A particle sensor housing system and methods of configuring and making the same

Abstract

The application discloses a kind of granule sensor shell systems and its configuration optimization and preparation method, belong to infrastructure monitoring technical field.The shell system includes the empty shell of the real shape of the replica broken stone, the fixed support and counterweight block of inner setting.Through three-dimensional scanning, the target broken stone shape is obtained, and the shell digital model is established;Design the fixed support including sensor cavity, multiple counterweight cavities and support shaft;Establish the optimization model with the size of counterweight block as variable, the minimum matching error of system total mass and centroid as target, and obtain the optimal size of counterweight block by solving;According to the optimization result, adjust the size of counterweight cavity and iterate optimization until the accuracy meets the threshold;Finally, the shell preparation is completed through additive manufacturing and assembly.The application realizes the high-precision matching of shell geometric shape, mass and centroid with real broken stone, and optimizes signal transmission through material selection, wall thickness thinning and cambered surface transition, which is suitable for in-situ high-precision monitoring of broken stone, aggregate and other bulk materials.

Description

Technical Field

[0001] This invention relates to the field of infrastructure condition monitoring technology, specifically to a particle sensor housing system and its configuration optimization and preparation method, which is applicable to the mechanical response monitoring of bulk materials such as ballast, crushed stone, and aggregate. Background Technology

[0002] In infrastructure constructed from bulk materials such as railway ballast and building aggregates, accurate monitoring of the mechanical response of these materials is crucial for assessing the service performance of the infrastructure. To achieve this, sensors need to be embedded within the bulk medium for in-situ monitoring. However, the significant differences in shape, material, and internal structure between existing sensor housings and actual crushed stone or other particles severely interfere with the accuracy and representativeness of the monitoring data, becoming a core bottleneck in technological development. Traditional regularly shaped sensor housings, due to their geometric shape being significantly inconsistent with the complex morphology of actual crushed stone or aggregates, result in significant deviations in the contact distribution between the housing and surrounding particles, the contact force transmission path, and the motion response, failing to accurately reflect the mechanical response of the bulk medium. Recently, sensor housing technologies based on shape replication (such as casting processes) have emerged. While these technologies can simulate the external contours of crushed stone to some extent, they still have limitations in signal transmission efficiency, mechanical equivalence, and production efficiency. The high-filling solid casting used to simulate crushed stone density attenuates key physical signals such as electromagnetic waves transmitted and received by the sensor, leading to decreased wireless sensor sensitivity and data distortion. Furthermore, the mass and center of gravity of a solid cast-in-place structure are passively determined by its shape and material density, making it impossible to coordinate with the built-in sensors for control. This results in the overall mass and center of gravity position of the shell and sensor system being difficult to accurately simulate real crushed stone. Therefore, existing technologies have not addressed the need for coordinated geometric replication, mechanical parameter equivalence, and signal transmission fidelity. Consequently, there is an urgent need in this field for a novel sensor shell solution that can overcome these multiple constraints. Summary of the Invention

[0003] This invention provides a particle sensor housing system and its configuration optimization and fabrication method, aiming to solve the technical bottlenecks of existing sensor housings in terms of mechanical equivalence and signal transmission efficiency. It achieves high-precision matching between the housing and the target gravel in terms of geometry, mass, and center of mass, while reducing wireless signal transmission loss. The sensor housing system includes a hollow housing, a fixing bracket, and a counterweight. The hollow housing refers to a sensor housing that replicates the true geometry of the target gravel and has an internal cavity. The fixing bracket is constructed within the cavity of the housing and includes a sensor cavity, a support shaft, and a counterweight cavity. The sensor cavity is used to fix the sensor. The support shaft fixes the sensor cavity and the counterweight cavity within the hollow housing. The counterweight is fixed within the counterweight cavity to provide weight to the sensor housing. The configuration optimization and fabrication method of the sensor housing system obtains gravel shape data through 3D scanning and establishes a digital model, then designs the internal fixing bracket structure and optimizes its configuration. Finally, the housing is fabricated through additive manufacturing and assembly. Specifically, it includes the following steps:

[0004] 1. Material selection for the outer casing system

[0005] To ensure efficient wireless signal transmission, a high-strength, low-dielectric-loss material is selected for the outer casing and mounting bracket; its density is denoted as [density value missing]. A high-density material with low dielectric loss is selected as the counterweight material, and its density is denoted as . .

[0006] 2. Digitalization of Crushed Stone Shape and Acquisition of Physical Parameters

[0007] The mass of the target gravel was measured using a precision electronic scale, and its surface point cloud data was acquired using a 3D laser scanner. The point cloud data was then subjected to mesh repair and boundary representation (B-Rep) transformation to establish an accurate digital model of the gravel. Based on this model, the centroid coordinates were calculated in 3D modeling software, assuming that the gravel material was uniform.

[0008] 3. Digital design and configuration optimization of sensor housing

[0009] (1) Structural design

[0010] The sensor housing was digitally designed based on a digital model of the crushed stone. By modifying the digital model to create a hollow structure inside, a fixed support was constructed within the cavity. This hollow structure maintains the accuracy of the external geometry of the crushed stone while providing an efficient and stable signal transmission environment for the internal sensor.

[0011] The design of the fixed support includes: using the centroid of the crushed stone as the center of the sensor cavity, and determining the size of the sensor cavity based on the sensor size and the preset assembly gap; the outer wall of the sensor cavity is designed as a prism structure; N support axes extend outward along the outer wall of the sensor cavity, corresponding to the ±X, ±Y, and ±Z directions respectively; a counterweight cavity is set at the midpoint of each support axis, with the center point coordinates being P. i (i=1~N), initial feature size is Meanwhile, the local structure of the outer shell is specifically optimized according to the main radiation direction of the sensor antenna: the inside of the sensor cavity is hollowed out to form a cavity to accommodate the sensor, and the angle of the internal cavity and the installation position of the sensor in the cavity are adjusted so that each support shaft is located outside the main radiation path of the sensor antenna, thus achieving structural avoidance; the wall thickness of the outer shell area facing the antenna is reduced, and the outer wall of this area is designed with arc transition and rounded corners to reduce signal reflection and attenuation; this structure not only ensures the stable fixation of the sensor, but also ensures the quality and stability of signal transmission.

[0012] After completing the above design, density was assigned to the shell and the fixed support in the 3D modeling software. A three-dimensional model of the sensor is created, and its density is assigned according to the actual sensor, or its total measured mass is directly assigned. The sensor model is then assembled into the sensor cavity according to the design position, and the initial mass M of the outer shell, including the sensor and the mounting bracket, is calculated using software. s0 With the center of mass C s0 .

[0013] (2) Optimization model establishment and solution

[0014] With initial shell parameters M s0 C s0 and the center coordinates P of the counterweight cavity i A counterweight size optimization model is established using the characteristic dimensions of the six counterweights as input. Using mass matching error, centroid matching error, and dimensional uniformity regularization term as decision variables, an objective function is constructed, and the size range of the counterweight is limited by boundary constraints. Solving this optimization model yields the initial side length of the counterweight. Corresponding quality and expected error.

[0015] Based on the optimization results, the dimensions of each counterweight cavity were adjusted to match the counterweight block, while retaining the preset assembly gap δ to ensure assembly alignment. After updating the shell model in the 3D software, the mass of the shell including the sensor and mounting bracket was recalculated. With center of mass The updated parameters are used to replace the initial values, and the optimization model is solved iteratively until the total mass of the system and the total centroid relative to the mass of the target crushed stone are equal. With center of mass The error meets the preset engineering accuracy threshold, thus obtaining the optimal size of the counterweight. Corresponding quality and the total mass of the system M total With the total centroid C total It outputs a final 3D model file that can be used for additive manufacturing.

[0016] 4. Shell fabrication and assembly

[0017] The outer shell and mounting bracket are manufactured using additive manufacturing processes; according to the optimal dimension L i Fabricate the counterweights; during assembly, first place the sensor in the sensor cavity of the lower half of the shell, adjust the sensor's orientation so that its main antenna radiation direction is directly facing the pre-designed thin-walled area, avoiding the direction of the support shaft and the counterweight, and fix it with epoxy resin; then, according to the correspondence determined by the optimization results, glue it to the counterweight cavity of each support shaft with epoxy resin; finally, align the upper and lower shells with the mating surfaces, fix them together with epoxy resin, and apply silicone sealant to the mating surfaces for waterproof sealing.

[0018] The technical solution provided in this application has at least the following technical effects or advantages:

[0019] This application achieves high-precision matching between the sensor housing and the actual crushed stone in terms of geometry, mass, and center of mass. Through an internal hollow shell structure, optimized local wall thickness, and the selection of low-dielectric-loss materials, it effectively reduces signal shielding and interference, improving the transmission quality and reception accuracy of the wireless sensor signal. Compared to existing technologies relying on customized casting processes for each type of crushed stone, the digital design method and additive manufacturing process employed in this application significantly improve production efficiency and consistency, making it more suitable for large-scale engineering deployments. The technical solution provided in this application enables the physical information picked up by the sensor to more accurately reflect the actual mechanical response of the granular material, offering a concealed, reliable, easily manufactured, and high-precision sensor housing solution for in-situ monitoring of granular materials. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the sensor housing system composition of the present invention;

[0021] Figure 2 This is a flowchart of the sensor housing system configuration optimization and fabrication method of the present invention;

[0022] Figure 3 This is a schematic diagram of the fixed support structure and the distribution of the counterweight cavity;

[0023] Figure 4 This is a schematic diagram of partial treatment of the outer casing and positioning posts and holes;

[0024] Figure 5Flowchart for optimizing the counterweight of the sensor housing system.

[0025] The numbers in the diagram are: 1-outer shell with cavity, 11-upper shell, 12-lower shell, 2-fixed bracket, 21-sensor cavity, 22-support shaft, 23-counterweight cavity, 211-sensor cavity cover, 212-sensor cavity without cover, 121 / 122 / 2111-positioning post, 111 / 114 / 2121-positioning hole, 112 / 123-rounded area, 113 / 124-thin-walled area, 3-counterweight block. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, systems, products, or devices.

[0028] like Figure 1 As shown, this application discloses a particle sensor housing system, including a hollow housing 1, a fixing bracket 2, and a counterweight 3. The hollow housing 1 refers to a sensor housing that replicates the actual geometry of the target gravel and has an internal cavity, and can be divided into two parts, 11 and 12. The fixing bracket 2 is constructed within the internal cavity of the hollow housing 1 and includes a sensor cavity 21, a support shaft 22, and a counterweight cavity 23. The sensor cavity 21 is used to fix the sensor. The support shaft 22 is used to fix the sensor cavity 21 and the counterweight cavity 23 within the hollow housing. The counterweight 3 is fixed within the counterweight cavity 23 and is used to provide weight to the sensor housing. Figure 2 As shown, the configuration optimization and fabrication method of the sensor housing system includes the following four key steps:

[0029] S1 housing system material selection

[0030] Engineering plastics with low electromagnetic wave signal loss are selected as the manufacturing materials for the hollow shell 1 and the fixed bracket 2. Nylon PA12 is preferred because it has stable density, consistent with the assumptions of the optimization model, and possesses both good mechanical properties and mature printing technology. Its density is denoted as... Compared to materials such as polyetheretherketone (PEEK), it offers advantages in cost and availability while meeting mechanical and signal transmission requirements. Compared to materials such as photocurable resins, it exhibits superior weather resistance and toughness. Low dielectric loss and high density materials are selected for the counterweight 3, preferably alumina ceramic or calcium tungstate, with a density denoted as [density information missing]. .

[0031] Digitalization of S2 Crushed Stone Shape and Acquisition of Physical Parameters

[0032] A real crushed stone sample was selected as the implementation object. The mass of the target crushed stone was measured using a precision electronic scale, and the average value was obtained after three repeated measurements to obtain the mass M of the target crushed stone. t Point cloud data of the gravel surface was acquired using a 3D laser scanner. After setting the scanning precision, multi-angle scanning was performed to ensure data integrity. Based on the acquired point cloud data, mesh repair and surface reconstruction were performed in professional software to generate an accurate B-Rep format digital model of the gravel. Based on this digital model, assuming the gravel material is homogeneous, its centroid C was calculated using a mass property tool in the 3D modeling software. t .

[0033] Digital design and configuration optimization of the S3 sensor housing

[0034] S31 Digital Design

[0035] Based on the aforementioned digital model of the crushed stone, the sensor housing is digitally modeled and designed using 3D modeling software such as SolidWorks. The digital model of the crushed stone is internally excised and shelled to create an empty shell 1 with an internal cavity, and a fixing support 2 is constructed within the cavity. Figure 3 As shown, in the design of the fixed bracket 2, the origin of the spatial rectangular coordinate system is set as the centroid C of the crushed stone. t A sensor cavity 21 is established with the center of the crushed stone as the center. The size of the sensor cavity 21 is determined according to the size of the sensor used and the preset assembly gap. The outer wall of the sensor cavity 21 is designed as an octagonal prism, and six support shafts are formed by extending outward along the four sides, top surface, and bottom surface of the octagonal prism, respectively, corresponding to the ±X, ±Y, and ±Z directions. Each shaft adopts a solid structure, and its cross-sectional shape is preferably circular or square. A reference plane perpendicular to the support shaft is generated at the connection position between the inner wall of the hollow shell 1 and each support shaft 22, which is used to fix the connection design between the bracket 2 and the hollow shell 1. The support shaft 22 and the inner wall of the hollow shell 1 are connected by an arc surface to reduce stress concentration and form a stable force transmission path. At the midpoint of each support shaft, a counterweight cavity 23 is set according to the maximum counterweight size allowed by the internal cavity of the hollow shell 1 at that position. Preferably, the counterweight cavity 23 is designed as a hollow cube or a hollow sphere. The coordinates of the center point of the i-th counterweight cavity are P. i (i=1~6), initial side length is Assign density parameters to the counterweights in 3D design software. .

[0036] The inner shape of the sensor cavity 21 is designed according to the shape of the sensor used, so that the sensor can be stably fixed in the sensor cavity 21. Preferably, the inner cavity of the sensor cavity 21 is designed as a cuboid shape. To optimize the wireless signal transmission performance, the inner angle of the sensor cavity 21 and the sensor mounting orientation are adjusted so that the main radiation direction of the sensor antenna avoids the area of ​​the support shaft 22 and the counterweight 3; for example... Figure 4 As shown, based on the main radiation direction of the sensor antenna, the wall thickness of the local areas 113 and 124 of the outer shell facing the antenna is reduced, and a smooth arc transition is adopted at the connection between the thinned area and the surrounding outer shell 112 and 123. At the same time, the outer surface of the outer shell in this area is slightly rounded to eliminate sharp corners and abrupt changes in the surface, so as to reduce the loss caused by signal reflection, scattering and edge diffraction.

[0037] After completing the initial digital design of the outer shell, to obtain the input parameters required for subsequent counterweight optimization, a 3D model of the sensor is created in 3D modeling software based on the actual sensor structure, and the corresponding material density is assigned. The sensor mass is calculated or assigned based on actual measurement results. The sensor model is then assembled into the sensor cavity 21 according to the designed position, and the assembly relationship is fixed. The density of the empty outer shell 1 and the fixed support 2 is assigned in the 3D modeling software. And calculate the initial total mass M of the empty shell 1, fixed bracket 2 and sensor without the counterweight 3 installed. s0 and the initial centroid C s0 .like Figure 4 As shown, the sensor housing 1 is divided into an upper housing 11 and a lower housing 12 by a splitting operation, and the fixing bracket 2 is divided into upper and lower parts. The upper part includes a support shaft 22 and a cover 211 of the sensor cavity 21, and the lower part includes five other support shafts 22, a sensor cavity 212 without a cover, and a counterweight cavity 23. Positioning posts 121, 122, and 2111 are stretched out at the joint surface of the upper housing 11 and the lower housing 12, the sensor cavity 212, and the cover 211, respectively, and positioning holes 111, 114, and 2121 are cut out for precise positioning and secure installation.

[0038] S32 weight optimization

[0039] The optimization of the sensor housing system counterweight includes two stages, and the optimization process is as follows: Figure 5 As shown:

[0040] Phase 1: Establish and solve the counterweight size optimization model to obtain the preliminary design dimensions of the counterweight. .

[0041] Known parameters: Initial total mass M of the housing system and sensor (excluding counterweights) s0 With the initial centroid C s0 Target crushed stone mass M t With the center of mass C t Density of counterweight material The center coordinate P of the i-th counterweight cavity i The maximum allowable size of the i-th counterweight (Determined by the initial counterweight cavity size).

[0042] Decision variables: Characteristic dimensions of the six counterweights Preferably, for a cubic counterweight, the characteristic dimension is the side length of the cube, and for a spherical counterweight, the characteristic dimension is the diameter of the sphere.

[0043] Total mass of sensor, housing system and counterweight

[0044] (1)

[0045] In the formula, the mass m of the i-th counterweight is... i (L i ) is its size L i The function, preferably, for cubic counterweights, the mass of each counterweight is

[0046] (2)

[0047] For the spherical counterweights, the mass of each counterweight is

[0048] (3)

[0049] The total center of mass of the sensor, housing system, and counterweight is

[0050] (4)

[0051] To select a more practical solution, the objective function after adding a regularization term is:

[0052] (5)

[0053] (6)

[0054] Parameter description:

[0055] The characteristic length of the gravel is used to normalize the centroid error;

[0056] Δx, Δy, and Δz represent the maximum span of the gravel point cloud along the X, Y, and Z axes, respectively.

[0057] α is the quality matching weighting coefficient, which adjusts the importance of the relative error between the total system mass and the target mass;

[0058] β is the centroid matching weight coefficient, which adjusts the importance of the relative error between the system's total centroid and the target centroid;

[0059] λ is the regularization weight coefficient, used to control the uniformity of the counterweight size, avoid extreme size solutions (such as one counterweight being particularly large while the others are zero), and improve the stability and manufacturing feasibility of the solution.

[0060] The constraints mainly consider the dimensional constraints of the counterweight:

[0061] (7)

[0062] Solution: The weight parameters can be adjusted as needed; α=0.6, β=0.35, λ=0.05 can be selected. The maximum side length is determined based on the actual size of the counterweight cavity. A sequential quadratic programming algorithm is used to solve the optimization model, obtaining preliminary optimization results for the counterweight block size. Numerical calculation tools such as the built-in fmincon function in MATLAB (configured with the SQP algorithm) can be used to solve the problem. After solving the optimization model, the preliminary side length of the i-th counterweight block is output. and quality .

[0063] Second stage: Adjust the size of the counterweight cavity based on the optimization results of the first stage, update the shell parameters, and iteratively solve for optimization.

[0064] The adjustment of the counterweight cavity size involves adjusting the size of each counterweight cavity based on the preliminary optimization results to match the corresponding counterweight block.

[0065] (8)

[0066] in, The preset assembly gap can be set to 0.1~1.0mm to ensure that the counterweight can be smoothly installed into the counterweight cavity and accurately aligned.

[0067] The updated shell parameters refer to updating the size of the i-th counterweight cavity to D in the 3D software. i Recalculate the total mass M of the housing system including the outer shell 1, the fixed bracket 2, and the sensor without the counterweight 3 installed. s and center of mass C s Due to the fine-tuning of the cavity size of the counterweight chamber 23, M s and C s Compared to M s0 and C s0 There will be minor changes.

[0068] The iterative solution optimization is based on the updated M. sand C s Replace initial parameter M s0 and C s0 Run the counterweight size optimization model again to calculate the optimized counterweight size result L. i Corresponding mass m i and the total mass M of the optimized housing system and sensor total Total mass center C total Calculate the relative quality error ε. m and the absolute error of the centroid ε c And compare it with the preset engineering accuracy threshold. Relative quality error ε m Reflects the relative deviation between the total mass of the outer casing system and sensors and the mass of the target debris:

[0069] (9)

[0070] absolute error of centroid ε c Reflects the positional deviation in three-dimensional space between the total centroid of the outer shell system and the sensor and the centroid of the target debris:

[0071] (10)

[0072] In the formula: The total centroid C total The three-dimensional coordinates For the target gravel centroid C t The three-dimensional coordinates.

[0073] If the relative mass error ε m and the absolute error of the centroid ε c If the preset threshold requirements are met, the models of shell 1 and fixed bracket 2 are updated according to the optimization results, and the additive manufacturing file is output. Otherwise, the design parameters of shell 1 and fixed bracket 2 are adjusted, and the optimization model is solved again until an optimization solution that meets the requirements is obtained.

[0074] S4 Shell System Fabrication and Assembly

[0075] Based on the counterweight dimensions obtained from the final optimization solution in step S3, six counterweights are fabricated using precision machining technology and the counterweight material selected in step S1. The outer shell 1 and the fixing bracket 2 are integrally formed or manufactured separately using selective laser sintering 3D printing technology. During printing, the segmented model is imported into the equipment. Typical printing parameters can be set as follows: layer thickness 0.1mm, laser power 30W, and scanning speed 2000mm / s.

[0076] Housing assembly process: Place the sensor in the sensor cavity 21, ensuring the sensor antenna faces the thin-walled area of ​​the housing, avoiding direct contact with the support shaft 22 and counterweight 3, and fix it with epoxy resin adhesive; according to the correspondence between counterweight 3 and counterweight cavity 23 determined by the optimization results, fix the six counterweights to their respective counterweight cavities 23 with epoxy resin adhesive; align the mating surfaces of the upper housing 11, lower housing 12, and sensor cavity using positioning posts 121, 122, 2111 and positioning holes 111, 114, 2121, and fix them with epoxy resin adhesive; apply silicone sealant to the mating surfaces of the upper housing 11 and lower housing 12 to form a continuous sealing ring, and cure it at an appropriate temperature until completely sealed to achieve a good waterproof effect.

[0077] The assembly scheme provided in this embodiment ensures both the positioning accuracy of the internal structure and the robustness of the connection between the outer shell and the internal structure. Through reasonable structural optimization and assembly sequence, this design scheme achieves convenient assembly of complex internal structures and precise mass centroid matching control. Simultaneously, this scheme reduces signal loss, thus providing a concealed, reliable, and highly accurate sensor solution for in-situ monitoring of bulk materials.

Claims

1. A particle sensor housing system, characterized in that, include: The shell (1) has an external geometry that replicates the shape of the target gravel and has an internal cavity; the fixed support (2) is constructed in the internal cavity of the shell (1) and includes a support shaft (22), a sensor cavity (21) for accommodating and fixing the sensor, and multiple independent counterweight cavities (23); and multiple counterweights (3) are fixed in the corresponding counterweight cavities (23); wherein the size and spatial distribution of the counterweights (3) are optimized so that the total mass and center of mass of the entire system including the sensor, the shell (1), the fixed support (2) and all the counterweights (3) match the mass and center of mass of the target gravel.

2. The sensor housing system according to claim 1, characterized in that, The support shaft (22) of the fixed bracket (2) extends outward from the sensor cavity (21) in multiple different directions, and the counterweight cavity (23) is disposed on the support shaft (22).

3. The sensor housing system according to claim 1, characterized in that, The empty shell (1) and the fixed bracket (2) are made of a first material with high strength and low dielectric loss, and the counterweight (3) is made of a second material with low dielectric loss and high density.

4. The sensor housing system according to claim 3, characterized in that, The first material is nylon PA12, and the second material is alumina ceramic or calcium tungstate.

5. The sensor housing system according to claim 1, characterized in that, The region on the empty shell (1) corresponding to the main radiation direction of the sensor antenna is an optimized thin-walled region, and the connection between the thin-walled region and the surrounding shell adopts a smooth arc transition.

6. A method for configuration optimization and fabrication of a sensor housing system according to any one of claims 1-5, characterized in that, Includes the following steps: S1: Obtain the geometric shape data and mass M of the target crushed stone. t and center of mass C t S2: Based on the geometric shape data, perform digital design to form a hollow shell (1) model with an internal cavity, and design a fixed bracket (2) model containing a sensor cavity (21), a support shaft (22) and multiple counterweight cavities (23) in the cavity; S3: Establish a counterweight optimization model, using the size of each counterweight block (3) as the decision variable, and the total mass M of the entire sensor and shell system as the decision variable. total With M t Error, total centroid C total With C t Minimize the error to solve for the optimization objective and obtain the optimal size of each counterweight (3); S4: Make the empty shell (1), fixed bracket (2) and counterweight (3) according to the optimal size and assemble them.

7. The method according to claim 6, characterized in that, In step S2, the center of the sensor cavity (21) is set at the centroid C of the target debris. t At this location, the outer wall of the sensor cavity (21) is prismatic, and the inner cavity is cuboid.

8. The method according to claim 6 or 7, characterized in that, In step S3, the objective function of the counterweight optimization model includes a mass matching error term, a centroid matching error term, and a size uniformity regularization term. The objective function is in the form of: Among them, L i Let M be the characteristic dimension of the i-th counterweight. total For the total mass of the sensor and housing system, C total The center of mass of the sensor and housing system. Δx, Δy, and Δz represent the maximum span of the target crushed stone in the X, Y, and Z axes, respectively, and α, β, and λ are the weighting coefficients for mass error, centroid error, and dimensional uniformity, respectively. max i Let be the maximum allowable size of the i-th counterweight.

9. The method according to claim 6, characterized in that, Step S3 also includes an iterative optimization process: adjust the counterweight cavity size according to the counterweight block size obtained from the preliminary solution, update the mass and centroid parameters of the empty shell and the fixed support, and run the counterweight optimization model again to solve until the error of the total mass and centroid of the entire shell system and the sensor relative to the target gravel meets the preset threshold.

10. The method according to claim 6, characterized in that, Step S2 further includes: determining the corresponding region on the empty shell (1) model according to the main radiation direction of the sensor's antenna, and performing wall thickness reduction and corner rounding on the region model to form a thin-walled region.

Images