CN115114832A - Multi-source multi-scale intelligent grading early warning method and system for bridge - Google Patents
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Abstract
The invention discloses a multi-source multi-scale intelligent grading early warning method and a system for a bridge, wherein the method comprises the following steps: establishing a bridge finite element model and a midspan model with the maximum bridge deflection; analyzing the influence of various different influence factors on the downwarping of the bridge based on the constructed model to obtain internal forces of the bridge under different downwarping degrees, further analyzing the corresponding crack condition of the bridge, and simultaneously determining a mid-span deflection threshold value of the bridge; dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade; and constructing corresponding relations among various different influence factors, deflection and crack change, determining the actual deflection and crack conditions of the bridge according to the actual values of the various different influence factors, and further determining the corresponding risk evaluation grade. The method and the device realize the graded early warning of the continuous rigid frame bridge downwarping and cracks, select corresponding counter measures according to the grades of the continuous rigid frame bridge downwarping and cracks, and ensure the safety and the durability of the bridge structure in service period.
Description
Technical Field
The invention relates to the technical field of bridge management, in particular to a multi-source multi-scale intelligent grading early warning method and system for a bridge.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The continuous rigid frame bridge is a continuous beam bridge formed by solidifying pier beams, generally adopts a prestressed concrete structure, has more than two main piers and adopts a pier beam solidifying system. The prestressed concrete continuous rigid frame bridge is widely applied to bridge engineering due to the advantages of large structural rigidity, good driving smoothness and low manufacturing cost.
However, in the long-term service process of the continuous rigid frame bridge, with the increase of the service life, the span of the continuous rigid frame is continuously bent downwards, which causes the bad line shape in the operation period of the bridge to cause the uncomfortable feeling of the driving passengers and even endanger the driving safety. Namely, various loads after the bridge is communicated with the vehicle cause the bending of the beam body to cause the downward displacement in the midspan, namely the downward deflection, and the midspan serious downward deflection problem in the continuous rigid frame bridge occurs, so that the safety and the durability of the bridge structure are influenced, and the further application of the continuous rigid frame bridge in the engineering is limited. Therefore, downwarping prediction risk assessment and early warning research are carried out on the continuous rigid frame bridge, and the method has important significance for guaranteeing the safety and the durability of the bridge structure in service period.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a multi-source multi-scale intelligent grading early warning method and system for a bridge, which can realize grading early warning for the downwarp and cracks of a continuous rigid frame bridge, select corresponding countermeasures according to the grade of the downwarp and cracks of the continuous rigid frame bridge and ensure the safety and durability of a bridge structure in service.
In a first aspect, the present disclosure provides a multi-source multi-scale intelligent hierarchical early warning method for a bridge:
a multi-source and multi-scale intelligent bridge grading early warning method comprises the following steps:
establishing a bridge finite element model and a midspan model with the maximum bridge deflection according to the actual design data of the bridge;
analyzing the influence of various different influence factors on the downwarping of the bridge based on a bridge finite element model to obtain internal forces applied to the bridge under different downwarping degrees, and simultaneously determining a mid-span deflection threshold value of the bridge;
analyzing the crack condition of the bridge under different downwarping degrees by combining internal forces of the bridge under different downwarping degrees and different downwarping degrees based on a midspan model with the maximum bridge deflection;
dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade;
and constructing a corresponding relation between various different influence factors and the deflection and crack change of the bridge, determining the actual deflection and crack condition of the bridge according to the actual values of the various different influence factors, and further determining the corresponding risk evaluation grade.
According to the further technical scheme, based on MIDAS large-scale finite element software, an initial finite element model of the steel bridge is established according to the structural geometric dimension, the member section and position and the material property in design data.
According to a further technical scheme, the influencing factors comprise loading age, environment relative humidity, prestress loss rate, fracture rigidity reduction rate and overweight rate.
According to the further technical scheme, the loading age and the environment relative humidity are in inverse proportion to the bridge deflection, and the prestress loss rate, the crack stiffness reduction rate and the overweight rate are in direct proportion to the bridge deflection.
In a further technical scheme, the step-over deflection threshold value of the bridge is determined by combining a deflection span ratio specified by a standard requirement to obtain the step-over deflection threshold value of the bridge.
According to the further technical scheme, the internal force applied to the bridge under different downwarping degrees is input into the midspan model with the maximum bridge deflection, and the bridge crack conditions under different downwarping degrees are obtained according to the midspan model with the maximum bridge deflection.
According to a further technical scheme, the risk assessment grade is as follows: setting the deflection threshold value to be 60% or below as a yellow early warning, and taking no measures; setting orange early warning at 60-80% of the deflection threshold value, and properly taking reinforcement measures; and setting the deflection threshold value to be more than 80% as a red early warning, and taking reinforcement measures as soon as possible.
In a second aspect, the present disclosure provides a hierarchical early warning system of multisource multiscale intelligence of bridge, include:
the model building module is used for building a bridge finite element model and a midspan model with the largest bridge deflection according to the actual design data of the bridge;
the risk assessment grade division module is used for analyzing the influence of various different influence factors on the downwarping of the bridge based on the bridge finite element model to obtain the internal force applied to the bridge under different downwarping degrees and simultaneously determining the mid-span deflection threshold value of the bridge; analyzing the crack condition of the bridge under different downwarping degrees by combining internal forces of the bridge under different downwarping degrees and different downwarping degrees based on a midspan model with the maximum bridge deflection; dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade;
the data processing module is used for constructing corresponding relations among various different influence factors, bridge deflection and crack change;
and the risk evaluation module is used for determining the actual deflection and crack conditions of the bridge according to the actual values of various different influence factors, and further determining the corresponding risk evaluation grade.
In a third aspect, the present disclosure also provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of the first aspect.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
The above one or more technical solutions have the following beneficial effects:
1. the invention provides a multi-source multi-scale intelligent grading early warning method and system for a bridge.
2. The invention provides a multi-source multi-scale intelligent grading early warning method and system for a bridge, which are used for analyzing cracks to obtain a reasonable threshold value of actual deflection of a continuous rigid frame bridge, realizing grading early warning of downwarping and cracks of the continuous rigid frame bridge, selecting corresponding countermeasures according to the grade of the downwarping and cracks of the continuous rigid frame bridge and ensuring the safety and durability of a bridge structure in a service period.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is an overall framework diagram of a multi-source multi-scale intelligent hierarchical early warning method for a bridge according to an embodiment of the invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The embodiment provides a multi-source multi-scale intelligent grading early warning method for a bridge, which comprises the following steps:
as shown in fig. 1, a multi-source multi-scale intelligent graded early warning method for a bridge comprises the following steps:
step 1, establishing a bridge finite element model and a midspan model with the maximum bridge deflection according to actual design data of a bridge;
step 2, analyzing the influence of various different influence factors on the downwarping of the bridge based on a bridge finite element model to obtain internal forces of the bridge under different downwarping degrees, and simultaneously determining a midspan deflection threshold value of the bridge;
step 3, analyzing the crack condition of the bridge under different downwarping degrees based on the midspan model with the maximum bridge deflection by combining different downwarping degrees and internal forces applied to the bridge under different downwarping degrees;
step 4, dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade;
and 5, constructing corresponding relations among various different influence factors, the deflection of the bridge and the change of the crack, determining the actual deflection and the crack condition of the bridge according to the actual values of the various different influence factors, and further determining the corresponding risk evaluation grade.
In this embodiment, in step 1, the whole bridge is analyzed, and a bridge finite element model and a midspan model with the largest bridge deflection are established according to the actual design data of the bridge.
Finite element analysis is an effective numerical analysis method in structural mechanics analysis, and is often applied to the technical fields of hydraulic engineering, civil engineering, bridges, machinery, motors, mechanics, physics and the like. In the prior art, software compiled based on a finite element analysis algorithm, namely finite element analysis software, comprises a plurality of types, and common general finite element software comprises Midas, Abaqus, LMS-Samtech, Algor, Femap/NX Nastran, Hypermesh, COMSOL Multiphysics, FEPG and the like. In this embodiment, the design data and the construction data of the actual engineering of the bridge are mainly the shape, the material, the size, the construction stage, the construction step, the environmental factors, etc. of the bridge, and the MIDAS finite element software is applied to construct the finite element model of the bridge. Specifically, based on MIDAS large finite element software, a bridge finite element model is established according to the structural geometric dimension, the member section and position and the material property in the design data, and the specific flow is as follows: firstly, establishing all nodes of a finite element model by using node coordinates of a steel bridge structure; then, establishing all units of the finite element model according to the design section, the material parameters and the positions; and finally, applying coupling and constraint to the nodes according to constraint conditions to obtain the bridge finite element model.
Also, in this embodiment, a midspan model with the largest bridge deflection is constructed by using ABAQUS finite element software according to design data, construction data and the like of the actual engineering of the bridge.
The bridge finite element model is used for analyzing the whole stress and displacement deformation conditions of the bridge, and the midspan model with the largest bridge deflection is used for analyzing the midspan local crack conditions of the bridge.
The bridge crack problem can seriously harm the durability and the bearing capacity of roads and bridges, and cracks with different degrees have different damage degrees, so that the degree is light, the driving comfort of a driver can be hindered, and the degree is heavy, and the vehicle and personal safety can be directly harmed. That is to say, the crack has a great influence on the overall rigidity and the usability safety of the bridge, and the performance of the bridge can be better mastered by performing overall analysis on the bridge and combining the crack analysis at the midspan maximum deflection position.
In the step 2, based on the constructed bridge finite element model, the influence of various different influence factors on the downwarping of the bridge is analyzed to obtain the internal force applied to the bridge under different downwarping degrees, and meanwhile, the midspan deflection threshold value of the bridge is determined.
Specifically, based on the established MIDAS bridge finite element model, the influence of various common influence factors on the downwarping of the bridge is analyzed, firstly, according to the research current situation of the continuous rigid frame bridge, the actual situation of the continuous rigid frame bridge engineering is combined, various different influence factors influencing the downwarping of the bridge are determined, the influence factors are screened, the factors with smaller influence are eliminated, the factors with larger influence are reserved, and the influence factors with larger influence are finally determined. The selection of the above-mentioned multiple different influence factors affecting the downwarping of the bridge is determined according to the specific actual conditions of the project, and similarly, for the screening of the influence factors, a technician may also determine by himself or herself according to the specific project practice, and reject some influence factors or not, and this embodiment is not limited herein. In the embodiment, the finally determined influence factors with large influence comprise loading age, environment relative humidity, prestress loss rate, fracture rigidity reduction rate and overweight rate.
After determining influence factors, analyzing the influence of various different influence factors on the downwarping of the bridge, adjusting the numerical values of the influence factors based on the bridge finite element model, such as adjusting the loading age days, the environmental relative humidity percentage, the prestress loss percentage, the rigidity reduction percentage and the overweight rate, adjusting various working conditions by changing the numerical values of the influence factors, observing the downwarping change rule of the bridge under different working conditions, and obtaining the internal force of the bridge under different downwarping degrees through MIDAS finite element software.
Wherein, the higher the relative humidity of the environment is, the smaller the downwarping is; the longer the loading age, the smaller the downwarp; the higher the prestress loss rate is, the larger the downwarping is; the higher the fracture rigidity reduction rate is, the larger the downwarping is; the higher the overweight rate, the greater the downwarp. Namely, the loading age and the environment relative humidity are in inverse proportion to the bridge deflection, and the prestress loss rate, the crack stiffness reduction rate and the overweight rate are in direct proportion to the bridge deflection.
The step-over deflection threshold value of the bridge is determined by acquiring the step-over deflection threshold value of the continuous rigid frame bridge under study by combining with a deflection span ratio specified by a standard requirement, wherein the specified deflection span ratio is a deflection span ratio specified according to the highway reinforced concrete and prestressed concrete bridge design standard (JTG3362-2018), and the deflection span ratio should not exceed 1/600.
And 3, analyzing the crack condition of the bridge under different downwarping degrees based on the midspan model with the maximum bridge deflection by combining different downwarping degrees and internal forces applied to the bridge under different downwarping degrees.
Inputting the internal force received by the bridge under different downwarping degrees obtained in the step 2 into a midspan model with the maximum bridge deflection, which is constructed by ABAQ US finite element analysis software, wherein the midspan model with the maximum bridge deflection simulates the crack condition of the bridge, and the deflection and the crack condition of the bridge corresponding to different working conditions are determined by adjusting the number of days of loading age, the environmental relative humidity percentage, the prestress loss percentage, the stiffness reduction percentage and the overweight rate to change within a certain numerical range. In fact, the larger the downwarp value of the continuous rigid frame bridge, the wider the crack, and the more severe the crack.
And 4, dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain the risk evaluation grade. And (3) respectively acquiring corresponding bridge mid-span deflection threshold values and crack conditions under different working conditions through the step 2 and the step 3, and dividing the bridge mid-span deflection threshold values according to the crack conditions under different working conditions to obtain risk assessment grades.
The analysis of the crack condition can be determined according to specific actual working conditions, such as dividing the crack condition into harmless, slight and serious according to the width of the crack. Dividing the mid-span deflection threshold of the bridge according to the crack condition to realize the division of risk evaluation grades, wherein in the embodiment, yellow early warning is set to reach 60% or below of the deflection threshold, and the deflection and the crack can be ignored at the moment without any measures; setting the deflection threshold value to be 60-80% as orange early warning, and adopting appropriate reinforcement measures; and setting the deflection threshold value to be more than 80% as a red early warning, and taking reinforcement measures as soon as possible.
And 5, constructing corresponding relations among various different influence factors, the deflection of the bridge and the change of the crack, determining the actual deflection and the crack condition of the bridge according to actual values of the various different influence factors, and further determining the corresponding risk evaluation grade.
Specifically, a database corresponding to the internal force, deflection and crack is constructed according to the numerical values of various different influence factors obtained in the steps and the bridge deflection and crack conditions under the numerical values, and Python is adopted for data processing to construct the corresponding relation between various different influence factors and the bridge deflection and crack changes. On the basis, the current actual deflection and crack conditions of the bridge can be determined according to actual multiple or single influence factor values of the bridge, the risk evaluation grade of the bridge is further determined by combining the determined deflection threshold, and whether reinforcement measures are taken or not is selected according to the risk evaluation grade.
According to the scheme, the reasonable threshold value of the actual deflection of the continuous rigid frame bridge is obtained through analyzing the cracks, the grading early warning of the continuous rigid frame bridge downwarping and cracks is realized, corresponding countermeasures are selected according to the grade of the continuous rigid frame bridge downwarping and cracks, and the safety and the durability of the bridge structure in the service period are guaranteed.
Example two
This embodiment provides a hierarchical early warning system of bridge multisource multiscale intelligence, includes:
the model building module is used for building a bridge finite element model and a midspan model with the largest bridge deflection according to the actual design data of the bridge;
the risk assessment grade division module is used for analyzing the influence of various different influence factors on the downwarping of the bridge based on the bridge finite element model to obtain the internal force applied to the bridge under different downwarping degrees and simultaneously determining the mid-span deflection threshold value of the bridge; analyzing the crack condition of the bridge under different downwarping degrees by combining internal forces of the bridge under different downwarping degrees and different downwarping degrees based on a midspan model with the maximum bridge deflection; dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade;
the data processing module is used for constructing corresponding relations among various different influence factors, bridge deflection and crack change;
and the risk evaluation module is used for determining the actual deflection and crack conditions of the bridge according to the actual values of various different influence factors, and further determining the corresponding risk evaluation grade.
EXAMPLE III
The embodiment provides an electronic device, which comprises a memory, a processor and computer instructions stored in the memory and run on the processor, wherein when the computer instructions are run by the processor, the steps in the bridge multi-source multi-scale intelligent hierarchical early warning method are completed.
Example four
The embodiment also provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the steps in the bridge multi-source multi-scale intelligent hierarchical early warning method are completed.
The steps involved in the second to fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the related description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A multi-source multi-scale intelligent grading early warning method for a bridge is characterized by comprising the following steps:
establishing a bridge finite element model and a midspan model with the maximum bridge deflection according to the actual design data of the bridge;
analyzing the influence of various different influence factors on the downwarping of the bridge based on a bridge finite element model to obtain internal forces applied to the bridge under different downwarping degrees, and simultaneously determining a mid-span deflection threshold value of the bridge;
analyzing the crack condition of the bridge under different downwarping degrees by combining internal forces of the bridge under different downwarping degrees and different downwarping degrees based on a midspan model with the maximum bridge deflection;
dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade;
and constructing corresponding relations among various different influence factors, the deflection of the bridge and the change of the crack, determining the actual deflection and the crack condition of the bridge according to the actual values of the various different influence factors, and further determining the corresponding risk assessment grade.
2. The multi-source multi-scale intelligent grading early warning method for the bridge as claimed in claim 1, wherein an initial finite element model of the steel bridge is established based on MIDAS large finite element software according to the structural geometry, the member section and position and the material properties in design data.
3. The multi-source multi-scale intelligent grading early warning method for the bridge according to claim 1, wherein the influencing factors comprise loading age, environment relative humidity, prestress loss rate, fracture rigidity reduction rate and overweight rate.
4. The multi-source multi-scale intelligent bridge grading early warning method as claimed in claim 3, wherein the loading age and the environmental relative humidity are inversely proportional to the bridge deflection, and the prestress loss rate, the crack stiffness reduction rate and the overweight rate are proportional to the bridge deflection.
5. The multi-source multi-scale intelligent grading early warning method for the bridge as claimed in claim 1, wherein the step of determining the bridge mid-span deflection threshold value is to obtain the bridge mid-span deflection threshold value in combination with a deflection span ratio specified by a standard requirement.
6. The multi-source multi-scale intelligent grading early warning method for the bridge as claimed in claim 1, wherein the analysis of the crack condition of the bridge under different downwarping degrees based on the midspan model with the maximum deflection of the bridge and the combination of the internal forces applied to the bridge under different downwarping degrees means:
and inputting the internal force received by the bridge under different downwarping degrees into the midspan model with the maximum bridge deflection, and obtaining the crack condition of the bridge under different downwarping degrees according to the midspan model with the maximum bridge deflection.
7. The multi-source multi-scale intelligent graded early warning method for the bridge as claimed in claim 1, wherein the risk assessment grade is as follows: setting the deflection threshold value to be 60% or below as a yellow early warning, and taking no measures; setting orange early warning at 60-80% of the deflection threshold value, and properly taking reinforcement measures; and setting the deflection threshold value to be more than 80% as a red early warning, and taking reinforcement measures as soon as possible.
8. The utility model provides a hierarchical early warning system of bridge multisource multiscale intelligence which characterized by includes:
the model building module is used for building a bridge finite element model and a midspan model with the largest bridge deflection according to the actual design data of the bridge;
the risk assessment grade division module is used for analyzing the influence of various different influence factors on the downwarping of the bridge based on the bridge finite element model to obtain the internal force applied to the bridge under different downwarping degrees and simultaneously determining the mid-span deflection threshold value of the bridge; analyzing the crack condition of the bridge under different downwarping degrees by combining internal forces of the bridge under different downwarping degrees and different downwarping degrees based on a midspan model with the maximum bridge deflection; dividing the mid-span deflection threshold value of the bridge based on the crack condition of the bridge to obtain a risk evaluation grade;
the data processing module is used for constructing corresponding relations among various different influence factors, bridge deflection and crack change;
and the risk evaluation module is used for determining the actual deflection and crack conditions of the bridge according to the actual values of various different influence factors, and further determining the corresponding risk evaluation grade.
9. An electronic device, characterized by: the bridge multi-source multi-scale intelligent grading early warning method comprises a memory, a processor and computer instructions stored on the memory and run on the processor, wherein when the computer instructions are run by the processor, the steps of the bridge multi-source multi-scale intelligent grading early warning method as claimed in any one of claims 1 to 7 are completed.
10. A computer-readable storage medium characterized by: the method is used for storing computer instructions, and when the computer instructions are executed by a processor, the steps of the bridge multi-source multi-scale intelligent grading early warning method according to any one of claims 1-7 are completed.
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| PCT/CN2022/112799 WO2024016415A1 (en) | 2022-07-22 | 2022-08-16 | Bridge multi-source, multi-scale intelligent hierarchy early warning method and system |
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| CN117933730B (en) * | 2024-03-21 | 2024-06-07 | 四川华腾公路试验检测有限责任公司 | Comprehensive bridge safety assessment method based on random forest |
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| CN103196642A (en) * | 2013-03-27 | 2013-07-10 | 中国人民解放军军事交通学院 | Method for quickly detecting and assessing heavy equipment passing ability of small-and-medium span concrete bridge |
| CN104677666B (en) * | 2015-03-18 | 2017-05-17 | 西安公路研究院 | Continuous rigid frame bridge prestress damage identification method based on deflection monitoring |
| CN109101734B (en) * | 2018-08-16 | 2019-12-24 | 交通运输部公路科学研究所 | Prediction method for continuous rigid frame bridge downwarping risk |
| CN110263461B (en) * | 2019-06-26 | 2023-07-21 | 江苏工程职业技术学院 | Bridge safety monitoring and early warning system based on BIM |
| DE102021105643B3 (en) * | 2021-03-09 | 2022-04-21 | Jörn GmbH | Method for determining the beginning of a wear-related remaining service life of an elastically deformable component, as a structural part and/or bearing part of a device |
| CN114417470A (en) * | 2022-01-05 | 2022-04-29 | 福州市公路事业发展中心 | Bridge crack safety evaluation method and device based on BIM |
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2022
- 2022-07-22 CN CN202210868232.3A patent/CN115114832A/en active Pending
- 2022-08-16 WO PCT/CN2022/112799 patent/WO2024016415A1/en unknown
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| WO2024016415A1 (en) | 2024-01-25 |
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