Shaftway type construction method based on cloud intelligent building

Abstract

The invention relates to the technical field of intelligent hoistway elevator construction detection, and particularly discloses a hoistway type construction method based on cloud intelligent building, which is used for solving the problem that deformation is easily caused and is difficult to detect in the transportation process of an existing hoistway type elevator guide rail. Guide rail point cloud data before transportation and after transportation are obtained through the laser displacement sensor, guide rail image data before transportation and guide rail image data after transportation are obtained, and the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data. And constructing a multi-dimensional guide rail damage model according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, analyzing whether the guide rail is deformed, and when deformation of the guide rail is detected, marking the guide rail and triggering alarm reminding.

Description

Technical Field

[0001] The present invention relates to the field of intelligent shaft elevator construction detection technology, and more specifically, to a shaft construction method based on cloud-based intelligent construction. Background Art

[0002] During the construction of a shaft elevator, the guide rail system is one of the core components to ensure the stable operation of the elevator car. The main function of the guide rail is to provide vertical guidance for the elevator car and counterweight to avoid deviation or shaking during operation. However, during the actual construction and use of the guide rail and bracket, due to improper transportation, stacking and installation methods, the guide rail is often deformed or the bracket is not firmly fixed, which affects the safety and comfort of the elevator. Elevator guide rails are usually made of high-strength cold-drawn T-shaped steel or hot-rolled steel. This type of guide rail has good straightness and strength, but due to its long length (usually more than 2 meters), it is easily affected by external forces during transportation and stacking, resulting in bending or twisting. If a special fixing fixture is not used during transportation or it is not placed horizontally during storage, the guide rail is easily affected by gravity. The deformation of the guide rail section will also be caused by unreasonable stacking or excessive extrusion during transportation, thereby affecting the verticality and parallelism of subsequent installation. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a shaft construction method based on cloud-based intelligent construction, which is used to solve the problem that the existing shaft elevator guide rails are prone to deformation and difficult to detect during transportation. When deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered, which not only improves the guide rail quality detection accuracy, but also greatly improves the construction efficiency and safety issues, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A shaft construction method based on cloud-based intelligent construction includes the following steps:

[0006] Step 1: Scan the straightness of the guide rail before and after transportation along the length direction by using a laser displacement sensor, set a fixed distance to collect information on the guide rail, obtain the guide rail point cloud data, and obtain the guide rail image data before and after transportation;

[0007] Step 2, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image after transportation as the second guide rail image data, and calculating the guide rail image damage coefficient according to the first guide rail image data and the second guide rail image data;

[0008] Step 3, extracting the guide rail point cloud data before transportation as the first guide rail point cloud data and the guide rail point cloud data after transportation as the second guide rail point cloud data, constructing a multidimensional guide rail damage model based on the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, and analyzing whether the guide rail is deformed;

[0009] Step 4: When deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered.

[0010] As a further solution of the present invention, in step 2, the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data, and the specific steps are:

[0011] Step 21, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image after transportation as the second guide rail image data;

[0012] Step 22, constructing a guide rail image damage coefficient calculation model according to the first guide rail image data and the second guide rail image data, and calculating the guide rail image damage coefficient. The guide rail image damage coefficient calculation formula is:

[0013]

[0014] Where: z is the guide rail image damage coefficient, R is the number of first guide rail image data, T de is the first guide rail image data at position d, T dr is the rth second guide rail image data at position d, T max is the maximum value in the second track image data, T min is the minimum value in the second guide rail image data.

[0015] As a further solution of the present invention, step 3 extracts the guide rail point cloud data before transportation as the first guide rail point cloud data and the guide rail point cloud data after completion as the second guide rail point cloud data, and constructs a multidimensional guide rail damage model based on the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data to analyze whether the guide rail is deformed. The specific steps are:

[0016] Step 31, extract the point cloud data of the guide rail before transportation as the first guide rail point cloud data and the point cloud data of the guide rail after transportation as the second guide rail point cloud data, and use the iterative closest point algorithm to align the first guide rail point cloud data and the second guide rail point cloud data to ensure that the data collected at different time points are in the same coordinate system; the first guide rail point cloud data is P i =(X i , Y i , Z i ), where X iY is the length direction coordinate of the guide rail before transportation, i Z is the horizontal position coordinate of the guide rail before transportation, i is the vertical position coordinate of the guide rail before transportation; the point cloud data of the second guide rail is P i =(X j , Y j , Z j ), where X j Y is the position coordinate of the guide rail in the length direction after completion. j Z is the horizontal position coordinate of the guide rail after completion. j is the vertical position coordinate of the guide rail after completion;

[0017] Step 32: slice the first rail point cloud data and the second rail point cloud data at equal intervals along the length direction of the rail, and set the slice interval to d s Each slice contains a fixed number of point cloud data, forming a YZ plane perpendicular to the X axis. In each slice, the point cloud data of the guide rail surface is extracted, and the horizontal reference line L is fitted using the least squares method. y : Y=aX+b, where Y is the horizontal position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, X is the length position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction;

[0018] Step 33, calculate the second guide rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The deviation between:

[0019]

[0020] Where: ΔY q is the reference straight line L in the horizontal direction of the j-th second guide rail point cloud data in the q-th slice and the fitting y The amount of deviation between

[0021] Record the second rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The deviation between the second guide point cloud data in each slice and the reference straight line L in the horizontal direction of the fit is extracted. y The maximum deviation between max , according to the second rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The maximum deviation between max Calculate the mean value as the final deviation value in the horizontal direction of the slice;

[0022] Step 34, at the same slice interval d sNext, extract the point cloud data of the guide rail in the vertical direction, that is, on the XZ plane, and use the least squares method to fit the vertical reference line L z :Z=cX+d, where Y is the horizontal position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, Z is the vertical position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, c is the inclination of the guide rail in the vertical direction, and d is the offset of the guide rail in the vertical direction;

[0023] Step 35, calculate the second guide rail point cloud data in each slice and the reference straight line L in the vertical direction of the fitting z The deviation between:

[0024]

[0025] Where: ΔZ q is the reference line L in the vertical direction of the j-th second rail point cloud data in the q-th slice and the fitting z The amount of deviation between

[0026] Record and calculate the second rail point cloud data in each slice and the reference line L in the vertical direction of the fit z The deviation between the second guide point cloud data in each slice and the reference straight line L in the vertical direction of the fitting is extracted. z The maximum deviation between As the final deviation value in the vertical direction of the slice;

[0027] Step 36, respectively extracting the final horizontal deviation value of the slice and the final vertical deviation value of the slice, comparing the final horizontal deviation value of the slice with a preset final horizontal deviation threshold value of the slice, if the final horizontal deviation value of the slice is greater than or equal to the preset final horizontal deviation threshold value of the slice, then deformation of the guide rail is detected; if the final horizontal deviation value of the slice is less than the preset final horizontal deviation threshold value of the slice, then deformation of the guide rail is not detected; comparing the final vertical deviation value of the slice with the preset final vertical deviation threshold value of the slice, if the final vertical deviation value of the slice is greater than or equal to the preset final vertical deviation threshold value of the slice, then deformation of the guide rail is detected; if the final vertical deviation value of the slice is less than the preset final vertical deviation threshold value of the slice, then deformation of the guide rail is not detected.

[0028] As a further solution of the present invention, in step 32, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula for the inclination of the guide rail in the horizontal direction is:

[0029]

[0030] Where n is the number of slices of the first guide rail point cloud data, and m is the number of slices of the second guide rail point cloud data.

[0031] As a further solution of the present invention, in step 35, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula for the inclination of the guide rail in the horizontal direction is:

[0032]

[0033] Where n is the number of slices of the first guide rail point cloud data, and m is the number of slices of the second guide rail point cloud data.

[0034] The technical effects and advantages of the shaft construction method based on cloud-based intelligent construction of the present invention: the present invention obtains the point cloud data of the guide rail before and after transportation through a laser displacement sensor, and obtains the guide rail image data before and after transportation, calculates the guide rail image damage coefficient according to the first guide rail image data and the second guide rail image data, constructs a multidimensional guide rail damage model according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, analyzes whether the guide rail is deformed, and when deformation of the guide rail is detected, marks the guide rail and triggers an alarm reminder. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a flow chart of a shaft construction method based on cloud-based intelligent construction provided by the present invention;

[0036] Figure 2 This is a flow chart of step 2 in a shaft construction method based on cloud-based intelligent construction according to the present invention;

[0037] Figure 3 This is a flow chart of step 3 in a shaft construction method based on cloud-based intelligent construction according to the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Figure 1 The present invention provides a flow chart of a shaft construction method based on cloud-based intelligent construction. Figure 1 As shown, a shaft construction method based on cloud-based intelligent construction includes the following steps:

[0040] Step 1: Scan the straightness of the guide rail before and after transportation along the length direction by using a laser displacement sensor, set a fixed distance to collect information on the guide rail, obtain the guide rail point cloud data, and obtain the guide rail image data before and after transportation;

[0041] Step 2, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image after transportation as the second guide rail image data, and calculating the guide rail image damage coefficient according to the first guide rail image data and the second guide rail image data;

[0042] Step 3, extracting the guide rail point cloud data before transportation as the first guide rail point cloud data and the guide rail point cloud data after transportation as the second guide rail point cloud data, constructing a multidimensional guide rail damage model based on the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, and analyzing whether the guide rail is deformed;

[0043] Step 4: When deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered.

[0044] Specifically, in step 2, the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data, and the specific steps are:

[0045] Step 21, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image after transportation as the second guide rail image data;

[0046] Step 22, constructing a guide rail image damage coefficient calculation model according to the first guide rail image data and the second guide rail image data, and calculating the guide rail image damage coefficient. The guide rail image damage coefficient calculation formula is:

[0047]

[0048] Where: z is the guide rail image damage coefficient, R is the number of first guide rail image data, T de is the first guide rail image data at position d, T dr is the rth second guide rail image data at position d, T max is the maximum value in the second track image data, T min is the minimum value in the second guide rail image data.

[0049] By calculating the damage coefficient of the guide rail image, the surface difference before and after the rail is presented in numerical form, avoiding the subjective judgment of the degree of damage in traditional manual inspection, and improving the objectivity and accuracy of damage assessment. The damage coefficient formula compares the pixel intensity difference of the guide rail image at different positions, which can accurately reflect the minor damage such as scratches and cracks on the guide rail surface, ensuring that even minor damage can be effectively identified; automatically extract key features from the guide rail surface image, analyze the subtle differences between the guide rail before and after transportation, without manual frame-by-frame comparison, greatly reducing labor costs and inspection time, and using batch guide rail image data for damage analysis to achieve real-time calculation and feedback, thereby improving inspection efficiency; the damage coefficient model is combined with point cloud data analysis, which can not only evaluate the straightness deformation of the guide rail, but also comprehensively detect the damage to the guide rail surface, realize all-round monitoring of the internal structure and external surface of the guide rail, ensure construction accuracy, and more sensitively detect potential hidden dangers such as local depression and peeling on the guide rail surface by analyzing the maximum pixel difference, avoiding subsequent equipment operation failures due to minor damage being ignored; the guide rail damage coefficient can The display in the form of charts or heat maps helps technicians to intuitively understand the distribution and severity of guide rail damage, which is convenient for timely repair or replacement of damaged guide rails. The damage coefficient calculation model retains the guide rail image data and analysis records before and after transportation, realizing the traceability of the construction quality throughout the process. Even after the guide rails are installed, the damage data can be traced and analyzed as an important basis for the quality assessment of the construction process. Before the guide rails are installed, the damage and deformation that may occur during transportation can be discovered in time, and the hidden dangers of elevator operation caused by the installation of damaged guide rails can be effectively avoided, ensuring the safety and reliability of elevator shaft construction. Through automatic damage detection and real-time alarm, damaged guide rails can be prevented from entering the installation link, reducing the chance of rework from the source, reducing construction costs and ensuring the project is delivered on schedule. The shaft construction method based on the guide rail image damage coefficient calculation model can realize high-precision quantitative detection of guide rail surface damage, give full play to the advantages of automation and intelligence, reduce the influence of human factors, and improve detection efficiency and accuracy, thereby greatly improving construction quality and safety, and helping to upgrade the intelligent construction of elevator guide rails.

[0050] Specifically, in step 3, the guide rail point cloud data before transportation is extracted as the first guide rail point cloud data and the guide rail point cloud data after completion is extracted as the second guide rail point cloud data. A multidimensional guide rail damage model is constructed based on the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data to analyze whether the guide rail is deformed. The specific steps are as follows:

[0051] Step 31, extract the point cloud data of the guide rail before transportation as the first guide rail point cloud data and the point cloud data of the guide rail after transportation as the second guide rail point cloud data, and use the iterative closest point algorithm to align the first guide rail point cloud data and the second guide rail point cloud data to ensure that the data collected at different time points are in the same coordinate system; the first guide rail point cloud data is Pi =(X i , Y i , Z i ), where X i Y is the length direction coordinate of the guide rail before transportation, i Z is the horizontal position coordinate of the guide rail before transportation, i is the vertical position coordinate of the guide rail before transportation; the point cloud data of the second guide rail is P i =(X j , Y j , Z j ), where X j Y is the position coordinate of the guide rail in the length direction after completion. j Z is the horizontal position coordinate of the guide rail after completion. j The vertical position coordinates of the guide rail after completion;

[0052] Step 32: slice the first rail point cloud data and the second rail point cloud data at equal intervals along the length direction of the rail, and set the slice interval to d s Each slice contains a fixed number of point cloud data, forming a YZ plane perpendicular to the X axis. In each slice, the point cloud data of the guide rail surface is extracted, and the horizontal reference line L is fitted using the least squares method. y : Y=aX+b, where Y is the horizontal position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, X is the length position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction;

[0053] Step 33, calculate the second guide rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The deviation between:

[0054]

[0055] Where: ΔY q is the reference straight line L in the horizontal direction of the j-th second guide rail point cloud data in the q-th slice and the fitting y The amount of deviation between

[0056] Record the second rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The deviation between the second guide point cloud data in each slice and the reference straight line L in the horizontal direction of the fit is extracted. y The maximum deviation between max , according to the second rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The maximum deviation between maxCalculate the mean value as the final deviation value in the horizontal direction of the slice;

[0057] Step 34, at the same slice interval d s Next, extract the point cloud data of the guide rail in the vertical direction, that is, on the XZ plane, and use the least squares method to fit the vertical reference line L z :Z=cX+d, where Y is the horizontal position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, Z is the vertical position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, c is the inclination of the guide rail in the vertical direction, and d is the offset of the guide rail in the vertical direction;

[0058] Step 35, calculate the second guide rail point cloud data in each slice and the reference straight line L in the vertical direction of the fitting z The deviation between:

[0059]

[0060] Where: ΔZ q is the reference line L in the vertical direction of the j-th second rail point cloud data in the q-th slice and the fitting z The amount of deviation between

[0061] Record and calculate the second rail point cloud data in each slice and the reference line L in the vertical direction of the fit z The deviation between the second guide point cloud data in each slice and the reference straight line L in the vertical direction of the fitting is extracted. z The maximum deviation between As the final deviation value in the vertical direction of the slice;

[0062] Step 36, respectively extracting the final horizontal deviation value of the slice and the final vertical deviation value of the slice, comparing the final horizontal deviation value of the slice with a preset final horizontal deviation threshold value of the slice, if the final horizontal deviation value of the slice is greater than or equal to the preset final horizontal deviation threshold value of the slice, then deformation of the guide rail is detected; if the final horizontal deviation value of the slice is less than the preset final horizontal deviation threshold value of the slice, then deformation of the guide rail is not detected; comparing the final vertical deviation value of the slice with the preset final vertical deviation threshold value of the slice, if the final vertical deviation value of the slice is greater than or equal to the preset final vertical deviation threshold value of the slice, then deformation of the guide rail is detected; if the final vertical deviation value of the slice is less than the preset final vertical deviation threshold value of the slice, then deformation of the guide rail is not detected.

[0063] The iterative closest point algorithm is used to accurately align the point cloud data of the rail before and after transportation to ensure that the two measurement data are in the same coordinate system, eliminating the systematic errors caused by equipment jitter or position change during the measurement process, making the deformation analysis more accurate and reliable; the point cloud data of the rail is analyzed in the horizontal direction (Y) and the vertical direction (Z), and the reference lines are fitted respectively to comprehensively judge whether the rail is deformed, ensuring the comprehensiveness and accuracy of deformation detection; the rail is sliced ​​along the length direction, and each slice is independently fitted with the reference lines in the horizontal and vertical directions to ensure accurate analysis of the local area of ​​the rail, and can detect subtle deformation in a small local area, rather than Only rely on global deformation to reduce the risk of missed detection; by calculating the deviation between the point cloud data in each slice and the reference line and extracting the maximum deviation, accurate quantification of rail deformation is achieved, which is convenient for rapid identification of abnormal areas and ensures construction accuracy; the use of automated slicing and point cloud fitting methods can batch analyze multiple slice data in the length direction of the rail, significantly improving detection speed and efficiency, reducing manual intervention, and avoiding the time-consuming and labor-intensive limitations of traditional detection methods; once it is detected that the deviation value of any slice of the rail exceeds the preset threshold, the system immediately triggers an alarm and automatically marks the deformed rail area, prompting technicians to make corrections or replacements in time to avoid construction problems or Safety hazards in elevator operation; based on point cloud data analysis, the guide rail surface image data is introduced to calculate the damage coefficient, realizing a comprehensive assessment from deformation to surface damage, which helps to identify guide rail damage caused by impact, friction or other external factors, and ensures that the quality of the guide rail is fully controlled; by calculating the mean of the deviation of each slice as the final horizontal and vertical deviation value, the interference of a single abnormal value on the overall evaluation is reduced, ensuring the stability and reliability of the guide rail deformation analysis results; before the guide rail is installed, the deformation and damage detection is carried out on the guide rail, and the deformed guide rail is removed in time to prevent unqualified guide rails from entering the installation link, thereby reducing the rework rate from the root and reducing the electrical damage caused by guide rail deformation. The problem of unstable or stuck elevator tracks is solved to ensure the long-term safe operation of the elevator. Point cloud and image detection are performed immediately after the guide rail transportation is completed. It can quickly identify the deformation of the guide rail caused by collision or extrusion during transportation, provide data support for improving transportation methods and strengthening guide rail protection, and reduce transportation losses. Through the dual analysis of point cloud data and image data, comprehensive detection of guide rail deformation and surface damage is achieved, which significantly improves the quality control capability during guide rail transportation. It has the characteristics of high precision, high efficiency and automation, which can effectively reduce the rework rate, ensure construction safety, and help construction companies realize intelligent and digital management, providing strong technical support for shaft construction.

[0064] Specifically, in step 32, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula for the inclination of the guide rail in the horizontal direction is:

[0065]

[0066] Where n is the number of slices of the first guide rail point cloud data, and m is the number of slices of the second guide rail point cloud data.

[0067] Specifically, in step 35, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula for the inclination of the guide rail in the horizontal direction is:

[0068]

[0069] Where n is the number of slices of the first guide rail point cloud data, and m is the number of slices of the second guide rail point cloud data.

[0070] The embodiment of the present invention obtains the guide rail point cloud data before and after transportation through a laser displacement sensor, and obtains the guide rail image data before and after transportation, calculates the guide rail image damage coefficient according to the first guide rail image data and the second guide rail image data, constructs a multidimensional guide rail damage model according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, analyzes whether the guide rail is deformed, and when deformation of the guide rail is detected, marks the guide rail and triggers an alarm reminder, which not only improves the guide rail quality detection accuracy, but also greatly improves the construction efficiency and safety.

[0071] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0072] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A shaft construction method based on cloud-based intelligent construction, characterized in that: The steps include: Step 1: Scan the straightness of the guide rail before and after transportation along the length direction by using a laser displacement sensor, set a fixed distance to collect information on the guide rail, obtain the guide rail point cloud data, and obtain the guide rail image data before and after transportation; Step 2, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image after transportation as the second guide rail image data, and calculating the guide rail image damage coefficient according to the first guide rail image data and the second guide rail image data; Step 3, extracting the guide rail point cloud data before transportation as the first guide rail point cloud data and the guide rail point cloud data after transportation as the second guide rail point cloud data, constructing a multidimensional guide rail damage model based on the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, and analyzing whether the guide rail is deformed; Step 4: When deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered.

2. The shaft construction method based on cloud-based intelligent construction according to claim 1 is characterized in that: In step 2, the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data, and the specific steps are: Step 21, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image after transportation as the second guide rail image data; Step 22, constructing a guide rail image damage coefficient calculation model according to the first guide rail image data and the second guide rail image data, and calculating the guide rail image damage coefficient. The guide rail image damage coefficient calculation formula is: Where: z is the guide rail image damage coefficient, R is the number of first guide rail image data, T de is the first guide rail image data at position d, T dr is the rth second guide rail image data at position d, T max is the maximum value in the second track image data, T min is the minimum value in the second guide rail image data.

3. The shaft construction method based on cloud-based intelligent construction according to claim 1 is characterized in that: In step 3, the point cloud data of the guide rail before transportation is extracted as the first guide rail point cloud data and the point cloud data of the guide rail after transportation is extracted as the second guide rail point cloud data. A multi-dimensional guide rail damage model is constructed based on the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data to analyze whether the guide rail is deformed. The specific steps are as follows: Step 31, extract the point cloud data of the guide rail before transportation as the first guide rail point cloud data and the point cloud data of the guide rail after transportation as the second guide rail point cloud data, and use the iterative closest point algorithm to align the first guide rail point cloud data and the second guide rail point cloud data to ensure that the data collected at different time points are in the same coordinate system; the first guide rail point cloud data is P i =(X i ,Y i ,Z i ), where X i Y is the length direction coordinate of the guide rail before transportation, i Z is the horizontal position coordinate of the guide rail before transportation, i is the vertical position coordinate of the guide rail before transportation; the point cloud data of the second guide rail is P i =(X j ,Y j ,Z j ), where X j Y is the position coordinate of the guide rail in the length direction after completion. j Z is the horizontal position coordinate of the guide rail after completion. j The vertical position coordinates of the guide rail after completion; Step 32: slice the first rail point cloud data and the second rail point cloud data at equal intervals along the length direction of the rail, and set the slice interval to d s Each slice contains a fixed number of point cloud data, forming a YZ plane perpendicular to the X axis. In each slice, the point cloud data of the guide rail surface is extracted, and the horizontal reference line L is fitted using the least squares method. y :Y=aX+b, where Y is the horizontal position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, X is the length position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; Step 33, calculate the second guide rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The deviation between: Where: ΔY q is the reference straight line L in the horizontal direction of the j-th second guide rail point cloud data in the q-th slice and the fitting y The amount of deviation between Record the second rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The deviation between the second guide point cloud data in each slice and the reference straight line L in the horizontal direction of the fit is extracted. y The maximum deviation between max , according to the second rail point cloud data in each slice and the reference straight line L in the horizontal direction of the fit y The maximum deviation between max Calculate the mean value as the final deviation value in the horizontal direction of the slice; Step 34, at the same slice interval d s Next, extract the point cloud data of the guide rail in the vertical direction, that is, on the XZ plane, and use the least squares method to fit the vertical reference line L z :Z=cX+d, where Y is the horizontal position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, Z is the vertical position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, c is the inclination of the guide rail in the vertical direction, and d is the offset of the guide rail in the vertical direction; Step 35, calculate the second guide rail point cloud data in each slice and the reference straight line L in the vertical direction of the fitting z The deviation between: Where: ΔZ q is the reference line L in the vertical direction of the j-th second guide rail point cloud data in the q-th slice and the fitting z The amount of deviation between Record and calculate the second rail point cloud data in each slice and the reference line L in the vertical direction of the fit z The deviation between the second guide point cloud data in each slice and the reference straight line L in the vertical direction of the fitting is extracted. z The maximum deviation between As the final deviation value in the vertical direction of the slice; Step 36, respectively extracting the final horizontal deviation value of the slice and the final vertical deviation value of the slice, comparing the final horizontal deviation value of the slice with a preset final horizontal deviation threshold value of the slice, if the final horizontal deviation value of the slice is greater than or equal to the preset final horizontal deviation threshold value of the slice, then deformation of the guide rail is detected; if the final horizontal deviation value of the slice is less than the preset final horizontal deviation threshold value of the slice, then deformation of the guide rail is not detected; comparing the final vertical deviation value of the slice with the preset final vertical deviation threshold value of the slice, if the final vertical deviation value of the slice is greater than or equal to the preset final vertical deviation threshold value of the slice, then deformation of the guide rail is detected; if the final vertical deviation value of the slice is less than the preset final vertical deviation threshold value of the slice, then deformation of the guide rail is not detected.

4. The shaft construction method based on cloud-based intelligent construction according to claim 3 is characterized in that: In step 32, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula for the inclination of the guide rail in the horizontal direction is: Where n is the number of slices of the first guide rail point cloud data, and m is the number of slices of the second guide rail point cloud data.

5. The shaft construction method based on cloud-based intelligent construction according to claim 3 is characterized in that: In step 35, a is the inclination of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula for the inclination of the guide rail in the horizontal direction is: Where n is the number of slices of the first guide rail point cloud data, and m is the number of slices of the second guide rail point cloud data.

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