy and quality, λ3 represent the weighting coefficient for the real-time performance of sensor data processing, and λ4 represent the weighting coefficient for cost.

[0058] In some embodiments, the contribution function of the sensor's spatial location to data accuracy is expressed as:

[0059] In the formula, The spatial coordinates of the target monitoring points for segment assembly. The actual coordinates of the sensor's location. For the first The data precision weighting coefficient for each region represents that region's contribution to the overall data precision requirement. m Indicates along x Number of partitions along the axis n Indicates along y The number of partitions along the axis is used to enumerate the target sub-region that each sensor may be able to act upon.

[0060] In some embodiments, the real-time objective function for sensor data processing is expressed as:

[0061] In the formula, For the first k The amount of data generated by a sensor per unit time The maximum amount of data that the sensor is allowed to process. p This represents the total number of sensors; The cost objective function is expressed as follows:

[0062] In the formula, φ is the cost adjustment factor. For the first The specific cost of installing sensors within the area includes the price of the sensor itself, installation, and maintenance costs.

[0063] In some embodiments, the optimal solution determination module is further configured to: Based on the effective spatial range of the sensor, potential sensor installation locations that meet practical needs are determined as the initial spatial position inputs for the particle swarm optimization (PSO) algorithm. Then, according to the iterative update formula of the PSO algorithm, the particle positions and velocities are continuously updated to gradually approach the global optimum of the objective function. During the iteration process, the historical optimum of each particle and the global optimum of the swarm are updated by comparing the current fitness value with the historical best fitness value. The iterative update formula is expressed as follows:

[0064]

[0065] In the formula, Indicates the firstk In the nth iteration s The position vectors of each particle. The updated velocity vector, Pbest s This is the optimal position in the particle's history. Gbest To be the globally optimal position Indicates the first k In the +1st iteration, the... s The position vectors of each particle.

[0066] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0067] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0068] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0069] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0070] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the sensor layout optimization method for the segment assembly machine described in the above embodiments.

[0071] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the sensor layout optimization method for the segment assembly machine in the above embodiments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0073] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0074] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0075] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0076] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0077] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0078] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0079] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0080] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0081] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing sensor arrangement in a segment assembly machine, characterized in that, The method includes: A three-dimensional model of the segment assembly machine where sensors are to be deployed is established, and the possible installation locations of the sensors are determined based on the shape and size of the three-dimensional model. Based on the aforementioned 3D model, a sensor component library is constructed; wherein, the sensor component library is used to identify different types of sensors; Simulation analysis was conducted based on the site environment and the dimensions of the tunnel lining segments to determine the effective spatial range of the sensors. An optimization objective function is constructed with the goals of data accuracy and quality, real-time sensor data processing, and cost. Based on particle swarm optimization, the objective function is solved to obtain the optimal sensor arrangement scheme.

2. The sensor layout optimization method for a segment assembly machine according to claim 1, characterized in that, Based on the aforementioned 3D model, a sensor component library is constructed, including: Acquire the performance parameters of various sensors; wherein, the performance parameters include detection accuracy, field of view angle, effective measurement range, resolution, minimum measurement distance, and maximum measurement distance; Establish three-dimensional models of the field of view for different sensors, and clearly mark the boundaries, center, and effective detection distance of the field of view; The performance parameters and 3D field-of-view model data of various sensors are integrated into the component library to obtain the sensor component library.

3. The sensor layout optimization method for a segment assembly machine according to claim 1, characterized in that, Based on the on-site environment and segment dimensions, simulation analysis was conducted to determine the effective range of the sensor space, including: Using the possible installation locations of sensors as candidate locations, simulation analysis is used to evaluate whether the detection range of different types of sensors at each candidate location can completely cover the key detection area during the segment assembly process and meet the installation accuracy requirements during construction. Locations and sensor types that cannot meet the accuracy requirements or whose detection range does not meet the actual application requirements are eliminated, and the best sensor type and its corresponding best installation space range that meet the specific engineering requirements are selected.

4. The sensor layout optimization method for a segment assembly machine according to claim 1, characterized in that, The optimization objective function is expressed as: In the formula, min represents the minimum value function, and G represents the optimization objective function. F ( x , y , z The function representing the contribution of the sensor's spatial location to the data accuracy is denoted as . This represents the objective function for real-time sensor data processing. Let λ1 represent the cost objective function, λ2 represent the weighting coefficient for data accuracy and quality, λ3 represent the weighting coefficient for the real-time performance of sensor data processing, and λ4 represent the weighting coefficient for cost.

5. The sensor layout optimization method for a segment assembly machine according to claim 4, characterized in that, The contribution function of the sensor's spatial location to the data accuracy is expressed as: In the formula, The spatial coordinates of the target monitoring points for segment assembly. The actual coordinates of the sensor's location. For the first The data precision weighting coefficient for each region represents that region's contribution to the overall data precision requirement. m Indicates along x Number of partitions along the axis n Indicates along y The number of partitions along the axis is used to enumerate the target sub-region that each sensor may be able to act upon.

6. The sensor layout optimization method for a segment assembly machine according to claim 5, characterized in that, The objective function for real-time sensor data processing is expressed as: In the formula, For the first q The amount of data generated by a sensor per unit time The maximum amount of data that the sensor is allowed to process. p This represents the total number of sensors; The cost objective function is expressed as follows: In the formula, φ is the cost adjustment factor. For the first The specific cost of installing sensors within the area includes the price of the sensor itself, installation, and maintenance costs.

7. The sensor layout optimization method for a segment assembly machine according to claim 1, characterized in that, Based on particle swarm optimization, the optimal sensor arrangement scheme is obtained by solving the objective function. Based on the effective spatial range of the sensor, the possible installation points of the sensor that meet the actual needs are determined as the spatial position input of the initial particles in the particle swarm algorithm. Then, according to the iterative update formula of the particle swarm optimization algorithm, the particle position and velocity are continuously updated to gradually approach the global optimum of the objective function. During the iteration process, the historical optimum of each particle and the global optimum of the swarm are updated by comparing the current fitness value of the particle with the historical best fitness value. The iterative update formula is expressed as follows: In the formula, Indicates the first k In the nth iteration s The position vectors of each particle. The updated velocity vector, Pbest s This is the optimal position in the particle's history. Gbest To be the globally optimal position Indicates the first k In the +1st iteration, the... s The position vectors of each particle.

8. A sensor layout optimization device for a segment assembly machine, characterized in that, The device includes: The location determination module is configured to create a three-dimensional model of the segment assembly machine where sensors are to be deployed, and determine the possible installation locations of the sensors based on the shape and size of the three-dimensional model. The component library construction module is configured to construct a sensor component library based on the 3D model; wherein the sensor component library is used to determine different types of sensors; The location selection module is configured to perform simulation analysis based on the site environment and the segment size to determine the effective range of the sensor space; The objective function building module is configured to construct and optimize an objective function with data accuracy and quality, real-time sensor data processing performance, and cost as the objectives. The optimal solution determination module is configured to solve the optimization objective function based on particle swarm optimization to obtain the optimal sensor arrangement scheme.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the sensor layout optimization method for a segment assembly machine as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the sensor layout optimization method for a segment assembly machine as described in any one of claims 1-7.

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