Bridge jig frame precision intelligent detection and comparison method based on design benchmark

Abstract

The invention discloses a bridge jig frame precision intelligent detection and comparison method based on a design benchmark, and relates to the technical field of bridge detection, and the method comprises the steps: obtaining a weather jig frame environment based on the design benchmark of a bridge jig frame and weather influence data; using a parameter calibration method to obtain a standard parameter group corresponding to the laser scanner in each weather jig frame environment; controlling a laser scanner to perform laser scanning and precision detection comparison based on the real-time jig frame environment; the method is used for solving the problems that in an existing bridge jig frame precision intelligent detection and comparison method, when a laser scanner is used for obtaining point cloud data, parameters of the laser scanner are not adjusted on the basis of environment changes, so that a large error exists between a model generated by the point cloud data and an actual bridge, and the accuracy of the point cloud data is influenced. And the accuracy of high-precision detection and comparison based on the point cloud data is influenced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge detection, in particular to a bridge bed-jig precision intelligent detection comparison method based on design benchmarks. BACKGROUND

[0002] The bridge bed-jig is a special process equipment used for supporting, positioning and assembling bridge components in bridge engineering, and its main functions include ensuring accurate docking of components, reducing welding deformation and improving construction efficiency, etc. The bridge bed-jig precision intelligent detection comparison is a process of real-time monitoring and data verification of the geometric parameters of the bed-jig structure in construction through multi-sensor fusion and AI technology. The equipment used in the process usually includes static level, laser scanner and micro-strain sensor, etc.

[0003] The existing method for bridge bed-jig precision intelligent detection comparison usually realizes accurate positioning of the robot on the bridge after obtaining the point cloud data of the target bridge, and uses the robot to detect the bed-jig and the bridge components on the bed-jig with high precision. Although this improved method can guarantee the accuracy of the comparison between the measured data and the theoretical model, it does not consider the influence of the environment on the acquisition of point cloud data, resulting in that the parameters of the laser scanner are not adjusted based on the changes in the environment when the point cloud data is acquired by the laser scanner, causing the influence of environmental factors on laser transmission and signal reception, which makes the model generated from the point cloud data have a large error compared with the actual bridge, and further affects the accuracy of high-precision detection comparison based on point cloud data. For example, in the patent application with the publication number CN120141498A, a multi-sensor information fusion bridge detection robot positioning method is disclosed, which screens the real shooting area by combining the point cloud collected by the robot in real time with the similarity of the bridge pier point cloud position and the similarity of the image corner point distribution, realizes accurate positioning of the robot, and uses the accurately positioned robot to detect the bed-jig and the bridge components on the bed-jig with high precision. Other improvements of the method for bridge bed-jig precision intelligent detection comparison usually focus on the improvement of model assembly and coincidence, but still cannot solve the problem that the parameters of the laser scanner are not adjusted based on the changes in the environment when the point cloud data is acquired by the laser scanner, causing the influence of environmental factors on laser transmission and signal reception, which makes the model generated from the point cloud data have a large error compared with the actual bridge, and further affects the accuracy of high-precision detection comparison based on point cloud data. Therefore, it is necessary to improve the existing bridge bed-jig precision intelligent detection comparison method. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the prior art by proposing a bridge bed precision intelligent detection comparison method based on design benchmarks, which is used to solve the problem that in the existing bridge bed precision intelligent detection comparison method, when using a laser scanner to obtain point cloud data, the parameters of the laser scanner are not adjusted based on environmental changes, causing the influence of environmental factors on laser transmission and signal reception, resulting in a large error between the model generated from the point cloud data and the actual bridge, and further affecting the accuracy of high-precision detection comparison based on point cloud data.

[0005] To achieve the above-mentioned purpose, the present application provides a bridge bed precision intelligent detection comparison method based on design benchmarks, comprising the following steps: Based on the design benchmarks of the bridge bed, a digital twin model corresponding to the bridge bed is constructed, denoted as the bed model, and the virtual environment in which the bed model is located is denoted as the bed environment; based on the geographical location of the bridge bed, weather influence data is obtained, and based on the weather influence data, a plurality of weather influence parameters are obtained; the weather influence parameters are added to the bed environment, and a plurality of weather bed environments are obtained; Based on the instrument parameters of the laser scanner, a digital twin model corresponding to the laser scanner is constructed, denoted as the scanning model; the scanning model is placed in each weather bed environment, and the standard parameter set of the laser scanner in each weather bed environment is obtained using a parameter calibration method; The weather influence data when the bridge bed precision detection is performed is denoted as real-time weather data; based on the weather bed environment and the real-time weather data, a real-time bed environment is obtained, and the laser scanner is controlled based on the standard parameter set corresponding to the real-time bed environment to perform laser scanning and precision detection comparison on the bridge bed.

[0006] Further, based on the geographical location of the bridge bed, the weather influence data includes: The geographical location of the bridge bed is denoted as the bridge location, the weather data of the bridge location within one year is obtained based on the weather data, and all weather in the weather data is denoted as the bridge environment weather; for any one bridge environment weather, the date corresponding to the weather data when the weather is the bridge environment weather is obtained and denoted as the bridge environment date; the temperature, light intensity and dust concentration recorded by the meteorological data and environmental monitoring data at the bridge location within the bridge environment date are denoted as the weather influence data of the bridge environment date; Obtain the weather influence data of all bridge environment dates corresponding to each bridge environment weather.

[0007] Further, based on the weather influence data, a plurality of weather influence parameters are obtained, including: For any one bridge environment weather α corresponding to any one bridge environment date γ: in the weather influence data of the bridge environment date γ, the maximum and minimum values of the temperature, the light intensity and the dust concentration are respectively recorded as the intraday maximum temperature and the intraday minimum temperature, the intraday maximum light intensity and the intraday minimum light intensity, and the intraday maximum dust concentration and the intraday minimum dust concentration, and the temperature, the light intensity and the dust concentration are all recorded as weather influence parameters; The average values of the intraday maximum temperature and the intraday minimum temperature, the average values of the intraday maximum light intensity and the intraday minimum light intensity, and the average values of the intraday maximum dust concentration and the intraday minimum dust concentration are respectively recorded as the intraday average temperature, the intraday average light intensity and the intraday average dust concentration.

[0008] Further, the weather influence parameters are added to the jig environment, and a plurality of weather jig environments are obtained, including: The intraday average temperatures, the intraday average light intensities and the intraday average dust concentrations of all bridge environment dates corresponding to the bridge environment weather α are obtained, and the average value of all the intraday average temperatures is recorded as the environmental average temperature, the average value of all the intraday average light intensities is recorded as the environmental average light intensity, and the average value of all the intraday average dust concentrations is recorded as the environmental average dust concentration; the environmental average temperature, the environmental average light intensity and the environmental average dust concentration are all recorded as environmental average parameters; The bridge environment dates in which the intraday average temperature is greater than or equal to the environmental average temperature and the intraday average temperature is less than the environmental average temperature are respectively recorded as high-temperature environment dates and low-temperature environment dates; the bridge environment dates in which the intraday average light intensity is greater than or equal to the environmental average light intensity and the intraday average light intensity is less than the environmental average light intensity are respectively recorded as high-light environment dates and low-light environment dates; the bridge environment dates in which the intraday average dust concentration is greater than or equal to the environmental average dust concentration and the intraday average dust concentration is less than the environmental average dust concentration are respectively recorded as high-dust environment dates and low-dust environment dates.

[0009] Further, the key factor of the high-temperature environment date and the low-temperature environment date is recorded as the temperature, the key factor of the high-light environment date and the low-light environment date is recorded as the light intensity, the key factor of the high-dust environment date and the low-dust environment date is recorded as the dust concentration, the weather influence parameters are added to the jig environment, and a plurality of weather jig environments are obtained, further including: For any one type of date β in the high-temperature environment date, the low-temperature environment date, the high-light environment date, the low-light environment date, the high-dust environment date and the low-dust environment date: the interval formed by the maximum value and the minimum value of the key factor in all dates corresponding to the date β is recorded as the key influence interval; In the jig environment, an environment in which the key factor fluctuates within the key influence interval and the weather influence parameters other than the key factor are all environmental average parameters is added, and the jig environment at this time is recorded as the weather jig environment of the date β; Obtain all types of weather corresponding to all bridge environment weather of the day.

[0010] Further, a scanning model is placed in each weather environment, and a parameter calibration method is used to obtain a standard parameter set of the laser scanner corresponding to each weather environment. The parameters that need to be adjusted when starting the laser scanner are respectively denoted as scanning influence parameters SY1 to scanning influence parameters SY t For any weather environment: place a scanning model in the weather environment, and use a parameter calibration method to obtain a standard parameter set of the laser scanner corresponding to each weather environment. Obtain a standard parameter set of the laser scanner corresponding to each weather environment.

[0011] Further, the parameter calibration method includes: Start the scanning model to perform laser emission and scanning motion on the bed model in the weather environment, and generate a three-dimensional model from the point cloud data obtained after scanning, denoted as the laser scanning model; the similarity between the laser scanning model and the bed model is denoted as the scanning similarity; Based on the range allowed to be adjusted for all scanning influence parameters, adjust the values of all scanning influence parameters and restart the scanning model k times to perform laser emission and scanning motion, and the obtained laser scanning models are respectively denoted as candidate models DX1 to candidate models DX k , wherein for any two candidate models DX k1 and candidate model DX k2 , there is at least one scanning influence parameter δ in the scanning influence parameters corresponding to the candidate model DX k1 , whose value is different from the value of the scanning influence parameter δ corresponding to the candidate model DX k2 ; k1 and k2 are positive integers less than or equal to k and greater than or equal to 1; The scanning influence parameters SY1 to SY t of the candidate model with the maximum scanning similarity among the candidate models DX1 to DX k are denoted as the standard parameter set of the laser scanner.

[0012] Further, the weather influence data when performing bridge bed precision detection is denoted as real-time weather data; based on the weather environment and the real-time weather data, a real-time bed environment is obtained, including: The weather and weather influence data when performing bridge bed precision testing are respectively denoted as test weather and real-time weather data; Based on the value of each weather influence parameter corresponding to real-time weather data, an environment constituted by the weather influence parameters of real-time weather data is added in the jig environment, and the jig environment at this time is recorded as a real-time jig environment.

[0013] Further, the laser scanner is controlled based on the standard parameter group corresponding to the real-time jig environment to perform laser scanning on the bridge jig and precision detection comparison, which comprises: The weather of the bridge environment same as the test weather is recorded as an available environment weather; the light reflectivity, temperature and dust concentration of the bridge jig surface in all types of weather jig environments of the available environment weather are compared with the light reflectivity, temperature and dust concentration of the bridge jig surface in the real-time jig environment respectively, and the weather jig environment corresponding to the maximum value of the sum of the similarities of the light reflectivity, temperature and dust concentration in the comparison results is recorded as a permissible jig environment.

[0014] Further, the laser scanner is controlled based on the standard parameter group corresponding to the real-time jig environment to perform laser scanning on the bridge jig and precision detection comparison, which comprises: The laser scanner is used to perform laser emission and scanning operation on the bridge jig, and before starting the laser scanner, all scanning influence parameters in the laser scanner are adjusted based on the standard parameter group corresponding to the permissible jig environment. The three-dimensional model generated by the point cloud data obtained after the bridge jig is scanned by the laser scanner is recorded as a real-time scanning model, and the comparison result of the real-time scanning model and the standard model constructed based on the design reference is recorded as the precision detection comparison result of the bridge jig.

[0015] The application has the following beneficial effects: firstly, based on the design reference of the bridge jig, a jig model is constructed, and the virtual environment where the jig model is located is recorded as a jig environment; weather influence data is obtained based on the geographical position of the bridge jig, and multiple weather influence parameters are obtained based on the weather influence data; the weather influence parameters are added to the jig environment, and multiple weather jig environments are obtained, which has the advantages that by constructing the jig environment and obtaining weather image data, the constructed weather jig environment can truly reflect the state of the jig under different weather conditions, so that in subsequent analysis, parameters that need to be adjusted by the laser scanner can be obtained for different temperatures, light intensities and dust concentrations, thereby adjusting the parameters of the laser scanner based on environmental changes in actual precision comparison, reducing the error between the model generated by the point cloud data and the actual bridge, and improving the accuracy of the precision detection comparison. The application also constructs a scanning model based on the instrument parameters of the laser scanner; the scanning model is placed in each weather bed frame environment, and a parameter calibration method is used to obtain a standard parameter group of the laser scanner in each weather bed frame environment; finally, based on the weather bed frame environment and real-time weather data, a real-time bed frame environment is obtained, and the laser scanner is controlled based on the standard parameter group corresponding to the real-time bed frame environment to perform laser scanning and precision detection comparison on the bridge bed frame. The advantage is that by obtaining the standard parameter group of each weather bed frame environment, the real-time bed frame environment can be obtained during high-precision comparison, and the corresponding standard parameter group is matched to ensure that after adjusting all scanning influence parameters in the laser scanner based on the standard parameter group of the laser scanner in the standard bed frame environment, the laser scanner can adapt to the temperature, light intensity and dust concentration in the real-time bed frame environment, thereby improving the similarity of the model generated from the point cloud data to the actual bridge and the accuracy of high-precision detection comparison based on the point cloud data. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A step flowchart of the method of the application; Figure 2 A flowchart of the parameter calibration method of the application; Figure 3 A structural diagram of the electronic device of the application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0018] Embodiment 1, please refer to Figure 1 The application provides a bridge bed frame precision intelligent detection comparison method based on design benchmarks, which comprises the following steps: Step S1, based on the design benchmarks of the bridge bed frame, a digital twin model corresponding to the bridge bed frame is constructed, denoted as a bed frame model, and a virtual environment in which the bed frame model is located is denoted as a bed frame environment; weather influence data is obtained based on the geographical location of the bridge bed frame, and a plurality of weather influence parameters are obtained based on the weather influence data; the weather influence parameters are added to the bed frame environment, and a plurality of weather bed frame environments are obtained; Step S1 comprises: step S101, recording the geographical position where the bridge bed is as the bridge position, obtaining the weather data in one year at the bridge position based on the weather data, and recording all the weather existing in the weather data as the bridge environmental weather; for any one bridge environmental weather, obtaining the date corresponding to the weather in the weather data as the bridge environmental date; recording the temperature, the light intensity and the dust concentration recorded by the weather influence data of the bridge position in the meteorological data and the environmental monitoring data in the bridge environmental date as the weather influence data of the bridge environmental date; Step S102, obtaining the weather influence data of all the bridge environmental dates corresponding to each bridge environmental weather.

[0019] Step S1 further comprises: step S103, for any one bridge environmental weather a corresponding to any one bridge environmental date y: recording the maximum and minimum values of the temperature, the light intensity and the dust concentration in the weather influence data of the bridge environmental date y as the daily maximum temperature and the daily minimum temperature, the daily maximum light intensity and the daily minimum light intensity, and the daily maximum dust concentration and the daily minimum dust concentration, and recording the temperature, the light intensity and the dust concentration as the weather influence parameters; In the specific implementation process, the bridge environmental weather can be adaptively adjusted according to the geographical characteristics of the bridge position, for example, if there is a sandstorm or frost in the area where the bridge is located, the sandstorm or frost should be included in the bridge environmental weather; for example, if it has not snowed in the area where the bridge is located in the past year, the snow day should not be included in the bridge environmental weather; for example, in a data analysis, the bridge environmental weather for analysis is sunny, and the bridge environmental date is December 1; by obtaining the meteorological data and the environmental monitoring data in December 1, the maximum and minimum values of the temperature, the light intensity and the dust concentration in December 1 are obtained as 18℃ and 3℃, 48000lx and 90000lx, and 90μg / m³ and 30μg / m³; by calculation, the daily average temperature, the daily average light intensity and the daily average dust concentration are 10.5℃, 69000lx and 60μg / m³ respectively; Step S104, recording the average values of the daily maximum temperature and the daily minimum temperature, the average values of the daily maximum light intensity and the daily minimum light intensity, and the average values of the daily maximum dust concentration and the daily minimum dust concentration as the daily average temperature, the daily average light intensity and the daily average dust concentration respectively.

[0020] The step S1 further includes: a step S105 of acquiring the daily average temperature, the daily average light intensity and the daily average dust concentration of all bridge environment dates corresponding to the bridge environment weather a, and recording the average value of all daily average temperatures as an environment average temperature, the average value of all daily average light intensities as an environment average light intensity, and the average value of all daily average dust concentrations as an environment average dust concentration; and recording the environment average temperature, the environment average light intensity and the environment average dust concentration as environment average parameters; In the specific implementation process, by acquiring the environment average parameters, a measurement standard of different parameters in the same weather can be obtained based on the environment parameters in different dates, such as the same sunny day, which can be divided into a sunny day with high temperature, a sunny day with low temperature, a sunny day with high light intensity, a sunny day with low light intensity, a sunny day with high dust concentration, and a sunny day with low dust concentration, that is, the weather corresponding to the high-temperature environment date, the low-temperature environment date, the high-light environment date, the low-light environment date, the high-dust environment date and the low-dust environment date obtained in subsequent analysis; because when using a laser scanner to acquire point cloud data, temperature, light intensity and dust concentration will all affect the accuracy of the laser scanner when acquiring point cloud data, such as in a strong light environment, the laser reflection signal is too strong and easy to saturate, resulting in point cloud false points, and the reflective surface is easy to misjudge the signal, and in a weak light environment, the laser long-distance transmission signal attenuates, and dark-colored frame members are easy to miss points, therefore, by analyzing different types of dates in the same weather, the direction in which the parameters of the laser scanner need to be adjusted when using the laser scanner to acquire point cloud data in different types of dates in the weather can be obtained in subsequent analysis; For example, in the analysis of the present embodiment, the bridge environment weather is sunny, and the daily average temperature, the daily average light intensity and the daily average dust concentration corresponding to the bridge environment date of December 1 are 10.5℃, 69000lx and 60μg / m³ respectively, and the environment average light intensity obtained by data acquisition is 59000lx, so December 1 should be recorded as a high-light environment date, and through the foregoing analysis, it is obtained that in the high-light environment date, the laser reflection signal is too strong and easy to saturate, resulting in point cloud false points, and the reflective surface is easy to misjudge the signal, therefore, the laser power of the laser scanner is controlled to be self-adaptive attenuation at this time, so as to avoid the laser reflection signal being too strong and easy to saturate, in addition, the scanning frequency is also increased to increase the point cloud density, the redundant data is removed to remove the reflective false points, and the design size of the feature points is compared to ensure the accuracy, thereby improving the accuracy of acquiring point cloud data by using the laser scanner; Step S106, the bridge environment date when the daily average temperature is greater than or equal to the average ambient temperature and the daily average temperature is less than the average ambient temperature is recorded as a high-temperature environment date and a low-temperature environment date respectively; the bridge environment date when the daily average light intensity is greater than or equal to the average ambient light intensity and the daily average light intensity is less than the average ambient light intensity is recorded as a high-light environment date and a low-light environment date respectively; the bridge environment date when the daily average powder concentration is greater than or equal to the average ambient powder concentration and the daily average powder concentration is less than the average ambient powder concentration is recorded as a high-powder environment date and a low-powder environment date respectively.

[0021] The key factor of the high-temperature environment date and the low-temperature environment date is recorded as temperature, the key factor of the high-light environment date and the low-light environment date is recorded as light intensity, and the key factor of the high-powder environment date and the low-powder environment date is recorded as powder concentration, and step S1 further comprises: step S107, for any one type of date β in the high-temperature environment date, the low-temperature environment date, the high-light environment date, the low-light environment date, the high-powder environment date and the low-powder environment date: the interval formed by the maximum value and the minimum value of the key factor in all dates corresponding to the date β is recorded as a key influence interval; Step S108, in the jig environment, add the environment in which the key factor fluctuates within the key influence interval and the weather influence parameters other than the key factor are all average ambient parameters, and record the jig environment at this time as the weather jig environment of the date β; In the specific implementation process, by using the key influence interval of the key factor and the average ambient parameter of the weather influence parameter other than the key factor, the weather jig environment corresponding to each date is constructed, which can more accurately simulate all types of weather corresponding to each weather in subsequent analysis, so as to obtain the standard parameter group of the laser scanner corresponding to each weather jig environment, realize the adjustment of the parameters of the laser scanner based on the change of the environment, reduce the error between the model generated from the point cloud data and the actual bridge, and improve the accuracy of the detection comparison; Step S109, obtain the weather jig environment of all types of dates corresponding to all bridge environment weather.

[0022] Step S2, based on the instrument parameters of the laser scanner, construct a digital twin model corresponding to the laser scanner, and record it as a scanning model; place the scanning model in each weather jig environment, and use the parameter calibration method to obtain the standard parameter group of the laser scanner corresponding to each weather jig environment; Step S2 comprises: step S201, the parameters that need to be adjusted when the laser scanner is started are recorded as scanning influence parameters SY1 to scanning influence parameters SY t ; for any weather jig environment: place the scanning model in the weather jig environment, and use the parameter calibration method to obtain the standard parameter group of the laser scanner corresponding to the weather jig environment; Step S202: Obtain the standard parameter set corresponding to each weather frame environment for the laser scanner.

[0023] Please see Figure 2 As shown, the parameter calibration method includes: Step V1, starting the scanning model to perform laser emission and scanning motion on the tire model in the weather tire environment, and recording the three-dimensional model generated from the point cloud data obtained after the scanning as the laser scanning model; the similarity between the laser scanning model and the tire model is recorded as the scanning similarity. Step V2: Based on the adjustable range of all scanning influence parameters, adjust the values ​​of all scanning influence parameters and restart the k-times scanning model for laser emission and scanning motion. The resulting laser scanning models are denoted as model DX1 to model DX1 respectively. k Wherein, for any two candidate models DX among all candidate models, k1 and the DX model to be selected k2 The candidate model DX is obtained. k1 Among all the scanning influence parameters corresponding to the time-scanning model, at least one scanning influence parameter δ has a value that is consistent with the value of the model to be selected, DX. k2 The values ​​of the scanning influence parameter δ corresponding to the time-scanning model are different; k1 and k2 are both positive integers less than or equal to k and greater than or equal to 1; In the specific implementation process, the value of k can be set according to the actual range of parameter adjustment. The larger the range of parameter adjustment, the higher the value of k can be, so as to ensure that the selected model can cover all types of models obtained by the laser scanner after scanning the tire frame; in this embodiment, the value of k is 10. The scanning parameters may include laser power, scanning frequency, scanning speed, laser wavelength, scanning mirror rotation speed, laser beam divergence angle, and scanning mode. For example, in the analysis of this embodiment, it is found that under the bright light environment of a sunny day, the laser reflection signal is too strong and easily saturates, resulting in false points in the point cloud, and the reflective surface is prone to signal misjudgment. Therefore, by adjusting the laser power and scanning frequency, after 10 laser emission and scanning movements, among the 10 candidate models obtained, the candidate model with a laser power of 15% and a scanning frequency increased by 25% has the highest scanning similarity. Therefore, in actual accuracy detection, if the weather environment of the bridge frame is the weather frame environment corresponding to the bright light environment of a sunny day, the laser power should be adjusted to 15% and the scanning frequency should be increased by 25% in the default state before the laser scanner performs laser emission and scanning operation to ensure that the obtained point cloud data can generate a more accurate model. Step V3: Transfer the model to be selected (DX1) to the model to be selected (DX). k The scanning influence parameter SY1 to the scanning influence parameter SY of the candidate model with the highest scanning similarity.t , the standard parameter group corresponding to the laser scanner is recorded as a standard parameter group of the laser scanner; Based on the analysis of the embodiment, the data in the standard parameter group of the laser scanner in the weather test bed environment corresponding to the high light environment date on a sunny day is that the laser power is 15%, the scanning frequency is increased by 25%, and other parameters remain unchanged.

[0024] Step S3, the weather influence data when the bridge test bed precision test is performed is recorded as real-time weather data; based on the weather test bed environment and the real-time weather data, the real-time test bed environment is obtained, and the laser scanner is controlled based on the standard parameter group corresponding to the real-time test bed environment to perform laser scanning and precision test comparison on the bridge test bed; Step S3 includes: step S301, the weather and weather influence data when the bridge test bed precision test is performed are recorded as test weather and real-time weather data respectively; Step S302, based on the value of each weather influence parameter corresponding to the real-time weather data, an environment composed of the weather influence parameters of the real-time weather data is added in the test bed environment, and the test bed environment at this time is recorded as a real-time test bed environment.

[0025] Step S3 further includes: step S303, the same bridge environment weather as the test weather is recorded as an available environment weather; the light reflectivity, temperature and dust concentration of the bridge test bed surface in the weather test bed environment of all types of dates of the available environment weather are compared with the light reflectivity, temperature and dust concentration of the bridge test bed surface in the real-time test bed environment respectively, and the maximum value of the sum of the similarities of the light reflectivity, temperature and dust concentration in the comparison results corresponds to the allowable test bed environment; In the specific implementation process, by comparing the light reflectivity, temperature and dust concentration of the bridge test bed surface, the allowable test bed environment most similar to the real-time test bed environment can be obtained, so as to ensure the accuracy of the laser scanner adjustment, and further improve the similarity of the model generated from the point cloud data and the actual bridge and the accuracy of the high-precision detection comparison based on the point cloud data; Step S304, the laser scanner is used to perform laser emission and scanning operation on the bridge test bed, and before starting the laser scanner, all scanning influence parameters in the laser scanner are adjusted based on the standard parameter group corresponding to the laser scanner in the allowable test bed environment; Step S305, the three-dimensional model generated from the point cloud data obtained after the bridge test bed is scanned by the laser scanner is recorded as a real-time scanning model, and the comparison result between the real-time scanning model and the standard model constructed based on the design reference is recorded as the precision test comparison result of the bridge test bed.

[0026] Embodiment 2, please refer to Figure 3 as shown,Figure 3 An example is provided for a structural diagram of an electronic device, which can include a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus. The memory stores computer readable instructions, and the processor can call the instructions in the memory. When the computer readable instructions are executed by the processor, the steps in the method for intelligent detection and comparison of bridge bed-jig precision based on design datum are run to realize the following functions: first, based on the design datum of the bridge bed-jig, a jig model is constructed, and the virtual environment in which the jig model is located is recorded as a jig environment; weather influence data is obtained based on the geographical location where the bridge bed-jig is located, and a plurality of weather influence parameters are obtained based on the weather influence data; the weather influence parameters are added to the jig environment, and a plurality of weather jig environments are obtained; then, based on the instrument parameters of the laser scanner, a scanning model is constructed; the scanning model is placed in each weather jig environment, and a parameter calibration method is used to obtain a standard parameter group corresponding to the laser scanner in each weather jig environment; finally, the weather influence data when the bridge bed-jig precision detection is performed is recorded as real-time weather data; based on the weather jig environment and the real-time weather data, a real-time jig environment is obtained, and the laser scanner is controlled based on the standard parameter group corresponding to the real-time jig environment to perform laser scanning and precision detection and comparison on the bridge bed-jig.

[0027] In addition, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0028] In embodiment 3, the application further provides a computer program product, which comprises a computer program stored on a computer readable storage medium, and the computer program comprises program instructions, when the program instructions are executed by a computer, the computer can execute the design reference based bridge bed precision intelligent detection and comparison method provided by each method, and the method comprises the following steps: firstly, based on the design reference of the bridge bed, a bed model is constructed, and a virtual environment in which the bed model is located is recorded as a bed environment; weather influence data is obtained based on the geographical position of the bridge bed, and a plurality of weather influence parameters are obtained based on the weather influence data; the weather influence parameters are added to the bed environment, and a plurality of weather bed environments are obtained; then, based on the instrument parameters of the laser scanner, a scanning model is constructed; the scanning model is placed in each weather bed environment, and the standard parameter group of the laser scanner in each weather bed environment is obtained by using the parameter calibration method; finally, the weather influence data when the bridge bed precision detection is performed is recorded as real-time weather data; based on the weather bed environment and the real-time weather data, a real-time bed environment is obtained, and the laser scanner is controlled based on the standard parameter group corresponding to the real-time bed environment to perform laser scanning and precision detection and comparison on the bridge bed.

[0029] In embodiment 4, the application further provides a computer readable storage medium, and the application provides a storage medium, which stores a computer program, when the computer program is executed by a processor, the steps in the design reference based bridge bed precision intelligent detection and comparison method are executed, so as to realize the following functions: firstly, based on the design reference of the bridge bed, a bed model is constructed, and a virtual environment in which the bed model is located is recorded as a bed environment; weather influence data is obtained based on the geographical position of the bridge bed, and a plurality of weather influence parameters are obtained based on the weather influence data; the weather influence parameters are added to the bed environment, and a plurality of weather bed environments are obtained; then, based on the instrument parameters of the laser scanner, a scanning model is constructed; the scanning model is placed in each weather bed environment, and the standard parameter group of the laser scanner in each weather bed environment is obtained by using the parameter calibration method; finally, the weather influence data when the bridge bed precision detection is performed is recorded as real-time weather data; based on the weather bed environment and the real-time weather data, a real-time bed environment is obtained, and the laser scanner is controlled based on the standard parameter group corresponding to the real-time bed environment to perform laser scanning and precision detection and comparison on the bridge bed.

[0030] Through the description of the above embodiments, the embodiments of the present application can be provided as a method, a system or a computer program product. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

[0031] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other manners. The embodiments described above are merely schematic, and should not be construed as limiting. For example, the division of the modules or the units is merely logical function division, and there can be other division manners in actual implementation. For example, a plurality of modules or units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different modules can be indirect couplings or communication connections through some interfaces, and there can be electric, mechanical or other forms.

[0032] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; even if the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A bridge bed-jig precision intelligent detection comparison method based on design benchmarks, characterized in that, The method comprises the following steps: Based on the design basis of the bridge bed frame, a digital twin model corresponding to the bridge bed frame is constructed, denoted as a bed frame model, and a virtual environment in which the bed frame model is located is denoted as a bed frame environment; weather influence data is obtained based on the geographical location where the bridge bed frame is located, and a plurality of weather influence parameters are obtained based on the weather influence data; the weather influence parameters are added to the bed frame environment, and a plurality of weather bed frame environments are obtained; Based on the instrument parameters of the laser scanner, a digital twin model corresponding to the laser scanner is constructed, denoted as a scanning model; the scanning model is placed in each weather bed frame environment, and the standard parameter set of the laser scanner in each weather bed frame environment is obtained using a parameter calibration method; The weather influence data when the bridge bed frame precision detection is performed is denoted as real-time weather data; based on the weather bed frame environment and the real-time weather data, a real-time bed frame environment is obtained, and the laser scanner is controlled based on the standard parameter set corresponding to the real-time bed frame environment to perform laser scanning and precision detection comparison on the bridge bed frame.

2. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 1, characterized in that, The weather influence data is obtained based on the geographical location where the bridge bed frame is located, which comprises: The geographical location where the bridge bed frame is located is denoted as a bridge location, weather data within one year at the bridge location is obtained based on the weather data, and all weathers existing in the weather data are denoted as bridge environment weathers; for any one bridge environment weather, the date corresponding to the weather in the weather data is obtained, denoted as a bridge environment date; the temperature, light intensity and dust concentration recorded by the meteorological data and environmental monitoring data at the bridge location within the bridge environment date are denoted as the weather influence data of the bridge environment date; The weather influence data of all bridge environment dates corresponding to each bridge environment weather is obtained.

3. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 2, characterized in that, The plurality of weather influence parameters are obtained based on the weather influence data, which comprises: For any one bridge environment date γ corresponding to any one bridge environment weather α: the maximum and minimum values of the temperature, light intensity and dust concentration in the weather influence data of the bridge environment date γ are denoted as the daily maximum temperature and daily minimum temperature, daily maximum light intensity and daily minimum light intensity, and daily maximum dust concentration and daily minimum dust concentration, respectively, and the temperature, light intensity and dust concentration are all denoted as weather influence parameters; The average values of the daily maximum temperature and daily minimum temperature, the average values of the daily maximum light intensity and daily minimum light intensity, and the average values of the daily maximum dust concentration and daily minimum dust concentration are denoted as the daily average temperature, daily average light intensity and daily average dust concentration, respectively.

4. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 3, characterized in that, The weather influence parameters are added to the bed frame environment, and a plurality of weather bed frame environments are obtained, which comprises: The daily average temperature, daily average light intensity and daily average dust concentration of all bridge environment dates corresponding to the bridge environment weather α are obtained, and the average value of all daily average temperatures is denoted as the environmental average temperature, the average value of all daily average light intensities is denoted as the environmental average light intensity, and the average value of all daily average dust concentrations is denoted as the environmental average dust concentration; the environmental average temperature, environmental average light intensity and environmental average dust concentration are all denoted as environmental average parameters; The bridge environment date when the daily average temperature is greater than or equal to the average ambient temperature and the daily average temperature is less than the average ambient temperature is recorded as a high-temperature environment date and a low-temperature environment date respectively; the bridge environment date when the daily average light intensity is greater than or equal to the average ambient light intensity and the daily average light intensity is less than the average ambient light intensity is recorded as a high-light environment date and a low-light environment date respectively; the bridge environment date when the daily average powder concentration is greater than or equal to the average ambient powder concentration and the daily average powder concentration is less than the average ambient powder concentration is recorded as a high-powder environment date and a low-powder environment date respectively.

5. The design reference based bridge bed-jack precision intelligent detection comparison method according to claim 4, characterized in that, The key factor of the high-temperature environment date and the low-temperature environment date is recorded as temperature, the key factor of the high-light environment date and the low-light environment date is recorded as light intensity, the key factor of the high-powder environment date and the low-powder environment date is recorded as powder concentration, the weather influence parameter is added to the bed environment, and the plurality of weather bed environments are obtained, and the method further comprises: For any one type of date β in the high-temperature environment date, the low-temperature environment date, the high-light environment date, the low-light environment date, the high-powder environment date and the low-powder environment date: the interval formed by the maximum value and the minimum value of the key factor in all dates corresponding to the date β is recorded as a key influence interval; In the bed environment, the key factor fluctuates within the key influence interval, and the weather influence parameters other than the key factor are all average ambient parameters, and the bed environment at this time is recorded as the weather bed environment of the date β; Obtain the weather bed environment corresponding to all types of dates of all bridge environment weather.

6. The design reference based bridge bed-jack precision intelligent detection comparison method according to claim 5, characterized in that, A scanning model is placed in each weather bed environment, and a standard parameter set corresponding to the laser scanner in each weather bed environment is obtained using a parameter calibration method, comprising: The parameters to be adjusted when starting the laser scanner are respectively denoted as scanning influence parameters SY1 to scanning influence parameters SY t ; for any weather rack environment: placing the scanning model in the weather rack environment, and using the parameter calibration method to obtain the corresponding standard parameter group of the laser scanner; A standard parameter set corresponding to the laser scanner in each weather bed environment is obtained.

7. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 6, characterized in that, The parameter calibration method comprises: Start the scanning model to perform laser emission and scanning motion on the bed model in the weather bed environment, and generate a three-dimensional model from the point cloud data obtained after scanning, which is recorded as a laser scanning model; the similarity between the laser scanning model and the bed model is recorded as a scanning similarity; Based on the range in which all the scanning influence parameters can be adjusted, the values of all the scanning influence parameters are adjusted and the k-time scanning model is restarted for laser emission and scanning movement, and the obtained laser scanning model is respectively denoted as a to-be-selected model DX1 to a to-be-selected model DX k , wherein, for any two to-be-selected models DX k1 and DX k2 , there is at least one scanning influence parameter δ in all the scanning influence parameters corresponding to the to-be-selected models DX k1 and DX k2 , and the numerical value of the scanning influence parameter δ corresponding to the to-be-selected model DX k1 is different from the numerical value of the scanning influence parameter δ corresponding to the to-be-selected model DX k2 . k1 and k2 are positive integers less than or equal to k and greater than or equal to 1; DX1 to DXn are selected as the target model k SY1 to SYn are selected as the scanning influence parameters of the target model t , which are referred to as the standard parameter set of the laser scanner.

8. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 7, characterized in that, The weather influence data when the bridge bed precision detection is performed is recorded as real-time weather data; Based on the weather bed environment and the real-time weather data, the real-time bed environment is obtained, comprising: The weather and weather influence data when the bridge bed precision test is performed are recorded as test weather and real-time weather data respectively; Based on the value of each weather influence parameter corresponding to the real-time weather data, an environment composed of the weather influence parameters of the real-time weather data is added in the bed environment, and the bed environment at this time is recorded as the real-time bed environment.

9. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 8, characterized in that, Based on the standard parameter set corresponding to the real-time bed environment, the laser scanner is controlled to perform laser scanning and precision detection comparison on the bridge bed, comprising: The bridge environment weather same as the test weather is recorded as the available environment weather; the light reflectivity, temperature and dust concentration of the bridge mould surface in the available environment weather of all types of weather are compared with the light reflectivity, temperature and dust concentration of the bridge mould surface in the real-time mould environment respectively, and the weather mould environment corresponding to the maximum value of the sum of the similarity of the light reflectivity, temperature and dust concentration in the comparison result is recorded as the allowable mould environment.

10. The bridge design datum-based bridge bed-jack precision intelligent detection comparison method according to claim 9, characterized in that, The laser scanner is controlled to scan the bridge mould and perform precision detection comparison based on the standard parameter group corresponding to the real-time mould environment, and the method further comprises the steps of: The laser scanner is controlled to scan the bridge mould and perform precision detection comparison based on the standard parameter group corresponding to the real-time mould environment, and the method further comprises the steps of: The three-dimensional model generated by the point cloud data obtained after the bridge mould is scanned by the laser scanner is recorded as the real-time scanning model, and the comparison result of the real-time scanning model and the standard model constructed based on the design datum is recorded as the precision detection comparison result of the bridge mould.

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