CN118913189A - Rainshed grid-shaped steel structure radian error judging method - Google Patents

Abstract

The present invention discloses a method for determining the curvature error of a canopy grid-shaped steel structure, and relates to the technical field of building structure measurement. In order to solve the problem that a large amount of scanned data needs to be processed and analyzed in a complex manner, the time and complexity of data processing are increased, and further on-site measurement and adjustment are still required, which affects the construction efficiency and final quality; by deploying ranging sensors at different monitoring nodes and performing repeated measurements for many times, the accuracy and reliability of data acquisition are significantly improved, and the average random error is measured for many times, so that the measurement result is closer to the true value, ensuring that the ranging sensor covers the entire canopy grid-shaped steel structure area of the canopy, and the structural information of the canopy can be fully obtained. At the same time, the curvature parameters of each monitoring node are analyzed, so that the error determination is more accurate, and the errors or defects in the canopy grid-shaped steel structure can be discovered in time, and targeted improvement measures are formulated based on the analysis and evaluation of the error determination results.

Description

A method for determining the curvature error of a canopy grid steel structure

Technical Field

The invention relates to the technical field of building structure measurement, and in particular to a method for determining the curvature error of a grid-shaped steel structure of a canopy.

Background Art

As a common sunshade and rainproof facility, the structural stability and curvature accuracy of the canopy are crucial to its use effect and safety. Traditional methods mostly rely on manual measurement, which has problems such as low measurement accuracy and long time consumption, and it is difficult to meet the accuracy requirements of modern buildings. Regarding the error judgment method of steel structure, the Chinese patent with publication number CN108009327B discloses a virtual pre-assembly error judgment method based on steel component deformation analysis. First, in the structural design stage, the structural unit is subjected to structural deformation analysis according to the construction plan and working conditions, and the structural pre-arch value is determined to complete the structural design optimization; the factory processes the components according to the optimized design plan; after the processing is completed, the components are scanned, inspected and virtually pre-assembled, and the accuracy of the virtual pre-assembly detection of steel components is improved through structural deformation analysis, providing reliable data guarantee for the virtual pre-assembly of steel structures.

Although the above patent improves the accuracy of detection through scanning detection and virtual pre-assembly, a large amount of scanning data needs to be complexly processed and analyzed to generate an accurate virtual pre-assembly model, which increases the time and complexity of data processing. The components may also produce new deformations or errors during transportation and installation. In the actual construction process, further on-site measurements and adjustments may still be required, affecting construction efficiency and final quality.

Summary of the invention

The purpose of the present invention is to provide a method for determining the curvature error of a canopy grid steel structure. Through high-precision ranging sensors and data analysis, the curvature parameters of the canopy surface can be accurately acquired and the error determination can be achieved, thereby improving the measurement accuracy and efficiency to solve the problems raised in the above-mentioned background technology.

To achieve the above object, the present invention provides the following technical solutions:

A method for determining the curvature error of a canopy grid steel structure comprises the following steps:

Step 1: Data collection: Deploy distance measuring sensors at each monitoring node of the canopy grid steel structure, measure the distance between each monitoring node of the canopy at least once, including the distance data in the horizontal and vertical directions, and record the length and arc value of each measurement;

Among them, the various monitoring nodes include: key connection nodes, such as node plates, welding points, etc.; steel column tilt monitoring nodes, which select specific axis steel columns for monitoring, and set a column tilt measuring point on both sides of each axis to monitor the tilt of the steel column; truss beam steel structure stress and tilt monitoring nodes, including stress monitoring points and tilt monitoring points, which are used to monitor the stress state and tilt of the truss beam in real time; environmental monitoring nodes, which are used to monitor environmental parameters such as wind speed, wind direction, and temperature; specific nodes, which monitor stress concentration points and vibration sensitive points at specific locations;

Step 2: Data calculation: Calculate the radius of the canopy at each monitoring node based on the length and curvature values measured each time, determine the actual curvature of the canopy at each monitoring node, and calculate the overall actual curvature of the canopy based on the actual curvature of each monitoring node;

Step 3: Error determination: A three-dimensional geometric model of the canopy surface is constructed by calculating the actual curvature of the canopy, and the radian parameter information of each monitoring node is obtained based on the three-dimensional geometric model. The relative error between the radian parameter information and the standard value is calculated, and the relative error is compared with the preset error threshold, and the radian parameter exceeding the threshold is marked;

Step 4: Result analysis: Analyze and evaluate the marked error judgment results, formulate corresponding improvement measures based on the analysis and evaluation results, and output the measurement results, error judgment results and improvement measures in the form of charts and reports.

Furthermore, the data collection in step 1 specifically includes:

Obtain basic structural data of the grid-shaped steel structure of the canopy, and determine each monitoring node of the grid-shaped steel structure characteristics of the canopy based on the basic structural data;

Deploy the ranging sensor on each monitoring node, and number each monitoring node based on the extraction order, with the number corresponding to each monitoring node one by one;

Measure the distance between each monitoring node for the first time and record the measurement data of the first measurement;

Repeated measurements are performed on the same group of monitoring nodes under different time periods and environmental conditions, wherein each repeated measurement is performed according to the same measurement process.

Furthermore, after the ranging sensor is deployed, the ranging sensor is automatically calibrated, including:

After the ranging sensor is deployed, the input value of the ranging sensor is adjusted to a first range value, the output value of the ranging sensor corresponding to the input value of the ranging sensor being the first range value is obtained, and the output value of the ranging sensor corresponding to the first range value is used as the first sensor value; wherein the first range value is zero;

The second range value is set according to the output value of the ranging sensor corresponding to the first range value when the input value of the ranging sensor is the first range value, wherein the second range value is obtained by the following formula:

;

Wherein, W represents the second range value; _Wm represents the value corresponding to the full range; _X01 represents the output value of the distance measuring sensor corresponding to the first range value, that is, the first sensor value; _Xm represents the theoretical sensor output value corresponding to the full range; e represents the error range span value corresponding to the theoretical output value of the distance measuring sensor;

Adjusting the input value of the ranging sensor to a second range value, obtaining an output value of the ranging sensor corresponding to when the input value of the ranging sensor is the second range value, and using the output value of the ranging sensor corresponding to when the second range value is used as a second sensor value;

Obtaining an adjustment compensation amount of a distance measuring sensor using the first sensor value and the second sensor value;

Combined with the adjustment compensation amount, each measurement value of the distance measuring sensor is automatically calibrated in real time.

Further, obtaining the adjustment compensation amount of the ranging sensor by using the first sensor value and the second sensor value includes:

Extracting the input value of the distance measuring sensor and adjusting it to a theoretical output value corresponding to the first range value as the first theoretical output value;

Extracting the input value of the distance measuring sensor and adjusting it to a theoretical output value corresponding to the second range value as the second theoretical output value;

Obtaining a first difference value using the first sensor value and the first theoretical output value;

Obtain a second difference value using the second sensor value and the second theoretical output value;

The adjustment compensation amount is obtained by using the first difference and the second difference, wherein the adjustment compensation amount is obtained by the following formula:

;

Among them, ΔX represents the adjustment compensation amount; _X01 represents the output value of the distance measuring sensor corresponding to the first range value, that is, the first sensor value; _X02 represents the output value of the distance measuring sensor corresponding to the second range value, that is, the second sensor value; _Xe01 represents the first theoretical output value; _Xe02 represents the second theoretical output value.

Furthermore, each measurement value of the distance measuring sensor is automatically calibrated in real time in combination with the adjustment compensation amount, including:

Each time the distance measuring sensor performs a distance measuring operation, the temperature and humidity of the environment in which the distance measuring sensor performs historical distance detection operation are retrieved as first reference data;

Each time the distance measuring sensor performs a distance measuring operation, the light intensity of the environment in which the distance measuring sensor performs historical distance detection is retrieved as second reference data;

The adjustment coefficient is obtained by using the first reference data and the second reference data; wherein the adjustment coefficient is obtained by the following formula:

;

Wherein, x represents the adjustment coefficient; n represents the number of historical distance detections of the ranging sensor; _Xsi represents the actual value corresponding to the ranging of the i-th ranging sensor; _eup and _edown represent the upper and lower limits of the error range corresponding to the ranging of the ranging sensor; _Te , _Qe and _Be represent the maximum temperature, humidity and light intensity that meet the normal operation requirements of the ranging sensor respectively; _Qsi represents the actual environmental humidity corresponding to the ranging of the i-th ranging sensor; _Bsi represents the actual environmental light intensity corresponding to the ranging of the i-th ranging sensor; _Tsi represents the actual environmental temperature corresponding to the ranging of the i-th ranging sensor;

The adjustment coefficient is combined with the adjustment compensation amount to obtain the output value of the automatically calibrated distance measuring sensor; wherein the output value of the automatically calibrated distance measuring sensor is obtained by the following formula:

;

Wherein, _Xt represents the output value of the distance measuring sensor after automatic calibration; _Xc represents the output value of the distance measuring sensor before automatic calibration; x represents the adjustment coefficient; ΔX represents the adjustment compensation amount.

Furthermore, when repeatedly measuring the same group of monitoring nodes, it also includes: recording the target data of each measurement, including the monitoring node number, measurement date and time, environmental conditions and measurement data, and performing data verification after each measurement.

Furthermore, in step 2, determining the actual curvature of the canopy at each monitoring node specifically includes:

Based on the length value and arc value obtained in each measurement, the arc segments of each monitoring node of the canopy grid steel structure are constructed, wherein the arc segments correspond to the measurement data one by one;

Fitting arc segment nodes on each arc segment based on the measurement data, extracting the best fitting arc according to the arc segment nodes, and calculating the radius value of the best fitting arc;

The corresponding center angle is calculated based on the arc segment radius value and the measured length value of each monitoring node, the center angle is converted from degrees to radians, and the actual radian value on each monitoring node is determined.

Furthermore, the calculation of the overall actual curvature of the canopy in step 2 specifically includes:

Integrate the actual radian values: Integrate the actual radian values of each monitoring node, verify the actual radian values, and eliminate abnormal values in the actual radian values;

Determine the local curvature: determine the curvature calculation model based on the local structural characteristics of the canopy grid steel structure, and calculate the local curvature of each monitoring node according to the measurement data of the adjacent monitoring nodes of each monitoring node;

Wherein, the curvature calculation model includes:

Gaussian curvature is used to describe the degree of curvature at each point on the surface. It is obtained by calculating the product of the two principal curvatures at a point on the surface and is applicable to the area of double curvature surfaces.

Mean curvature is used to describe the average curvature of the normal direction at a point on the surface. It is based on the arithmetic mean of the principal curvatures and is applicable to planes and approximate plane areas.

Finite element analysis, used to simulate and calculate the global curvature of surfaces through discretization and numerical approximation, applicable to complex nodes and connected areas;

Calculate the overall curvature: determine the curvature on the non-monitoring nodes based on the interpolation method, and obtain the overall actual curvature of the canopy based on the weighted average according to the calculation results of the local curvature and the curvature of the non-monitoring nodes obtained by interpolation.

Furthermore, the error determination in step 3 specifically includes:

Generate a three-dimensional geometric model of the canopy based on the calculated overall actual curvature data, and identify each monitoring node in the three-dimensional geometric model;

Extract the actual arc value and the corresponding arc length and radius of each monitoring node, and integrate them to generate the arc parameter information of each monitoring node;

Obtain the standard value of the radian parameter of each monitoring node, perform difference calculation between the radian parameter information of each monitoring node and the standard value, and determine the relative error between the radian parameter information and the standard value;

The relative error of each monitoring node is compared with the preset error threshold, the arc parameters whose relative error exceeds the preset threshold are marked, and the detailed information corresponding to the monitoring node is obtained.

Furthermore, the result analysis in step 4 specifically includes:

The relative errors between the measured arc parameters of all monitoring nodes and the standard values are summarized to generate a table, the errors are classified according to the set error thresholds, and the corresponding positions are marked in the table;

Determine whether there is a systematic error based on the trend of the error distribution, analyze the possible causes for each excessive error, and identify whether there is a structural defect;

According to the error analysis results, the design of the grid steel structure of the canopy is optimized, the problems in the installation process are extracted, and specific process improvement measures are proposed;

Present the measurement results, error determination results and improvement measures in charts and graphs, and write a detailed measurement report.

Compared with the prior art, the present invention has the following beneficial effects:

By deploying ranging sensors at different monitoring nodes and performing repeated measurements, the accuracy and reliability of data collection can be significantly improved. The average random error of multiple measurements makes the measurement result closer to the true value, ensuring that the ranging sensor covers the entire grid steel structure area of the canopy, and can fully obtain the structural information of the canopy. At the same time, through the calculated actual curvature and the constructed three-dimensional geometric model, the curvature parameters of each monitoring node are analyzed, so that the error judgment is more accurate, and the errors or defects in the grid steel structure of the canopy can be discovered in time. Based on the analysis and evaluation of the error judgment results, targeted improvement measures are formulated to optimize the design and manufacturing process of the canopy, which helps to improve the overall quality and performance of the canopy while reducing manufacturing and maintenance costs. Relevant personnel can understand the performance of the grid steel structure of the canopy more accurately, so as to make scientific and reasonable decisions and improve the overall benefits and safety of the project.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for determining the curvature error of a grid-like steel structure of a canopy according to the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

In order to solve the problem that a large amount of scanned data needs to be processed and analyzed in a complex manner to generate an accurate virtual pre-assembly model, the time and complexity of data processing are increased. The components may also produce new deformations or errors during transportation and installation. In the actual construction process, further on-site measurements and adjustments may still be required, which will affect the construction efficiency and final quality. Please refer to FIG1. This embodiment provides the following technical solutions:

A method for determining the curvature error of a canopy grid steel structure comprises the following steps:

Step 1: Data collection: Deploy distance measuring sensors at each monitoring node of the canopy grid steel structure to ensure that the entire canopy grid steel structure area can be covered. Start the distance measuring sensors through the control system to measure the distance between each monitoring node of the canopy at least once, including the distance data in the horizontal and vertical directions, and record the length and arc value of each measurement;

Step 2: Data calculation: Calculate the radius of the canopy at each monitoring node based on the length and curvature values measured each time, determine the actual curvature of the canopy at each monitoring node, and calculate the overall actual curvature of the canopy based on the actual curvature of each monitoring node;

Step 3: Error determination: A three-dimensional geometric model of the canopy surface is constructed by calculating the actual curvature of the canopy. Based on the three-dimensional geometric model, the arc parameter information of each monitoring node is obtained, including arc length, arc, radius, etc. The relative error between the arc parameter information and the standard value is calculated, and the relative error is compared with the preset error threshold. The arc parameters that exceed the threshold are marked, including:

Generate a three-dimensional geometric model of the canopy based on the calculated overall actual curvature data, and identify each monitoring node in the three-dimensional geometric model;

Extract the actual arc value and the corresponding arc length and radius of each monitoring node, and integrate them to generate the arc parameter information of each monitoring node;

Obtain the standard value of the radian parameter of each monitoring node, perform difference calculation between the radian parameter information of each monitoring node and the standard value, and determine the relative error between the radian parameter information and the standard value;

Compare the relative error of each monitoring node with the preset error threshold, mark the arc parameters whose relative error exceeds the preset threshold, and obtain the detailed information corresponding to the monitoring node, including location, measured value, standard value, relative error, etc.;

In this embodiment, by generating a three-dimensional geometric model of the canopy, the design team, construction personnel and quality inspection personnel can intuitively understand the structure and shape of the canopy, thereby improving the understanding of the design and the accuracy of the construction. Each monitoring node is marked in the three-dimensional model, so that in the subsequent analysis and evaluation, important structural points can be quickly located, which is convenient for accurate measurement and comparison. By calculating the difference between the actual curvature parameter and the standard value, the manufacturing and installation errors of the canopy can be quantitatively evaluated, providing an objective basis for quality control, timely discovering the monitoring nodes that exceed the standard, and providing direction for subsequent rectification and optimization.

Step 4: Result analysis: Analyze and evaluate the marked error determination results to determine whether there are errors or defects in the grid-shaped steel structure of the canopy. Based on the analysis and evaluation results, formulate corresponding improvement measures to optimize the design and production process of the canopy, and output the measurement results, error determination results and improvement measures in the form of charts and reports for reference by design, construction and maintenance personnel, including:

The relative errors between the measured arc parameters and the standard values of all monitoring nodes are summarized to generate a table, including key information such as location number, measured value, standard value, relative error, etc. The errors are classified according to the set error threshold into different categories such as acceptable range, slight excess, and serious excess, and marked at the corresponding positions in the table;

Determine whether there is a systematic error based on the trend of the error distribution. For example, if the errors at all or most locations are biased in the same direction, analyze the possible causes for each location of the error that exceeds the standard, such as differences in material properties, insufficient machining accuracy, installation errors, environmental factors, etc., and identify whether there are structural defects, such as poor welding, material cracks, corrosion, etc.

According to the error analysis results, the design of the canopy grid steel structure is optimized, such as adjusting the design parameters to reduce the dependence on processing accuracy, or adding redundant design to improve the fault tolerance of the structure, extracting installation process problems, and proposing specific process improvement measures, such as improving the accuracy of processing equipment, improving welding technology, and strengthening quality control and inspection.

Display the measurement results, error determination results and improvement measures in the form of charts, such as error distribution charts, trend charts, improvement measures comparison charts, etc., so as to intuitively understand the problem and improvement effect, and write a detailed measurement report, including the measurement purpose, method, process, results, analysis, improvement measures, etc.

In this embodiment, by conducting a detailed analysis and classification of the relative errors between the measured curvature parameters of all monitoring nodes and the standard values, those excessive errors that may affect the safety of the structure can be discovered and corrected in a timely manner, and structural failure or damage caused by error accumulation can be prevented, thereby improving the safety of the overall structure. The measurement results, error judgment results and improvement measures are displayed in the form of charts, which can intuitively show the project team and relevant stakeholders where the problems and improvement effects are, ensure that the report content is comprehensive, accurate and convincing, supervise the implementation process, ensure that various measures are effectively implemented, and re-measure and evaluate after the implementation of the improvement measures to verify the effectiveness of the improvement measures.

In this embodiment, by deploying ranging sensors at different monitoring nodes and performing repeated measurements, the accuracy and reliability of data acquisition are significantly improved, and the average random error is measured multiple times, so that the measurement result is closer to the true value, ensuring that the ranging sensor covers the entire grid steel structure area of the awning, and can fully obtain the structural information of the awning. At the same time, through the calculated actual curvature and the constructed three-dimensional geometric model, the curvature parameters of each monitoring node are analyzed, so that the error judgment is more accurate, and the errors or defects in the grid steel structure of the awning can be discovered in time. Based on the analysis and evaluation of the error judgment results, targeted improvement measures are formulated to optimize the design and manufacturing process of the awning, which helps to improve the overall quality and performance of the awning, while reducing manufacturing and maintenance costs. Relevant personnel can more accurately understand the performance status of the grid steel structure of the awning, so as to make scientific and reasonable decisions and improve the overall efficiency and safety of the project.

Specifically, after the ranging sensor is deployed, the ranging sensor is automatically calibrated, including:

After the ranging sensor is deployed, the input value of the ranging sensor is adjusted to a first range value, the output value of the ranging sensor corresponding to the input value of the ranging sensor being the first range value is obtained, and the output value of the ranging sensor corresponding to the first range value is used as the first sensor value; wherein the first range value is zero;

The second range value is set according to the output value of the ranging sensor corresponding to the first range value when the input value of the ranging sensor is the first range value, wherein the second range value is obtained by the following formula:

;

Wherein, W represents the second range value; _Wm represents the value corresponding to the full range; _X01 represents the output value of the distance measuring sensor corresponding to the first range value, that is, the first sensor value; _Xm represents the theoretical sensor output value corresponding to the full range; e represents the error range span value corresponding to the theoretical output value of the distance measuring sensor;

Adjusting the input value of the ranging sensor to a second range value, obtaining an output value of the ranging sensor corresponding to when the input value of the ranging sensor is the second range value, and using the output value of the ranging sensor corresponding to when the second range value is used as a second sensor value;

Obtaining an adjustment compensation amount of a distance measuring sensor using the first sensor value and the second sensor value;

Combined with the adjustment compensation amount, each measurement value of the distance measuring sensor is automatically calibrated in real time.

The technical effect of the above technical solution is: through the automatic calibration process, the technical solution can ensure that the distance measuring sensor has higher accuracy during measurement. By using the first sensor value and the second sensor value to obtain the adjustment compensation amount, the initial error of the sensor can be corrected, making subsequent measurements more accurate.

This solution calculates the adjustment compensation by setting the first range value (zero position) and the second range value and obtaining the corresponding sensor output value. This compensation will be used to calibrate each measurement value in real time, effectively reducing the measurement error caused by the sensor itself or environmental factors. In actual use, the ranging sensor may be affected by environmental factors such as air pressure, light, and smoke. Through automatic calibration, the sensor can better adapt to these changes and maintain the stability and accuracy of the measurement.

The automatic calibration process reduces the need for human intervention, making the system more intelligent and automated, thereby improving the reliability of the entire measurement system. Accurate calibration can reduce sensor misoperation and overload, which may extend the service life of the sensor.

In summary, this technical solution significantly improves the measurement accuracy, environmental adaptability, system reliability and service life of the ranging sensor through the automatic calibration process, providing strong technical support for practical applications.

Specifically, obtaining the adjustment compensation amount of the distance measuring sensor by using the first sensor value and the second sensor value includes:

Extracting the input value of the distance measuring sensor and adjusting it to a theoretical output value corresponding to the first range value as the first theoretical output value;

Extracting the input value of the distance measuring sensor and adjusting it to a theoretical output value corresponding to the second range value as the second theoretical output value;

Obtaining a first difference value using the first sensor value and the first theoretical output value;

Obtain a second difference value using the second sensor value and the second theoretical output value;

The adjustment compensation amount is obtained by using the first difference and the second difference, wherein the adjustment compensation amount is obtained by the following formula:

;

Among them, ΔX represents the adjustment compensation amount; _X01 represents the output value of the distance measuring sensor corresponding to the first range value, that is, the first sensor value; _X02 represents the output value of the distance measuring sensor corresponding to the second range value, that is, the second sensor value; _Xe01 represents the first theoretical output value; _Xe02 represents the second theoretical output value.

The technical effect of the above technical solution is: by using the first sensor value and the second sensor value to obtain the adjustment compensation amount, the technical solution can significantly improve the measurement accuracy of the ranging sensor. The calculation of the adjustment compensation amount is based on the difference between the actual output value and the theoretical output value, which can effectively correct the deviation that may exist in the actual measurement of the sensor, thereby obtaining a more accurate measurement result.

Secondly, this solution helps reduce measurement errors. By comparing the first sensor value with the first theoretical output value, and the second sensor value with the second theoretical output value, the first difference and the second difference are calculated, and then the adjustment compensation amount is obtained. This process can identify and correct the error of the sensor under a specific range, making the measurement data more reliable.

In addition, this calibration method also enhances the adaptability of the ranging sensor to environmental changes. In actual applications, the sensor may be affected by various environmental factors, such as air pressure, light, and smoke. Through automatic calibration and adjustment compensation, the sensor can better cope with these changes and maintain the stability and accuracy of the measurement.

Finally, the technical solution also improves the reliability of the measurement system. By reducing human intervention and automating the calibration process, the system is more intelligent, which reduces the possibility of operating errors and improves the reliability and efficiency of the entire measurement system.

In summary, the technical solution of obtaining the adjustment compensation amount of the ranging sensor by using the first sensor value and the second sensor value has achieved remarkable technical effects in improving measurement accuracy, reducing errors, enhancing environmental adaptability and improving system reliability.

Specifically, automatically calibrating each measurement value of the distance measuring sensor in real time in combination with the adjustment compensation amount includes:

Each time the distance measuring sensor performs a distance measuring operation, the temperature and humidity of the environment in which the distance measuring sensor performs historical distance detection operation are retrieved as first reference data;

Each time the distance measuring sensor performs a distance measuring operation, the light intensity of the environment in which the distance measuring sensor performs historical distance detection is retrieved as second reference data;

The adjustment coefficient is obtained by using the first reference data and the second reference data; wherein the adjustment coefficient is obtained by the following formula:

;

Wherein, x represents the adjustment coefficient; n represents the number of historical distance detections of the ranging sensor; _Xsi represents the actual value corresponding to the ranging of the i-th ranging sensor; _eup and _edown represent the upper and lower limits of the error range corresponding to the ranging of the ranging sensor; _Te , _Qe and _Be represent the maximum temperature, humidity and light intensity that meet the normal operation requirements of the ranging sensor respectively; _Qsi represents the actual environmental humidity corresponding to the ranging of the i-th ranging sensor; _Bsi represents the actual environmental light intensity corresponding to the ranging of the i-th ranging sensor; _Tsi represents the actual environmental temperature corresponding to the ranging of the i-th ranging sensor;

The adjustment coefficient is combined with the adjustment compensation amount to obtain the output value of the automatically calibrated distance measuring sensor; wherein the output value of the automatically calibrated distance measuring sensor is obtained by the following formula:

;

Wherein, _Xt represents the output value of the distance measuring sensor after automatic calibration; _Xc represents the output value of the distance measuring sensor before automatic calibration; x represents the adjustment coefficient; ΔX represents the adjustment compensation amount.

The technical effect of the above technical solution is: the solution dynamically adjusts the calibration parameters by combining the temperature and humidity (first reference data) and light intensity (second reference data) of the historical operating environment of the ranging sensor in real time. This enables the ranging sensor to perform more accurate measurements under different environmental conditions, significantly enhancing the environmental adaptability of the sensor. By introducing the adjustment coefficient, the solution can more effectively control the error range of the ranging sensor. The calculation of the adjustment coefficient takes into account the historical ranging data, the upper and lower limits of the error range, and the environmental parameter requirements for the normal operation of the sensor. This helps to ensure that the measured value is within the predetermined error range, thereby improving the reliability and accuracy of the measurement.

During each ranging operation, the solution calibrates the sensor output value in real time based on the current adjustment coefficient and the pre-calculated adjustment compensation. This real-time calibration mechanism can respond quickly to environmental changes and ensure that the ranging sensor can provide optimized and accurate measurement data at all times. This technical solution reduces the need for human intervention and improves the intelligence level of the entire measurement system through automated data processing and calibration processes. This not only improves work efficiency, but also reduces measurement errors caused by human operating errors. By dynamically adjusting calibration parameters and calibrating measurement data in real time, the solution can reduce excessive wear or damage to the sensor caused by environmental factors, thereby potentially extending the service life of the sensor.

In summary, the above technical solution significantly improves the measurement accuracy, environmental adaptability, system intelligence level and service life of the ranging sensor through dynamic calibration, error range control, real-time calibration and output optimization, providing strong technical support for accurate ranging in various application scenarios.

In this embodiment, the data collection in step 1 specifically includes:

Obtain the basic structural data of the grid-like steel structure of the canopy, including structural features, size, shape and possible deformation areas, and determine the various monitoring nodes of the grid-like steel structure characteristics of the canopy based on the basic structural data, including the intersection of the grid, which is an important support point of the grid structure and an area where deformation is prone to occur; the boundary points of the grid are crucial to determining the overall shape and size of the canopy, and special structural points such as special nodes, reinforcement components or connection points should also be used as monitoring nodes;

Deploy the ranging sensor on each monitoring node, and number each monitoring node based on the extraction order, with the number corresponding to each monitoring node one by one;

Measure the distance between each monitoring node for the first time, and record the measurement data of the first measurement, including the distance values in the horizontal and vertical directions and the corresponding arc values;

Repeated measurements are performed on the same set of monitoring nodes in different time periods and environmental conditions, such as changes in temperature and humidity. Each repeated measurement is performed according to the same measurement process to ensure the consistency of the measurement data. The number of repeated measurements can be determined according to project requirements, measurement accuracy and error range to evaluate the stability and repeatability of the measurement results.

When repeated measurements are made on the same set of monitoring nodes, the following are also included:

Record the target data of each measurement, including the monitoring node number, measurement date and time, environmental conditions such as temperature, humidity, etc., and measurement data such as horizontal distance, vertical distance, arc value, and perform data verification after each measurement.

In this embodiment, the basic structural data of the grid steel structure of the canopy is obtained to accurately understand the overall condition of the structure, and repeated measurements are performed under different time periods and environmental conditions to evaluate the stability of the structure under different environmental factors. By comparing the measurement data at different time points, slight deformations or potential problems of the structure are discovered in time, and data verification is performed after each measurement. This helps to improve the accuracy and reliability of the data. By comparing and analyzing multiple measurement data, possible problems or potential improvement points in the structure can be identified, and errors or problems in the structure can be discovered and corrected in time, which helps to enhance the safety and durability of the structure and reduce potential risks and economic losses caused by structural failure or damage.

In this embodiment, the step 2 of determining the actual curvature of the canopy at each monitoring node specifically includes:

Based on the length value and arc value obtained in each measurement, the arc segments of each monitoring node of the canopy grid steel structure are constructed, wherein the arc segments correspond to the measurement data one by one;

Fitting arc segment nodes on each arc segment based on the measurement data, extracting the best fitting arc according to the arc segment nodes, and calculating the radius value of the best fitting arc;

Calculate the corresponding center angle based on the arc segment radius value and the measured length value of each monitoring node, convert the center angle from degrees to radians, and determine the actual radian value at each monitoring node;

In this embodiment, the calculation of the overall actual curvature of the canopy in step 2 specifically includes:

Integrate the actual radian values: Integrate the actual radian values of each monitoring node, verify the actual radian values, and eliminate abnormal values in the actual radian values;

Determine the local curvature: determine the curvature calculation model based on the local structural characteristics of the canopy grid steel structure, and calculate the local curvature of each monitoring node according to the measurement data of the adjacent monitoring nodes of each monitoring node, including:

Gaussian curvature is used to describe the degree of curvature at each point on the surface. It is obtained by calculating the product of the two principal curvatures at a point on the surface and is applicable to the area of double curvature surfaces.

Mean curvature is used to describe the average curvature of the normal direction at a point on the surface. It is based on the arithmetic mean of the principal curvatures and is applicable to planes and approximate plane areas.

Finite element analysis, used to simulate and calculate the global curvature of surfaces through discretization and numerical approximation, applicable to complex nodes and connected areas;

Calculate the overall curvature: determine the curvature at non-monitoring nodes based on the interpolation method, match it with the measured data at the monitoring nodes, and provide a smooth curvature change on the entire surface. According to the calculation results of the local curvature and the interpolated curvature of the non-monitoring nodes, the overall actual curvature of the canopy is obtained by weighted average.

In this embodiment, arc segments are constructed based on the measured data and the best fitting arc is extracted to accurately calculate the actual curvature value of each monitoring node, thereby reducing the impact of measurement errors or data anomalies on the results and improving the accuracy of curvature measurement. The curvature at non-monitoring nodes is determined by interpolation method and matched with the measured data at the monitoring nodes to provide a smooth curvature change on the entire surface. The characteristics of the local curvature and the overall structure are combined to make the evaluation of the overall curvature more comprehensive and accurate, which helps to take measures in advance for repair or reinforcement, thereby improving the safety and stability of the structure.

The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims (10)

1. The method for judging radian error of the grid-shaped steel structure of the awning is characterized by comprising the following steps of:

step one: and (3) data acquisition: deploying ranging sensors at each monitoring node of the awning grid-shaped steel structure, repeatedly measuring the distance between each monitoring node of the awning at least once, including distance data in the horizontal direction and the vertical direction, and recording the length value and the radian value measured each time;

Step two: and (3) data calculation: calculating the radian radius value of the awning at each monitoring node according to the length value and the radian value measured each time, determining the actual radian of the awning at each monitoring node, and calculating the integral actual curvature of the awning according to the actual radian of each monitoring node;

Step three: and (3) error judgment: constructing a three-dimensional geometric model of the curved surface of the awning by the calculated actual curvature of the awning, acquiring radian parameter information of each monitoring node based on the three-dimensional geometric model, calculating relative errors of the radian parameter information and a standard value, comparing the relative errors with a preset error threshold value, and marking the radian parameter exceeding the threshold value;

Step four: analysis of results: and analyzing and evaluating the marked error judgment result, formulating corresponding improvement measures based on the analysis and evaluation result, and outputting the measurement result, the error judgment result and the improvement measures in the form of charts and reports.

2. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 1, wherein the method comprises the following steps of: the data acquisition in the first step specifically comprises the following steps:

Basic structural data of the canopy grid-shaped steel structure is obtained, wherein the basic structural data comprise structural characteristics, sizes, shapes and connection modes of the canopy grid-shaped steel structure, and all monitoring nodes of the canopy grid-shaped steel structure characteristics are determined based on the basic structural data;

the ranging sensors are deployed on all the monitoring nodes, the monitoring nodes are numbered based on the extraction sequence, and the numbers are in one-to-one correspondence with the monitoring nodes;

measuring the distance between each monitoring node for the first time, and recording the measurement data of the first measurement;

and repeatedly measuring the same group of monitoring nodes under different time periods and environmental conditions, wherein each time of repeated measurement is performed according to the same measurement flow.

3. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 2, wherein the method comprises the following steps of: after the ranging sensor is deployed, automatically calibrating the ranging sensor, including:

After the distance measuring sensor is deployed, adjusting an input value of the distance measuring sensor to be a first range value, acquiring an output value of the distance measuring sensor corresponding to the first range value, and taking the output value of the distance measuring sensor corresponding to the first range value as the first sensor value; wherein the first span value is zero;

setting a second range value according to the output value of the corresponding range sensor when the input value of the range sensor is the first range value, wherein the second range value is obtained by the following formula:

；

Wherein W represents a second turndown value; w _m represents a full scale corresponding value; x ₀₁ represents the output value of the corresponding ranging sensor at the first range value, i.e., the first sensor value; x _m represents a theoretical sensor output value corresponding to a full scale; e represents an error range span value corresponding to a theoretical output value of the ranging sensor;

Adjusting the input value of the ranging sensor to a second range value, acquiring the output value of the ranging sensor corresponding to the second range value, and taking the output value of the ranging sensor corresponding to the second range value as the second sensor value;

Acquiring an adjustment compensation amount of the ranging sensor by using the first sensor value and the second sensor value;

And carrying out automatic calibration on each measured value of the ranging sensor in real time by combining the adjustment compensation quantity.

4. A rainshed grid-like steel structure radian error determination method as claimed in claim 3, wherein: obtaining an adjustment compensation amount of the ranging sensor using the first sensor value and the second sensor value, comprising:

extracting an input value of a ranging sensor, and adjusting the input value to a theoretical output value corresponding to a first range value, wherein the theoretical output value is used as a first theoretical output value;

Extracting an input value of the ranging sensor and adjusting the input value to a theoretical output value corresponding to a second range value, and taking the theoretical output value as a second theoretical output value;

acquiring a first difference value by using the first sensor value and a first theoretical output value;

Acquiring a second difference value by using the second sensor value and a second theoretical output value;

And obtaining an adjustment compensation amount by using the first difference value and the second difference value, wherein the adjustment compensation amount is obtained by the following formula:

；

Wherein Δx represents the adjustment compensation amount; x ₀₁ represents the output value of the corresponding ranging sensor at the first range value, i.e., the first sensor value; x ₀₂ represents the output value of the corresponding ranging sensor at the second range value, namely the second sensor value; x _e01 represents a first theoretical output value; x _e02 represents a second theoretical output value.

5. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 4, wherein the method comprises the following steps of: automatically calibrating each measured value of the ranging sensor in real time in combination with the adjustment compensation amount, comprising:

When the distance measuring sensor performs distance measuring operation each time, the temperature and the humidity of the environment where the history distance of the distance measuring sensor is used for detecting operation are called as first reference data;

When the distance measuring sensor performs distance measuring operation each time, the light intensity of the environment where the history distance detection operation of the distance measuring sensor is located is called as second reference data;

acquiring an adjustment coefficient by using the first reference data and the second reference data; wherein, the adjustment coefficient is obtained by the following formula:

；

Wherein x represents an adjustment coefficient; n represents the number of times of history distance detection of the distance measuring sensor; x _si represents an actual value corresponding to the ranging of the ith ranging sensor; e _up and e _down represent an upper limit value and a lower limit value of an error range corresponding to ranging of the ranging sensor; t _e、Q_e and B _e respectively represent the highest temperature, humidity and light intensity which meet the normal operation requirements of the ranging sensor; q _si represents the actual ambient humidity corresponding to the ranging of the ith ranging sensor; b _si represents the actual ambient light intensity corresponding to the ranging of the ith ranging sensor; t _si represents the actual ambient temperature corresponding to the ranging of the ith ranging sensor;

acquiring an output value of the ranging sensor after automatic calibration by utilizing the adjustment coefficient and the adjustment compensation quantity; the output value of the ranging sensor after automatic calibration is obtained through the following formula:

；

Wherein, X _t represents the output value of the ranging sensor after automatic calibration; x _c represents the output value of the ranging sensor before auto-calibration; x represents an adjustment coefficient; Δx represents the adjustment compensation amount.

6. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 2, wherein the method comprises the following steps of: when repeated measurement is carried out on the same group of monitoring nodes, the method further comprises the following steps: and recording target data of each measurement, including the number of the monitoring node, the measurement date and time, the environmental condition and the measurement data, and checking the data after each measurement is finished.

7. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 6, wherein the method comprises the following steps of: in the second step, determining the actual radian of the awning at each monitoring node, which specifically comprises the following steps:

Constructing arc sections of all monitoring nodes of the awning grid-shaped steel structure based on the length value and the radian value obtained by each measurement, wherein the arc sections correspond to the measurement data one by one;

fitting arc segment nodes on each arc segment based on the measurement data, extracting a best fit arc according to the arc segment nodes, and calculating a radius value of the best fit arc;

And calculating a corresponding central angle based on the radius value of the arc section and the measured length value of each monitoring node, converting the central angle from the degree to the radian, and determining the actual radian value on each monitoring node.

8. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 7, wherein the method comprises the following steps of: calculating the whole actual curvature of the canopy in the second step, specifically including:

integrating actual radian values: integrating the actual radian values of all the monitoring nodes, checking the actual radian values, and eliminating abnormal values in the actual radian;

Determining the local curvature: determining a curvature calculation model based on each local structural feature of the canopy grid-shaped steel structure, and calculating the local curvature of each monitoring node according to the measurement data of the adjacent monitoring nodes of each monitoring node;

Wherein the curvature calculation model includes:

The Gaussian curvature is used for describing the bending degree of each point on the curved surface, is obtained by calculating the product of two principal curvatures at a certain point on the curved surface, and is suitable for a double-curvature curved surface area;

The average curvature is used for describing the average bending degree of the normal direction at a certain point on the curved surface, and is applicable to a plane and an approximate plane area based on the arithmetic average value of the principal curvature;

Finite element analysis, which is used to simulate and calculate the whole curvature of the curved surface through discretization and numerical approximation, and is suitable for complex nodes and connection areas;

Calculating the overall curvature: and determining the curvature on the non-monitoring node based on an interpolation method, and obtaining the overall actual curvature of the canopy according to the calculation result of the local curvature and the non-monitoring node curvature obtained by interpolation and the weighted average.

9. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 8, wherein the method comprises the following steps of: the error judgment in the third step specifically comprises the following steps:

generating a three-dimensional geometric model of the awning according to the calculated integral actual curvature data, and marking each monitoring node in the three-dimensional geometric model;

extracting an actual radian value and a corresponding arc length and radius of each monitoring node, and integrating to generate radian parameter information of each monitoring node;

obtaining a standard value of each monitoring node radian parameter, carrying out difference calculation on the radian parameter information of each monitoring node and the standard value, and determining a relative error between the radian parameter information and the standard value;

And comparing the relative error of each monitoring node with a preset error threshold value, marking radian parameters of which the relative error exceeds the preset threshold value, and acquiring detailed information corresponding to the monitoring node.

10. The rainshed grid-shaped steel structure radian error judging method as claimed in claim 9, wherein the method comprises the following steps of: the analysis of the result in the fourth step specifically comprises the following steps:

summarizing the relative errors of the actual measurement radian parameters and the standard values of all the monitoring nodes to generate a table, classifying the errors according to a set error threshold value, and marking corresponding positions in the table;

Judging whether systematic errors exist or not based on the trend of error distribution, analyzing possible reasons aiming at the position of each exceeding error, and identifying whether structural defects exist or not;

Optimizing the design of the grid-shaped steel structure of the awning according to the error analysis result, extracting the problem of the installation process, and providing specific process improvement measures;

the measurement results, the error determination results and the improvement measures are displayed in a graph form, and a detailed measurement report is compiled.