A method and system for measuring the perpendicularity of a steel pipe column for pipe column construction

Abstract

This invention belongs to the field of construction surveying technology and discloses a method and system for measuring the verticality of steel pipe columns used in pipe column construction. The method includes: real-time acquisition of reflective surface distribution data and ambient light intensity data at the construction site, and dynamic adjustment of the laser pulse parameters of the total station; dynamically setting a signal reception time window based on the laser pulse parameters to receive reflected signals; removing non-prism reflection signals from the reflected signals to obtain valid signals; precisely filtering interference signals from the valid signals to obtain the target signal; performing signal quality analysis on the target signal to determine whether feedback optimization is needed; if so, optimizing the laser pulse parameters and signal reception time window; otherwise, calculating the verticality of the steel pipe column. This invention not only significantly improves measurement accuracy but also enhances the system's adaptability and autonomy, thereby achieving efficient steel pipe column verticality measurement and improving engineering safety and construction efficiency.

Description

Technical Field

[0001] This invention relates to the field of construction surveying technology, and more specifically, to a method and system for measuring the verticality of steel pipe columns used in pipe column construction. Background Technology

[0002] With the continuous development of construction technology, the accurate measurement of the verticality of steel pipe columns, as important structural support components, has become a key link in ensuring project quality and safety. In actual construction, using a total station in conjunction with a prism for verticality measurement is a mature and widely used method. This method involves fixing a prism at the top of the steel pipe column, with the total station emitting a laser signal and receiving the laser reflected back from the prism, thus achieving accurate measurement of the three-dimensional coordinates of the column top. Combined with the three-dimensional coordinates of the column bottom, the vector of the column's central axis is calculated and compared with the angle of the vertical direction of the design benchmark, thereby obtaining the verticality deviation of the steel pipe column. The verticality measured by this method is not only highly accurate but can also be easily imported into a building information modeling system, supporting digital construction and quality control, and is therefore widely used in pipe column construction sites.

[0003] Although the method of measuring the verticality of steel pipe columns by combining a total station with a prism has significant advantages in terms of accuracy and digital integration, the measurement accuracy is still severely limited by multipath reflection interference in complex construction environments. Construction sites typically contain a large number of metal components, reinforcing bars, and other reflective materials. These objects form complex reflective surfaces around the prism. When the total station emits a laser signal, in addition to the direct reflection from the prism, the laser will also produce multiple reflections on the surfaces of surrounding reflective objects, causing additional reflected signals to be received by the total station and confusing the measurement results. Existing technologies are unable to effectively filter these interference signals and lack the ability to monitor and adaptively adjust the reflection environment in real time, thus affecting the stability and reliability of the measurement results.

[0004] In view of this, the present invention proposes a method and system for measuring the verticality of steel pipe columns used in pipe column construction to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and achieve the above objectives, the present invention provides the following technical solution: a method for measuring the verticality of steel pipe columns used in pipe column construction, comprising:

[0006] Real-time data collection of reflective surface distribution and ambient light intensity at the construction site is used to construct reflective environment information.

[0007] Based on the reflection environment information, the laser pulse parameters of the total station are dynamically adjusted, and the total station is controlled to emit laser signals.

[0008] Based on the laser pulse parameters, the signal reception time window is dynamically set, and the reflected signal is received based on the set signal reception time window;

[0009] Signal feature data is extracted from the reflected signal, and signal filtering analysis is performed on the reflected signal based on the signal feature data to remove non-prism reflection signals in the reflected signal and obtain the effective signal;

[0010] Multipath reflection feature identification is performed on the valid signal, and the interference signal in the valid signal is accurately filtered based on the identification result to obtain the target signal;

[0011] The target signal is analyzed for signal quality to determine whether feedback optimization is needed. If feedback optimization is needed, the laser pulse parameters and signal reception time window are optimized based on the target signal.

[0012] If no feedback optimization is required, the verticality of the steel pipe column is calculated based on the target signal.

[0013] Furthermore, methods for dynamically adjusting the laser pulse parameters of the total station include:

[0014] Each reflector in the reflector distribution data is treated as a node. The existence of edges between nodes is determined based on the reflection paths between reflectors to obtain the edge connection information between all nodes. An adjacency matrix is ​​constructed based on the edge connection information between all nodes.

[0015] Obtain the value range of the laser pulse parameters and mark it as the parameter range; construct multiple sets of candidate parameters based on the parameter range; randomly select a set of candidate parameters as the current parameter;

[0016] Based on the current parameters and reflection environment information, calculate the multipath interference contribution value for each node; based on the adjacency matrix and the multipath interference contribution value, calculate the propagation probability of the laser signal between every two nodes; based on the propagation probability, calculate the steady-state probability of each node in turn; based on the propagation probability and the steady-state probability, calculate the propagation disorder corresponding to the current parameters.

[0017] Similarly, the propagation disorder degree corresponding to each group of candidate parameters is calculated in turn, and all propagation disorder degrees are compared; the candidate parameter with the smallest propagation disorder degree is used as the adjustment parameter, and the laser pulse parameters of the total station are dynamically adjusted based on the adjustment parameter.

[0018] Furthermore, methods for dynamically setting the signal reception time window include:

[0019] Obtain the design coordinates of the column top and the total station coordinates, and calculate the spatial distance between the total station coordinates and the design coordinates of the column top using Euclidean distance; preset the uncertainty, and calculate the distance range based on the uncertainty and the spatial distance; the distance range includes the maximum distance and the minimum distance;

[0020] The clock jitter is obtained and combined with the laser pulse width in the adjustment parameters to calculate the additional time margin; the maximum and minimum round-trip flight times are calculated according to the distance range and the speed of light, respectively.

[0021] Calculate the sum of the maximum round-trip flight time and the extra time margin to obtain the maximum time; calculate the difference between the minimum round-trip flight time and the extra time margin to obtain the minimum time; set the signal reception time window based on the maximum and minimum times;

[0022] Methods for receiving reflected signals include:

[0023] After the total station emits a laser signal, it begins time detection to obtain the duration. When the duration reaches the set minimum time, the total station begins to receive the laser signal reflected back by the prism. When the duration reaches the set maximum time, the total station stops receiving the laser signal reflected back by the prism and uses the received laser signal as the reflected signal.

[0024] Furthermore, methods for extracting signal feature data from reflected signals include:

[0025] The amplitude at each time point in the reflected signal is obtained and compared with a preset amplitude threshold in turn; amplitudes with values ​​greater than the amplitude threshold are marked as signal amplitudes.

[0026] The signal amplitudes at consecutive time points are treated as a set of amplitudes, and the number of amplitudes corresponding to each set is counted sequentially. Each amplitude count is compared with a preset threshold, and the set of amplitudes with a count greater than the threshold is marked as a signal set. Based on the time points of the signal amplitudes corresponding to each signal set, the reflected signal is divided into... Individual reflected signal, The number of signals in the set;

[0027] The kurtosis, skewness, and environmental correlation of each sub-reflection signal are calculated sequentially to form the signal characteristic data of each sub-reflection signal.

[0028] Furthermore, the method for calculating the environmental relevance of each sub-reflection signal includes:

[0029] While receiving reflected signals, overall light intensity data and overall humidity data are continuously collected to form an overall light intensity sequence and an overall humidity sequence. Based on the time point corresponding to the signal amplitude within each sub-reflection signal, sub-light intensity sequences and sub-humidity sequences corresponding to each sub-reflection signal are selected from the overall light intensity sequence and the overall humidity sequence, respectively. Based on the signal set, sub-light intensity sequence, and sub-humidity sequence corresponding to each sub-reflection signal, the light intensity correlation coefficient and humidity correlation coefficient corresponding to each sub-reflection signal are calculated sequentially.

[0030] Calculate the confidence level corresponding to each light intensity correlation coefficient and humidity correlation coefficient, and calculate the weighted average of the light intensity correlation coefficient and humidity correlation coefficient corresponding to each sub-reflection signal based on the confidence level to obtain the environmental correlation level corresponding to each sub-reflection signal.

[0031] Furthermore, the calculation method for the confidence level corresponding to the light intensity correlation coefficient is the same as the calculation method for the confidence level corresponding to the humidity correlation coefficient;

[0032] The method for calculating the confidence level corresponding to the light intensity correlation coefficient is as follows: Calculate the test statistic based on the light intensity correlation coefficient and the amplitude quantity; calculate the actual degrees of freedom based on the amplitude quantity; find the cumulative distribution function value corresponding to the absolute value of the test statistic based on the t-distribution corresponding to the actual degrees of freedom; and calculate the confidence level corresponding to the light intensity correlation coefficient based on the cumulative distribution function value.

[0033] Furthermore, methods for signal filtering analysis of reflected signals include:

[0034] All sub-reflection signals are sequentially assigned incrementally unique numerical labels, which are then marked as signal labels. Each group of signal feature data is sorted from largest to smallest according to the corresponding reflection signal's signal label, resulting in a feature sequence. This feature sequence is then input into a trained signal analysis model to predict the corresponding signal classification result. The signal classification result includes... Type labels, This represents the number of groups of signal feature data in the feature sequence; the type labels include non-prism labels, prism labels, and noise labels.

[0035] Based on the signal classification results, sub-reflection signals with a type label of "non-prism" are marked as non-prism reflection signals, and sub-reflection signals with a type label of "noise" are marked as noise signals. All non-prism reflection signals and noise signals are deleted, and only sub-reflection signals with a type label of "prism" are retained as valid signals.

[0036] Furthermore, methods for acquiring the target signal include:

[0037] Blind source separation is performed sequentially on each valid signal to obtain multiple decomposed signals corresponding to each valid signal; signal feature data is extracted sequentially from each decomposed signal and labeled as decomposed feature data; each set of decomposed feature data is sequentially input into the signal analysis model to obtain the corresponding type label;

[0038] For each valid signal, the type labels corresponding to the multiple decomposed signals are analyzed. If there is no prism label in the type labels, the corresponding valid signal is removed. If there is a prism label in the type labels, the corresponding valid signal is retained. If there are multiple valid signals, the initial time point corresponding to each valid signal is obtained and compared, and the valid signal with the earliest initial time point is selected as the candidate signal. If there is only one valid signal, it is directly used as the candidate signal. The decomposed signals whose type label is not a prism label are filtered out of the candidate signals in turn, and the filtered candidate signals are used as the target signal.

[0039] Furthermore, methods for determining whether feedback optimization is needed include:

[0040] The environmental correlation of the target signal is obtained and its absolute value is taken to obtain the absolute correlation. The absolute correlation is then compared with a preset correlation threshold.

[0041] If the absolute relevance is higher than or equal to the relevance threshold, feedback optimization is required.

[0042] If the absolute relevance is below the relevance threshold, no feedback optimization is needed.

[0043] Methods for optimizing laser pulse parameters and signal reception time windows include:

[0044] Based on the correlation between the laser pulse parameters corresponding to the target signal and the environment, the contribution prediction model is retrained; based on the retrained contribution prediction model, the multipath interference contribution value corresponding to each node under each set of candidate parameters is re-predicted; based on the re-predicted multipath interference contribution value, the propagation disorder of each set of candidate parameters is recalculated; based on the candidate parameter with the smallest propagation disorder after recalculation, the laser pulse parameters are optimized.

[0045] The difference between the absolute correlation value of the target signal and the correlation threshold is calculated to obtain the correlation difference; the standardized threshold is calculated based on the correlation threshold; the standardized correlation is calculated based on the correlation difference and the standardized threshold; the time amplification is calculated based on the standardized correlation, the maximum time, and the preset maximum amplification factor; the maximum time is optimized based on the sum of the time amplification and the maximum time.

[0046] A system for measuring the verticality of steel pipe columns used in pipe column construction, and a method for measuring the verticality of steel pipe columns used in pipe column construction, comprising:

[0047] The environmental monitoring module is used to collect real-time data on the distribution of reflective surfaces and ambient light intensity at the construction site to construct reflective environmental information.

[0048] The emission adjustment module is used to dynamically adjust the laser pulse parameters of the total station based on the reflection environment information, and to control the total station to emit laser signals.

[0049] The gated receiver module is used to dynamically set the signal reception time window according to the laser pulse parameters, and to receive the reflected signal based on the set signal reception time window;

[0050] The signal analysis module is used to extract signal feature data from the reflected signal and perform signal filtering analysis on the reflected signal based on the signal feature data to remove non-prism reflection signals in the reflected signal and obtain the effective signal.

[0051] The intelligent filtering module is used to identify the multipath reflection characteristics of the valid signal, and to accurately filter the interference signals in the valid signal based on the identification results to obtain the target signal;

[0052] The feedback optimization module is used to analyze the signal quality of the target signal and determine whether feedback optimization is needed. If feedback optimization is needed, the laser pulse parameters and signal reception time window are optimized based on the target signal.

[0053] The geometry calculation module is used to calculate the verticality of the steel pipe column based on the target signal if feedback optimization is not required.

[0054] The technical effects and advantages of the present invention regarding a method and system for measuring the verticality of steel pipe columns used in pipe column construction are as follows:

[0055] By monitoring the reflection environment information at the construction site in real time and dynamically adjusting the laser pulse parameters of the total station, it can effectively adapt to complex multi-reflection scenarios, reduce the impact of multipath interference on measurement accuracy, and significantly improve measurement accuracy and stability. By dynamically setting the signal reception time window, it can accurately capture the laser signal reflected back by the prism and avoid receiving other irrelevant reflection signals. By adopting a precise filtering method based on signal feature data and multipath reflection characteristics, it can accurately identify valid signals, efficiently eliminate various non-prism reflection signals, and effectively eliminate residual interference components in valid signals, ensuring the acquisition of high-quality target signals. Based on the quality status of the target signal, an adaptive feedback optimization mechanism is implemented to dynamically adjust the laser pulse parameters and signal reception time window, further improving the reliability and stability of the measurement results. By fully utilizing multi-sensor fusion sensing, intelligent signal processing, adaptive control, and other technologies, multipath reflection interference is effectively filtered to ensure the reliability of the target signal. This provides an effective solution to the measurement challenges in complex building construction environments, not only significantly improving measurement accuracy but also enhancing the system's adaptability and autonomy, thereby achieving efficient steel pipe column verticality measurement and improving engineering safety and construction efficiency. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of a steel pipe column verticality measurement system for pipe column construction according to Embodiment 1 of the present invention;

[0057] Figure 2 This is a flowchart of a method for measuring the verticality of steel pipe columns used in pipe column construction, according to Embodiment 2 of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1

[0060] Please see Figure 1 As shown in this embodiment, a steel pipe column verticality measurement system for pipe column construction includes an environmental monitoring module, a transmission adjustment module, a gating receiving module, a signal analysis module, an intelligent filtering module, a feedback optimization module, and a geometric calculation module. The modules are connected via wired and / or wireless means to realize data transmission between the modules.

[0061] The environmental monitoring module is used to collect real-time data on the distribution of reflective surfaces and ambient light intensity at the construction site, and to construct reflective environmental information.

[0062] Reflective surface distribution data refers to the spatial distribution and reflection intensity of reflective surfaces (i.e., the surfaces of reflective objects) within the construction site. This data is obtained by collecting reflection intensity signals from different directions using multi-angle optical sensors deployed within the construction site, combined with spatial positioning algorithms (such as triangulation, time-of-flight ranging, and laser scanning modeling).

[0063] Ambient light intensity data refers to the distribution of light intensity in the measurement area within the construction site, which is obtained through ambient light sensors deployed within the measurement area; the measurement area is the spatial range covered by the laser signal emitted by the total station during the measurement of the verticality of the steel pipe column.

[0064] Based on real-time collected data on the distribution of reflective surfaces and ambient light intensity, reflective environment information is constructed.

[0065] It should be noted that the purpose of collecting reflective surface distribution data and ambient light intensity data is:

[0066] A comprehensive understanding of the spatial distribution of reflective objects and changes in light intensity within the construction site provides precise environmental information support for the total station's laser emission and signal reception. Data on reflective surface distribution allows for the identification of potential multipath reflection sources, facilitating dynamic adjustments to laser pulse parameters and flexible setting of signal reception time windows to avoid interference. Ambient light intensity data reflects the lighting conditions at the construction site, aiding in further optimization of laser pulse parameters and signal reception time windows. By comprehensively utilizing these two types of data, the system can dynamically adapt to complex and changing construction environments, effectively suppress multipath reflection interference, improve signal quality and measurement accuracy, and ensure the accuracy and stability of steel pipe column verticality measurements.

[0067] The emission adjustment module is used to dynamically adjust the laser pulse parameters of the total station based on the reflection environment information, and to control the total station to emit laser signals.

[0068] Methods for dynamically adjusting the laser pulse parameters of a total station include:

[0069] Each reflector in the reflector distribution data is considered as a node. Based on the reflection path between the reflectors, it is determined whether there is an edge connecting the nodes, thus obtaining the connection status of all nodes. Specifically, those skilled in the art, combining the reflector distribution data and practical experience, sequentially determine whether the laser signal can be reflected and propagated from one reflector to another. If it can, then there is an edge connecting the two corresponding nodes; if not, then there is no edge connecting the two corresponding nodes.

[0070] Construct an adjacency matrix based on the edge connections between all nodes. Each element in the adjacency matrix indicates whether there is an edge connecting two corresponding nodes; if so, the value of the corresponding element is 1, and if not, the value of the corresponding element is 0. For example, the elements in the adjacency matrix... This indicates that there is an edge connecting the first node and the second node, meaning that the laser signal can be reflected from the first reflecting surface to the second reflecting surface.

[0071] Obtain the value range of the laser pulse parameters and mark it as the parameter range; randomly select a value from each parameter range to construct a set of candidate parameters, and so on to construct multiple sets of candidate parameters, all of which are different; randomly select a set of candidate parameters as the current parameter; the laser pulse parameters include the laser pulse width and the laser pulse frequency; the laser pulse width refers to the duration of a single laser pulse emission, and adjusting the laser pulse width can control the time resolution and energy of the laser signal; the laser pulse frequency refers to the number of laser pulses emitted per second by the total station, and adjusting the laser pulse frequency can avoid overlap or interference between the laser signal reflected by the prism and the laser signals reflected by various reflecting surfaces in the construction site, preventing signal confusion and misjudgment, thereby improving the accuracy and stability of the measurement;

[0072] Based on the current parameters and reflection environment information, calculate the multipath interference contribution value for each node, representing the specific contribution of each reflector to multipath interference. Based on the adjacency matrix and the multipath interference contribution value, calculate the propagation probability of the laser signal between every two nodes, representing the probability of the laser signal propagating from one reflector to another. Based on the propagation probability, calculate the steady-state probability for each node, representing the probability of the laser signal being distributed across each node. Based on the propagation probability and the steady-state probability, calculate the propagation disorder corresponding to the current parameters, representing the uncertainty and randomness of the laser signal in the propagation path. A higher propagation disorder indicates a more complex and diverse propagation path for the laser signal, and more severe multipath interference, and vice versa.

[0073] Similarly, the propagation disorder degree corresponding to each group of candidate parameters is calculated in turn, and all propagation disorder degrees are compared; the candidate parameter with the smallest propagation disorder degree is used as the adjustment parameter, and the laser pulse parameters of the total station are dynamically adjusted based on the adjustment parameter.

[0074] Methods for calculating the multipath interference contribution value for each node include:

[0075] The current parameters and the reflection intensity and illumination intensity corresponding to each reflecting surface are used as a set of contribution analysis data. That is, a set of contribution analysis data includes the reflection intensity and illumination intensity corresponding to a reflecting surface and the current parameters. Each set of contribution analysis data is input into the trained contribution prediction model to predict the corresponding multipath interference contribution value.

[0076] The contribution prediction model is a deep neural network model, which includes an input layer, a hidden layer, and an output layer. Each hidden layer contains multiple neurons, and each neuron is connected to the neurons in the next layer. The connections contain weights that determine the importance and impact of the data in the neural network. An activation function is applied to each neuron between the hidden layer and the output layer. The activation function introduces non-linearity, allowing the network to learn more complex patterns and features. The deep neural network model is a current technology, and the specific training process will not be described in detail here.

[0077] Methods for calculating the propagation probability of a laser signal between any two nodes include:

[0078] Take any two nodes as a set of nodes, calculate the product of the multipath interference contribution value corresponding to each set of nodes and the element in the corresponding adjacency matrix to obtain the weighted connection strength; calculate the sum of all weighted connection strengths to obtain the comprehensive connection strength; calculate the ratio between each weighted connection strength and the comprehensive connection strength in turn to obtain the propagation probability corresponding to each set of nodes.

[0079] The expression for calculating the steady-state probability of each node is:

[0080] ;

[0081] In the formula, For the first The steady-state probability of each node. For the first The node and the first The propagation probability between nodes Let be the number of nodes; where the sum of the steady-state probabilities of all nodes is 1.

[0082] The expression for calculating the degree of propagation disorder is:

[0083] ;

[0084] In the formula, To spread confusion.

[0085] The total station is controlled to emit laser signals based on the dynamically adjusted laser pulse parameters.

[0086] The gated receiver module is used to dynamically set the signal reception time window according to the laser pulse parameters, and to receive the reflected signal based on the set signal reception time window.

[0087] Methods for dynamically setting the signal reception time window include:

[0088] Obtain the design coordinates of the column top (i.e., the theoretical coordinates of the center position of the top of the steel pipe column) using BIM model or construction drawings; obtain the total station coordinates using GPS, and calculate the spatial distance between the total station coordinates and the design coordinates of the column top using Euclidean distance; preset the uncertainty, and calculate the distance range based on the uncertainty and the spatial distance; wherein, the distance range includes the maximum distance and the minimum distance, the maximum distance is the sum of the uncertainty and the spatial distance, and the minimum distance is the difference between the spatial area and the uncertainty; the uncertainty is preset by those skilled in the art based on the actual situation and experience of the construction site;

[0089] According to the total station's equipment specifications, obtain the clock jitter; add the clock jitter to half of the laser pulse width in the adjustment parameters to obtain an additional time margin; calculate the ratio of twice the maximum distance to the speed of light to obtain the maximum round-trip flight time; calculate the ratio of twice the minimum distance to the speed of light to obtain the minimum round-trip flight time; where the round-trip flight time is the length of time required for the laser signal to travel from the total station to the prism and back to the total station.

[0090] Calculate the sum of the maximum round-trip flight time and the extra time margin to obtain the maximum time; calculate the difference between the minimum round-trip flight time and the extra time margin to obtain the minimum time; set the signal reception time window based on the maximum and minimum times.

[0091] Methods for receiving reflected signals include:

[0092] After the total station emits a laser signal, the built-in timer of the total station is started to detect the time and obtain the duration. When the duration reaches the set minimum time, the receiving channel of the total station is turned on to start receiving the laser signal reflected back by the prism. When the duration reaches the set maximum time, the receiving channel of the total station is turned off to stop receiving the laser signal reflected back by the prism, and the received laser signal is used as the reflected signal.

[0093] The signal analysis module is used to extract signal feature data from the reflected signal and perform signal filtering analysis on the reflected signal based on the signal feature data to remove non-prism reflection signals in the reflected signal and obtain the effective signal.

[0094] Methods for extracting signal feature data from reflected signals include:

[0095] The amplitude corresponding to each time point in the reflected signal is obtained and compared with a preset amplitude threshold in turn; amplitudes with values ​​greater than the amplitude threshold are marked as signal amplitudes, while amplitudes with values ​​less than or equal to the amplitude threshold are not marked; wherein, the amplitude threshold is preset by a person skilled in the art based on the environmental noise level of the construction site.

[0096] The signal amplitudes at consecutive time points are grouped into amplitude sets, and the number of amplitudes corresponding to each amplitude set (i.e., the number of signal amplitudes within each amplitude set) is counted sequentially. Each amplitude count is compared sequentially with a preset quantity threshold. Amplitude sets with an amplitude count greater than the threshold are marked as signal sets, while amplitude sets with an amplitude count less than or equal to the threshold are not marked. The quantity threshold is preset by those skilled in the art based on actual conditions to avoid misjudging occasional noise spikes as sub-reflection signals. Based on the time points corresponding to the signal amplitudes of each signal set, the reflected signals are divided into... Individual reflected signal, The number of signals in the set;

[0097] The kurtosis, skewness, and environmental correlation of each sub-reflection signal are calculated sequentially to form the signal characteristic data of each sub-reflection signal.

[0098] The calculation method for the kurtosis of each sub-reflection signal includes:

[0099] The mean and standard deviation of the signal amplitude corresponding to each sub-reflection signal are calculated sequentially to obtain the mean and standard deviation of the amplitude for each sub-reflection signal. Based on the mean and standard deviation of the amplitude, the standardized amplitude corresponding to each signal amplitude is calculated sequentially. The calculation method of the standardized amplitude is as follows: subtract the mean amplitude of the corresponding sub-reflection signal from the signal amplitude, and then divide by the standard deviation of the amplitude corresponding to the corresponding sub-reflection signal to obtain the standardized amplitude. The fourth power calculation is performed on each standardized amplitude sequentially to obtain the fourth power amplitude. The fourth power amplitudes corresponding to the same sub-reflection signal are added together sequentially and then divided by the corresponding amplitude number to obtain the kurtosis of each sub-reflection signal.

[0100] The method for calculating the bias of each sub-reflected signal includes:

[0101] The cubic amplitude is calculated sequentially for each standardized amplitude; the cubic amplitudes corresponding to the same sub-reflection signal are added together sequentially and then divided by the corresponding number of amplitudes to obtain the skewness of each sub-reflection signal.

[0102] The methods for calculating the environmental relevance of each sub-reflection signal include:

[0103] While receiving reflected signals, overall light intensity data and overall humidity data are continuously collected to form overall light intensity sequences and overall humidity sequences. Overall light intensity data refers to the illumination intensity of the entire measurement area within the construction site, and overall humidity data refers to the humidity of the entire measurement area within the construction site. Overall light intensity data is also acquired through ambient light sensors deployed within the measurement area, and overall humidity data is acquired through humidity sensors deployed within the measurement area. It should be noted that the amplitude of non-prism reflected signals is significantly affected by environmental factors (i.e., light intensity and humidity), fluctuating noticeably with environmental changes, thus exhibiting high environmental correlation. In contrast, prism reflected signals, due to the stability of the prism structure and materials, are insensitive to environmental changes, exhibiting relatively stable amplitudes, and therefore exhibiting low environmental correlation.

[0104] Based on the time point corresponding to the signal amplitude within each sub-reflection signal, sub-light intensity sequences and sub-humidity sequences corresponding to each sub-reflection signal are selected from the overall light intensity sequence and the overall humidity sequence, respectively. Based on the signal set, sub-light intensity sequence, and sub-humidity sequence corresponding to each sub-reflection signal, the light intensity correlation coefficient and humidity correlation coefficient corresponding to each sub-reflection signal are calculated sequentially. The light intensity correlation coefficient is the Pearson correlation coefficient between the signal set and the sub-light intensity sequence, and the humidity correlation coefficient is the Pearson correlation coefficient between the signal set and the sub-humidity sequence. The calculation method for the Pearson correlation coefficient is existing technology, and the specific calculation process will not be elaborated upon here.

[0105] Calculate the confidence level corresponding to each light intensity correlation coefficient and humidity correlation coefficient, and calculate the weighted average of the light intensity correlation coefficient and humidity correlation coefficient corresponding to each sub-reflection signal based on the confidence level to obtain the environmental correlation level corresponding to each sub-reflection signal.

[0106] The calculation method for the confidence level corresponding to the light intensity correlation coefficient is the same as that for the humidity correlation coefficient. The calculation method for the confidence level corresponding to the light intensity correlation coefficient is as follows: Calculate the test statistic based on the light intensity correlation coefficient and the amplitude quantity; calculate the difference between the amplitude quantity and the two values ​​to obtain the actual degrees of freedom; based on the t-distribution corresponding to the actual degrees of freedom, find the cumulative distribution function value corresponding to the absolute value of the test statistic through the t-distribution table; subtract the cumulative distribution function value from the first value and multiply by the second value to obtain the error probability; subtract the error probability from the first value to obtain the confidence level corresponding to the light intensity correlation coefficient.

[0107] The expression for the test statistic is as follows:

[0108] ;

[0109] In the formula, To test the statistic, The correlation coefficient is the light intensity. This refers to the amplitude quantity.

[0110] Methods for calculating the weighted average of the correlation coefficient between light intensity and humidity include:

[0111] Fisher's z-transform is applied to the light intensity correlation coefficient and humidity correlation coefficient respectively to obtain the transformation coefficients. Each confidence level is then subjected to a negative logarithmic transformation to obtain the weighted coefficients of each transformation coefficient. The weighted sum of each transformation coefficient is then calculated and divided by the sum of all weighted coefficients to obtain the comprehensive transformation value. Finally, the inverse Fisher's z-transform is applied to the comprehensive transformation value to obtain the environmental correlation. Fisher's z-transform, negative logarithmic transformation, and inverse Fisher's z-transform are all existing technologies, and their specific expressions are not elaborated upon here.

[0112] Methods for signal filtering analysis of reflected signals include:

[0113] All sub-reflection signals are sequentially assigned incrementally unique numerical labels, which are then marked as signal labels. Each group of signal feature data is sorted from largest to smallest according to the corresponding reflection signal's signal label, resulting in a feature sequence. This feature sequence is then input into a trained signal analysis model to predict the corresponding signal classification result. The signal classification result includes... Type labels, The number of signal feature data sets in the feature sequence; the type labels include non-prism labels, prism labels, and noise labels. Non-prism labels are the digital labels corresponding to non-prism reflected signals (i.e., reflected signals from other reflective surfaces within the construction site), prism labels are the digital labels corresponding to prism reflected signals (i.e., reflected signals from prisms), and noise labels are the digital labels corresponding to noise signals (i.e., random noise interference that is not a signal); the signal analysis model is a deep neural network model.

[0114] Based on the signal classification results, sub-reflection signals with a type label of "non-prism" are marked as non-prism reflection signals, and sub-reflection signals with a type label of "noise" are marked as noise signals. All non-prism reflection signals and noise signals are deleted, and only sub-reflection signals with a type label of "prism" are retained as valid signals.

[0115] The intelligent filtering module is used to identify the multipath reflection characteristics of the valid signal, and based on the identification results, it accurately filters the interference signals in the valid signal to obtain the target signal.

[0116] Methods for acquiring target signals include:

[0117] Blind source separation is performed sequentially on each valid signal to obtain multiple decomposed signals corresponding to each valid signal; blind source separation methods include independent component analysis, nonnegative matrix factorization, etc.; corresponding signal feature data are extracted from each decomposed signal sequentially and labeled as decomposed feature data; each set of decomposed feature data is sequentially input into the signal analysis model to obtain the corresponding type label;

[0118] For each valid signal, the type labels corresponding to the multiple decomposed signals are analyzed. If there is no prism label in the type labels, the corresponding valid signal is removed. If there is a prism label in the type labels, the corresponding valid signal is retained. If there are multiple valid signals, the initial time point corresponding to each valid signal (i.e., the earliest time point among all time points corresponding to the valid signal) is obtained and compared, and the valid signal with the earliest initial time point is selected as the candidate signal. If there is only one valid signal, it is directly used as the candidate signal. The decomposed signals whose type label is not a prism label are filtered out from the candidate signals in turn, and the filtered candidate signals are used as the target signal.

[0119] The feedback optimization module is used to analyze the signal quality of the target signal and determine whether feedback optimization is needed. If feedback optimization is needed, the laser pulse parameters and signal reception time window are optimized based on the target signal.

[0120] Methods for determining whether feedback optimization is needed include:

[0121] The environmental correlation of the target signal is obtained and its absolute value is taken to obtain the absolute correlation. The absolute correlation is compared with a preset correlation threshold, which is preset by a person skilled in the art based on the actual situation. If the absolute correlation is higher than or equal to the correlation threshold, feedback optimization is required. If the absolute correlation is lower than the correlation threshold, feedback optimization is not required.

[0122] Methods for optimizing laser pulse parameters and signal reception time windows include:

[0123] Based on the correlation between the laser pulse parameters corresponding to the target signal and the environment, the contribution prediction model is retrained, that is, the correlation between the laser pulse parameters corresponding to the target signal and the environment is also used as the input of the contribution prediction model. Based on the retrained contribution prediction model, the multipath interference contribution value corresponding to each node under each set of candidate parameters is re-predicted. Based on the re-predicted multipath interference contribution value, the propagation disorder of each set of candidate parameters is recalculated. Based on the candidate parameter with the minimum propagation disorder after recalculation, the laser pulse parameters are optimized.

[0124] The difference between the absolute correlation value of the target signal and the correlation threshold is calculated to obtain the correlation difference; the difference between the absolute correlation value and the correlation threshold is calculated to obtain the standardized threshold; the ratio between the correlation difference and the standardized threshold is calculated to obtain the standardized correlation; the product of the standardized correlation and the preset maximum amplification factor is calculated to obtain the actual amplification factor; the product of the actual amplification factor and the maximum time is calculated to obtain the time amplification; the maximum time is optimized based on the sum of the time amplification and the maximum time; the maximum amplification factor is preset by those skilled in the art according to the actual situation.

[0125] The geometry calculation module is used to calculate the verticality of the steel pipe column based on the target signal if feedback optimization is not required.

[0126] The method for calculating the verticality of steel pipe columns is an existing technology. The specific calculation process is only briefly described here: Based on the target signal, the coordinates of the top of the steel pipe column are obtained, and the coordinates of the bottom of the steel pipe column are obtained using the same method; the central axis vector of the steel pipe column is calculated based on the coordinates of the top and bottom of the column, and the angle is compared with the preset reference vertical direction (i.e., the unit vector of the vertical direction) to obtain the verticality deviation angle of the steel pipe column; the difference between the right angle and the verticality deviation angle is calculated to obtain the verticality.

[0127] This embodiment effectively adapts to complex multi-reflection scenarios by dynamically adjusting the laser pulse parameters of the total station through real-time monitoring of the reflection environment at the construction site. This reduces the impact of multipath interference on measurement accuracy and significantly improves measurement accuracy and stability. By dynamically setting the signal reception time window, it can accurately capture the laser signal reflected back from the prism, avoiding the reception of other irrelevant reflection signals. Employing a precise filtering method based on signal feature data and multipath reflection characteristics, it can accurately identify valid signals, efficiently eliminate various non-prism reflection signals, and effectively eliminate residual interference components in the valid signals, ensuring the acquisition of high-quality target signals. An adaptive feedback optimization mechanism is implemented to dynamically adjust the laser pulse parameters and signal reception time window based on the quality status of the target signal, further improving the reliability and stability of the measurement results. By fully utilizing multi-sensor fusion sensing, intelligent signal processing, and adaptive control technologies, it effectively filters multipath reflection interference, ensuring the reliability of the target signal. This provides an effective solution to the measurement challenges in complex construction environments, significantly improving measurement accuracy and enhancing the system's adaptability and autonomy, thereby achieving efficient steel pipe column verticality measurement and improving engineering safety and construction efficiency.

[0128] Example 2

[0129] Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A method for measuring the verticality of steel pipe columns used in pipe column construction is provided, the method including:

[0130] Real-time data collection of reflective surface distribution and ambient light intensity at the construction site is used to construct reflective environment information.

[0131] Based on the reflection environment information, the laser pulse parameters of the total station are dynamically adjusted, and the total station is controlled to emit laser signals.

[0132] Based on the laser pulse parameters, the signal reception time window is dynamically set, and the reflected signal is received based on the set signal reception time window;

[0133] Signal feature data is extracted from the reflected signal, and signal filtering analysis is performed on the reflected signal based on the signal feature data to remove non-prism reflection signals in the reflected signal and obtain the effective signal;

[0134] Multipath reflection feature identification is performed on the valid signal, and the interference signal in the valid signal is accurately filtered based on the identification result to obtain the target signal;

[0135] The target signal is analyzed for signal quality to determine whether feedback optimization is needed. If feedback optimization is needed, the laser pulse parameters and signal reception time window are optimized based on the target signal.

[0136] If no feedback optimization is required, the verticality of the steel pipe column is calculated based on the target signal.

[0137] Example 3

[0138] This application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable code that, when executed by the one or more processors, can perform a method for measuring the verticality of steel pipe columns used in pipe column construction as described above.

[0139] The method or system according to the embodiments of this application can also be implemented using the architecture of the electronic device shown in this application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. The storage device in the electronic device, such as a ROM or hard disk, may store a method for measuring the verticality of steel pipe columns for pipe column construction provided in this application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in this application is merely exemplary; when implementing different devices, one or more components of the electronic device shown in this application may be omitted according to actual needs.

[0140] Example 4

[0141] One embodiment of this application discloses a computer-readable storage medium. The computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor, a method for measuring the verticality of a steel pipe column for pipe pile construction, as described above with reference to the accompanying drawings, according to an embodiment of this application, can be performed. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0142] Furthermore, according to embodiments of this application, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, this application provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be executed by a processor to perform instructions corresponding to the method steps provided in this application, such as a method for measuring the verticality of steel pipe columns used in pipe column construction. When this computer program is executed by a central processing unit (CPU), it performs the functions defined in the method of this application.

[0143] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0144] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0145] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0146] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0147] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0148] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0149] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0150] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for measuring the verticality of steel pipe columns used in pipe column construction, characterized in that, include: Real-time data collection of reflective surface distribution and ambient light intensity at the construction site is used to construct reflective environment information. Based on the reflection environment information, the laser pulse parameters of the total station are dynamically adjusted, and the total station is controlled to emit laser signals. Based on the laser pulse parameters, the signal reception time window is dynamically set, and the reflected signal is received based on the set signal reception time window; Signal feature data is extracted from the reflected signal, and signal filtering analysis is performed on the reflected signal based on the signal feature data to remove non-prism reflection signals in the reflected signal and obtain the effective signal; Multipath reflection feature identification is performed on the valid signal, and the interference signal in the valid signal is accurately filtered based on the identification result to obtain the target signal; The target signal is analyzed for signal quality to determine whether feedback optimization is needed. If feedback optimization is needed, the laser pulse parameters and signal reception time window are optimized based on the target signal. If no feedback optimization is required, the verticality of the steel pipe column is calculated based on the target signal.

2. The method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 1, characterized in that, Methods for dynamically adjusting the laser pulse parameters of a total station include: Each reflector in the reflector distribution data is treated as a node. The existence of edges between nodes is determined based on the reflection paths between reflectors to obtain the edge connection information between all nodes. An adjacency matrix is ​​constructed based on the edge connection information between all nodes. Obtain the value range of the laser pulse parameters and mark it as the parameter range; construct multiple sets of candidate parameters based on the parameter range; randomly select a set of candidate parameters as the current parameter; Based on the current parameters and reflection environment information, calculate the multipath interference contribution value for each node; based on the adjacency matrix and the multipath interference contribution value, calculate the propagation probability of the laser signal between every two nodes; based on the propagation probability, calculate the steady-state probability of each node in turn; based on the propagation probability and the steady-state probability, calculate the propagation disorder corresponding to the current parameters. Similarly, the propagation disorder degree corresponding to each group of candidate parameters is calculated in turn, and all propagation disorder degrees are compared; the candidate parameter with the smallest propagation disorder degree is used as the adjustment parameter, and the laser pulse parameters of the total station are dynamically adjusted based on the adjustment parameter.

3. The method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 2, characterized in that, Methods for dynamically setting the signal reception time window include: Obtain the design coordinates of the column top and the total station coordinates, and calculate the spatial distance between the total station coordinates and the design coordinates of the column top using Euclidean distance; preset the uncertainty, and calculate the distance range based on the uncertainty and the spatial distance; the distance range includes the maximum distance and the minimum distance; The clock jitter is obtained and combined with the laser pulse width in the adjustment parameters to calculate the additional time margin; the maximum and minimum round-trip flight times are calculated according to the distance range and the speed of light, respectively. Calculate the sum of the maximum round-trip flight time and the extra time margin to obtain the maximum time; calculate the difference between the minimum round-trip flight time and the extra time margin to obtain the minimum time; set the signal reception time window based on the maximum and minimum times; Methods for receiving reflected signals include: After the total station emits a laser signal, it begins time detection to obtain the duration. When the duration reaches the set minimum time, the total station begins to receive the laser signal reflected back by the prism. When the duration reaches the set maximum time, the total station stops receiving the laser signal reflected back by the prism and uses the received laser signal as the reflected signal.

4. The method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 3, characterized in that, Methods for extracting signal feature data from reflected signals include: The amplitude at each time point in the reflected signal is obtained and compared with a preset amplitude threshold in turn; amplitudes with values ​​greater than the amplitude threshold are marked as signal amplitudes. The signal amplitudes at consecutive time points are treated as a set of amplitudes, and the number of amplitudes corresponding to each set is counted sequentially. Each amplitude count is compared with a preset threshold, and the set of amplitudes with a count greater than the threshold is marked as a signal set. Based on the time points of the signal amplitudes corresponding to each signal set, the reflected signal is divided into... Individual reflected signal, The number of signals in the set; The kurtosis, skewness, and environmental correlation of each sub-reflection signal are calculated sequentially to form the signal characteristic data of each sub-reflection signal.

5. The method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 4, characterized in that, The methods for calculating the environmental relevance of each sub-reflection signal include: While receiving reflected signals, overall light intensity data and overall humidity data are continuously collected to form an overall light intensity sequence and an overall humidity sequence. Based on the time point corresponding to the signal amplitude within each sub-reflection signal, sub-light intensity sequences and sub-humidity sequences corresponding to each sub-reflection signal are selected from the overall light intensity sequence and the overall humidity sequence, respectively. Based on the signal set, sub-light intensity sequence, and sub-humidity sequence corresponding to each sub-reflection signal, the light intensity correlation coefficient and humidity correlation coefficient corresponding to each sub-reflection signal are calculated sequentially. Calculate the confidence level corresponding to each light intensity correlation coefficient and humidity correlation coefficient, and calculate the weighted average of the light intensity correlation coefficient and humidity correlation coefficient corresponding to each sub-reflection signal based on the confidence level to obtain the environmental correlation level corresponding to each sub-reflection signal.

6. The method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 5, characterized in that, The method for calculating the confidence level of the light intensity correlation coefficient is the same as the method for calculating the confidence level of the humidity correlation coefficient; The confidence level corresponding to the light intensity correlation coefficient is calculated as follows: the test statistic is calculated based on the light intensity correlation coefficient and the amplitude quantity. Calculate the actual degrees of freedom based on the amplitude, and find the cumulative distribution function value corresponding to the absolute value of the test statistic based on the t-distribution corresponding to the actual degrees of freedom. Calculate the confidence level corresponding to the light intensity correlation coefficient based on the cumulative distribution function value.

7. The method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 6, characterized in that, Methods for signal filtering analysis of reflected signals include: All sub-reflection signals are assigned sequentially incrementing unique numerical labels and marked as signal labels; each group of signal feature data is sorted from largest to smallest according to the corresponding signal label of the reflected signal to obtain a feature sequence; the feature sequence is input into the trained signal analysis model to predict the corresponding signal classification result; the signal classification result includes b type labels, where b is the number of groups of signal feature data in the feature sequence; the type labels include non-prism label, prism label, and noise label; Based on the signal classification results, sub-reflection signals with a type label of "non-prism" are marked as non-prism reflection signals, and sub-reflection signals with a type label of "noise" are marked as noise signals. All non-prism reflection signals and noise signals are deleted, and only sub-reflection signals with a type label of "prism" are retained as valid signals.

8. A method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 7, characterized in that, Methods for acquiring target signals include: Blind source separation is performed sequentially on each valid signal to obtain multiple decomposed signals corresponding to each valid signal; signal feature data is extracted sequentially from each decomposed signal and labeled as decomposed feature data; each set of decomposed feature data is sequentially input into the signal analysis model to obtain the corresponding type label; For each valid signal, the type labels corresponding to the multiple decomposed signals are analyzed. If there is no prism label in the type labels, the corresponding valid signal is removed. If there is a prism label in the type labels, the corresponding valid signal is retained. If there are multiple valid signals, the initial time point corresponding to each valid signal is obtained and compared, and the valid signal with the earliest initial time point is selected as the candidate signal. If there is only one valid signal, it is directly used as the candidate signal. The decomposed signals whose type label is not a prism label are filtered out of the candidate signals in turn, and the filtered candidate signals are used as the target signal.

9. A method for measuring the verticality of steel pipe columns used in pipe column construction according to claim 8, characterized in that, Methods for determining whether feedback optimization is needed include: The environmental correlation of the target signal is obtained and its absolute value is taken to obtain the absolute correlation. The absolute correlation is then compared with a preset correlation threshold. If the absolute relevance is higher than or equal to the relevance threshold, feedback optimization is required. If the absolute relevance is below the relevance threshold, no feedback optimization is needed. Methods for optimizing laser pulse parameters and signal reception time windows include: Based on the correlation between the laser pulse parameters corresponding to the target signal and the environment, the contribution prediction model is retrained; based on the retrained contribution prediction model, the multipath interference contribution value corresponding to each node under each set of candidate parameters is re-predicted; based on the re-predicted multipath interference contribution value, the propagation disorder of each set of candidate parameters is recalculated; based on the candidate parameter with the smallest propagation disorder after recalculation, the laser pulse parameters are optimized. The difference between the absolute correlation value of the target signal and the correlation threshold is calculated to obtain the correlation difference; the standardized threshold is calculated based on the correlation threshold; the standardized correlation is calculated based on the correlation difference and the standardized threshold; the time amplification is calculated based on the standardized correlation, the maximum time, and the preset maximum amplification factor; the maximum time is optimized based on the sum of the time amplification and the maximum time.

10. A system for measuring the verticality of steel pipe columns used in pipe column construction, implementing the method for measuring the verticality of steel pipe columns used in pipe column construction as described in any one of claims 1-9, characterized in that, include: The environmental monitoring module is used to collect real-time data on the distribution of reflective surfaces and ambient light intensity at the construction site to construct reflective environmental information. The emission adjustment module is used to dynamically adjust the laser pulse parameters of the total station based on the reflection environment information, and to control the total station to emit laser signals. The gated receiver module is used to dynamically set the signal reception time window according to the laser pulse parameters, and to receive the reflected signal based on the set signal reception time window; The signal analysis module is used to extract signal feature data from the reflected signal and perform signal filtering analysis on the reflected signal based on the signal feature data to remove non-prism reflection signals in the reflected signal and obtain the effective signal. The intelligent filtering module is used to identify the multipath reflection characteristics of the valid signal, and to accurately filter the interference signals in the valid signal based on the identification results to obtain the target signal; The feedback optimization module is used to analyze the signal quality of the target signal and determine whether feedback optimization is needed. If feedback optimization is needed, the laser pulse parameters and signal reception time window are optimized based on the target signal. The geometry calculation module is used to calculate the verticality of the steel pipe column based on the target signal if feedback optimization is not required.

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