A hoistway type construction method based on cloud intelligent construction

Abstract

The present application relates to the technical field of intelligent shaft elevator construction detection, and specifically discloses a shaft construction method based on cloud intelligent construction, which is used to solve the problem that deformation is difficult to detect in the transportation process of the existing shaft elevator guide rail; the present application obtains guide rail point cloud data before and after transportation through a laser displacement sensor, and obtains guide rail image data before transportation and guide rail image data after transportation; the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data; a multi-dimensional guide rail damage model is constructed according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data; whether the guide rail is deformed is analyzed; when the deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent shaft elevator construction detection, and more particularly to a shaft type construction method based on cloud intelligent construction. BACKGROUND

[0002] In the construction process of the shaft type 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 the counterweight, avoiding deviation or shaking during operation. However, in the actual construction and use process, the guide rail and the bracket are often improperly transported, stacked and installed, resulting in deformation of the guide rail or unstable fixing of the bracket, which affects the safety and comfort of the elevator operation. The elevator guide rail is 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 special fixing clamps are not used during transportation or the guide rail is not placed horizontally during storage, the guide rail will be affected by gravity and easily deformed. In addition, if the guide rail is not reasonably stacked or excessively squeezed during transportation, the cross section of the guide rail will be twisted, affecting the verticality and parallelism of subsequent installation. In order to solve the above problems, the present application provides a technical solution. SUMMARY

[0003] In order to overcome the above-mentioned defects of the prior art, the present application provides a shaft type construction method based on cloud intelligent construction, which solves the problem of deformation of the guide rail of the existing shaft type elevator during transportation. When deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered to remind the user. This not only improves the accuracy of guide rail quality detection, but also greatly improves the construction efficiency and safety, thereby solving the problems raised in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solution:

[0005] A shaft type construction method based on cloud intelligent construction, comprising the following steps:

[0006] Step 1: Straightness scanning of the guide rail before and after transportation is performed along the length direction by a laser displacement sensor. A fixed distance is set to collect information of the guide rail, obtain point cloud data of the guide rail, and obtain image data of the guide rail before and after transportation.

[0007] Step 2: The image data of the guide rail before transportation is extracted as the first guide rail image data, and the image data of the guide rail after transportation is extracted as the second guide rail image data. The guide rail image damage coefficient is calculated based on the first guide rail image data and the second guide rail image data.

[0008] Step 3, extract 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, construct 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, and analyze whether the guide rail has deformed;

[0009] Step 4, when it is detected that the guide rail has deformed, the guide rail is marked and an alarm is triggered.

[0010] As a further scheme of the present application, in step 2, the calculation of 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 as follows:

[0011] Step 21, extract 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, construct a guide rail image damage coefficient calculation model according to the first guide rail image data and the second guide rail image data, and calculate the guide rail image damage coefficient, the calculation formula of the guide rail image damage coefficient is:

[0013]

[0014] In the formula, σ z is the guide rail image damage coefficient, R is the number of the first guide rail image data, T de is the e-th first guide rail image data at position d, T dr is the r-th second guide rail image data at position d, T max is the maximum value in the second guide rail image data, T min is the minimum value in the second guide rail image data.

[0015] As a further scheme of the present application, in step 3, extract 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, construct 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, and analyze whether the guide rail has deformed, and the specific steps are as follows:

[0016] Step 31, extract 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, align the first guide rail point cloud data and the second guide rail point cloud data by using the iterative closest point algorithm, and ensure that the data collected at different time points are located in the same coordinate system; the first guide rail point cloud data is P i =(X i , Y i , Z i ), wherein X iY represents the position coordinates of the guide rail along its length before transportation. i Z represents the horizontal position coordinates of the guide rail before transportation. i The first is the vertical position coordinate of the guide rail before transportation; the second guide rail point cloud data is P. i =(X j Y j Z j ), where X j Y represents the position coordinates along the length of the completed guide rail. j Z represents the horizontal position coordinates of the completed guide rail. j These are the vertical position coordinates of the completed guide rail;

[0017] Step 32: Slice the point cloud data of the first guide rail and the point cloud data of the second guide rail at equal intervals along the length of the guide rail, with the slice interval set to d. s Each slice contains a fixed number of point cloud data, forming a YZ plane perpendicular to the X-axis. Within each slice, 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 tilt 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 and the fitted horizontal reference line L in each slice. y Deviation between:

[0019]

[0020] In the formula: ΔY q For the j-th second guide rail point cloud data in the q-th slice, the reference line L in the fitted horizontal direction is... y The deviation between;

[0021] Record the point cloud data of the second guide rail in each slice and the fitted horizontal reference line L. y The deviation between them is calculated by extracting the second guide rail point cloud data from each slice and fitting the reference straight line L in the horizontal direction. y The maximum deviation ΔY between max Based on the second guide rail point cloud data in each slice and the fitted horizontal reference line L y The maximum deviation ΔY between max Calculate the mean value as the final horizontal deviation of the slice;

[0022] Step 34, at the same slice interval d sNext, the point cloud data of the guide rail in the vertical direction, i.e. the X-Z plane, is extracted, and a vertical direction reference straight line L is fitted using the least square method z : Z = cX + d, wherein Y is the horizontal direction 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 direction 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 degree of the guide rail in the vertical direction, and d is the offset of the guide rail in the vertical direction;

[0023] Step 35, the deviation between the second guide rail point cloud data in each slice and the fitted vertical direction reference straight line L z is calculated:

[0024]

[0025] In the formula, ΔZ q is the deviation between the jth second guide rail point cloud data in the qth slice and the fitted vertical direction reference straight line L z

[0026] The deviation between the second guide rail point cloud data in each slice and the fitted vertical direction reference straight line L z is recorded, and the maximum deviation between the second guide rail point cloud data in each slice and the fitted vertical direction reference straight line L z is extracted as the vertical direction final deviation value of the slice;

[0027] Step 36, the horizontal direction final deviation value of the slice and the vertical direction final deviation value of the slice are extracted respectively, the horizontal direction final deviation value of the slice is compared with the preset horizontal direction final deviation threshold value of the slice, if the horizontal direction final deviation value of the slice is greater than or equal to the preset horizontal direction final deviation threshold value of the slice, it is detected that the guide rail is deformed; if the horizontal direction final deviation value of the slice is less than the preset horizontal direction final deviation threshold value of the slice, it is not detected that the guide rail is deformed; the vertical direction final deviation value of the slice is compared with the preset vertical direction final deviation threshold value of the slice, if the vertical direction final deviation value of the slice is greater than or equal to the preset vertical direction final deviation threshold value of the slice, it is detected that the guide rail is deformed; if the vertical direction final deviation value of the slice is less than the preset vertical direction final deviation threshold value of the slice, it is not detected that the guide rail is deformed.

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

[0029]

[0030] ​​In the formula, n is the slice number of the first guide rail point cloud data, and m is the slice number of the second guide rail point cloud data.

[0031] As a further scheme of the present application, in step 35, a is the inclination degree of the guide rail in the horizontal direction, and b is the offset amount of the guide rail in the horizontal direction; the calculation formula of the inclination degree of the guide rail in the horizontal direction is:

[0032]

[0033] In the formula, n is the slice number of the first guide rail point cloud data, and m is the slice number of the second guide rail point cloud data.

[0034] The technical effect and advantages of the shaft type construction method based on cloud intelligent construction of the present application are as follows: the laser displacement sensor is used to obtain the guide rail point cloud data before and after transportation, and the guide rail image data before transportation and the guide rail image data after transportation are obtained; the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data; the multi-dimensional guide rail damage model is constructed according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data; whether the guide rail is deformed is analyzed; when the deformation of the guide rail is detected, the guide rail is marked and an alarm is triggered. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The flowchart of the shaft type construction method based on cloud intelligent construction of the present application is provided.

[0036] Figure 2 The flowchart of step 2 in the shaft type construction method based on cloud intelligent construction of the present application is provided.

[0037] Figure 3 The flowchart of step 3 in the shaft type construction method based on cloud intelligent construction of the present application is provided. DETAILED DESCRIPTION

[0038] The technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described technical solutions are only a part of the present application, but not all. Based on the technical solutions in the present application, all other technical solutions obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0039] Figure 1 The flowchart of the shaft type construction method based on cloud intelligent construction of the present application is provided. As shown in Figure 1 The shaft type construction method based on cloud intelligent construction includes the following steps:

[0040] Step 1, straightness scanning of the guide rail before and after transportation along the length direction by a laser displacement sensor, setting a fixed distance to collect information of the guide rail, obtaining guide rail point cloud data, and obtaining guide rail image data before transportation and guide rail image data 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 data 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, 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 to analyze whether the guide rail is deformed;

[0043] Step 4, when the guide rail is detected to be deformed, marking the guide rail and triggering an alarm to remind.

[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 as follows:

[0045] Step 21, extracting the guide rail image data before transportation as the first guide rail image data and the guide rail image data 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 calculation formula of the guide rail image damage coefficient is:

[0047]

[0048] In the formula, σ z is the guide rail image damage coefficient, R is the number of the first guide rail image data, T de is the e-th first guide rail image data at position d, T dr is the r-th second guide rail image data at position d, T max is the maximum value in the second guide rail image data, T min is the minimum value in the second guide rail image data.

[0049] By calculating the guide rail image damage coefficient, the surface difference before and after transportation is presented in numerical form, avoiding the subjective judgment of damage degree in traditional manual detection, and improving the objectivity and accuracy of damage evaluation. The damage coefficient formula compares the pixel intensity difference of the guide rail image at different positions, which can accurately reflect the small damage such as scratches and cracks on the guide rail surface, and ensure that even slight damage can be effectively identified. The key features are automatically extracted from the guide rail surface image, and the subtle differences of the guide rail before and after transportation are analyzed, without manual frame-by-frame comparison, greatly reducing the labor cost and detection time. Using batch guide rail image data for damage analysis, real-time calculation and feedback are realized, and the detection efficiency is improved. 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 surface damage of the guide rail, realize the all-round monitoring of the internal structure and external surface of the guide rail, ensure the construction precision, and through the analysis of the maximum pixel difference, the potential hidden dangers such as local depression and peeling on the guide rail surface can be detected more sensitively, avoiding the subsequent equipment operation failure caused by neglecting the slight damage. The guide rail damage coefficient can be displayed in the form of chart or heat map, helping technicians to intuitively understand the damage distribution and severity of the guide rail, and facilitating 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 whole process traceability of construction quality. Even after the installation of the guide rail is completed, damage data can be traced and analyzed, serving as an important basis for quality evaluation of the construction process. Before the installation of the guide rail, the damage and deformation that may occur during transportation can be found in time, effectively avoiding the potential elevator operation risks caused by damaged guide rails after installation, ensuring the safety and reliability of the elevator shaft construction. Through automatic damage detection and real-time alarm, the damaged guide rail is prevented from entering the installation link, reducing the probability of rework from the source, reducing construction cost and ensuring the project to be 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, fully exert the advantages of automation and intelligence, reduce the influence of human factors, improve the detection efficiency and accuracy, and thus greatly improve the construction quality and safety, helping the intelligent upgrading of elevator guide rail construction.

[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 completed guide rail point cloud data is extracted as the second guide rail point cloud data. A multi-dimensional guide rail damage model is constructed according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, and whether the guide rail has been deformed is analyzed. The specific steps are as follows:

[0051] In step 31, the guide rail point cloud data before transportation is extracted as the first guide rail point cloud data, and the completed guide rail point cloud data is extracted as the second guide rail point cloud data. The iterative closest point algorithm is used to align the first guide rail point cloud data and the second guide rail point cloud data, ensuring that the data collected at different time points are located in the same coordinate system. The first guide rail point cloud data is Pi =(X i , Y i , Z i ), wherein X i is the length direction position coordinate of the guide rail before transportation, Y i is the horizontal direction position coordinate of the guide rail before transportation, and Z i is the vertical direction position coordinate of the guide rail before transportation; the second guide rail point cloud data is P i =(X j , Y j , Z j ), wherein X j is the length direction position coordinate of the completed guide rail, Y j is the horizontal direction position coordinate of the completed guide rail, and Z j is the vertical direction position coordinate of the completed guide rail;

[0052] Step 32, equally slice the first guide rail point cloud data and the second guide rail point cloud data along the length direction of the guide rail, and the slice interval is set as d s , each slice contains a fixed number of point cloud data, forming a Y-Z plane perpendicular to the X axis, and in each slice, the guide rail surface point cloud data is extracted, and a horizontal reference straight line L y : Y = aX + b is fitted using the least square method, wherein Y is the horizontal direction position coordinate in the first guide rail point cloud data and the second guide rail point cloud data, X is the length direction position coordinate in the first guide rail point cloud data and the second guide rail point cloud data, a is the inclination degree of the guide rail in the horizontal direction, and b is the offset amount of the guide rail in the horizontal direction;

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

[0054]

[0055] In the formula: ΔY q is the deviation amount between the jth second guide rail point cloud data in the qth slice and the fitted horizontal reference straight line L y ;

[0056] Record the deviation amount between the second guide rail point cloud data and the fitted horizontal reference straight line L y in each slice, extract the maximum deviation amount ΔY max between the second guide rail point cloud data and the fitted horizontal reference straight line L y in each slice, and according to the maximum deviation amount ΔY max between the second guide rail point cloud data and the fitted horizontal reference straight line L s in each slice,The average value is calculated as the horizontal final deviation value of the slice;

[0057] Step 34, the same slice interval d s Next, the point cloud data of the guide rail in the vertical direction, i.e. the X-Z plane, is extracted, and a vertical reference straight 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, the deviation between the second guide rail point cloud data in each slice and the fitted vertical reference straight line L z is calculated:

[0059]

[0060] In the formula: ΔZ q is the deviation between the jth second guide rail point cloud data in the qth slice and the fitted vertical reference straight line L z ;

[0061] The deviation between the second guide rail point cloud data in each slice and the fitted vertical reference straight line L z is recorded, and the maximum deviation between the second guide rail point cloud data in each slice and the fitted vertical reference straight line L z is extracted as the vertical final deviation value of the slice;

[0062] Step 36, the horizontal final deviation value of the slice and the vertical final deviation value of the slice are extracted respectively, the horizontal final deviation value of the slice is compared with the preset horizontal final deviation threshold value of the slice, if the horizontal final deviation value of the slice is greater than or equal to the preset horizontal final deviation threshold value of the slice, it is detected that the guide rail is deformed; if the horizontal final deviation value of the slice is less than the preset horizontal final deviation threshold value of the slice, it is detected that the guide rail is not deformed; the vertical final deviation value of the slice is compared with the preset vertical final deviation threshold value of the slice, if the vertical final deviation value of the slice is greater than or equal to the preset vertical final deviation threshold value of the slice, it is detected that the guide rail is deformed; if the vertical final deviation value of the slice is less than the preset vertical final deviation threshold value of the slice, it is detected that the guide rail is not deformed.

[0063] The iterative closest point algorithm is used to accurately align the guide rail point cloud data before and after transportation, to ensure that the two measurement data are located in the same coordinate system, to eliminate the systematic error caused by equipment shaking or position change during measurement, to make the deformation analysis more accurate and reliable; the guide rail point cloud data is analyzed in the horizontal direction (Y) and the vertical direction (Z), the reference straight lines are fitted respectively, and the guide rail deformation is comprehensively judged to ensure the comprehensiveness and accuracy of the deformation detection; slicing along the length direction of the guide rail, independently fitting the reference straight lines of the horizontal direction and the vertical direction for each slice to ensure accurate analysis of the local area of the guide rail and to find the subtle deformation in a small local range instead of relying on global deformation, reducing the risk of missed detection; by calculating the deviation of the point cloud data in each slice from the reference straight line and extracting the maximum deviation, the guide rail deformation is accurately quantified, which is convenient for quickly identifying abnormal areas and ensuring construction accuracy; using the automatic slicing and point cloud fitting method, multiple slice data in the length direction of the guide rail can be analyzed in batches, which significantly improves the detection speed and efficiency, reduces manual intervention, and avoids the time-consuming and labor-intensive limitations of traditional detection methods; once the deviation value of any slice of the guide rail exceeds the preset threshold, the system immediately triggers an alarm and automatically marks the deformed guide rail area, prompting the technician to correct or replace it in time to avoid construction problems or safety hazards caused by guide rail deformation; on the basis of point cloud data analysis, the damage coefficient is calculated by introducing the guide rail surface image data to realize comprehensive evaluation from deformation to surface damage, which helps to identify guide rail damage caused by impact, friction or other external factors and ensures the overall control of guide rail quality; by calculating the mean value of each slice deviation as the final horizontal and vertical deviation, the interference of single abnormal value on overall evaluation is reduced, and the stability and reliability of guide rail deformation analysis results are ensured; the guide rail is deformed and damaged before installation to timely eliminate deformed guide rails and avoid unqualified guide rails entering the installation link, which reduces the rework rate from the root cause, reduces the elevator track instability or jamming problem caused by guide rail deformation, and ensures the long-term safe operation of the elevator; immediately after the guide rail transportation is completed, point cloud and image detection can quickly identify the guide rail deformation caused by collision or extrusion during transportation, provide data support for improving transportation methods and strengthening guide rail protection, and reduce transportation loss; through double analysis of point cloud data and image data, comprehensive detection of guide rail deformation and surface damage is realized, which significantly improves the quality control ability of guide rail transportation process, has the characteristics of high precision, high efficiency and automation, can effectively reduce the rework rate, ensure construction safety, and help construction enterprises realize intelligent and digital management, providing strong technical support for shaft type construction.

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

[0065]

[0066] In the formula, 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 degree of the guide rail in the horizontal direction, and b is the offset of the guide rail in the horizontal direction; the calculation formula of the inclination degree of the guide rail in the horizontal direction is as follows:

[0068]

[0069] In the formula, 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] In the embodiment of the application, the guide rail point cloud data before and after transportation is acquired by a laser displacement sensor, the guide rail image data before transportation and the guide rail image data after transportation are acquired, the guide rail image damage coefficient is calculated according to the first guide rail image data and the second guide rail image data, the multi-dimensional guide rail damage model is constructed according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, the deformation of the guide rail is analyzed, when the deformation of the guide rail is detected, the guide rail is marked and the alarm is triggered, which not only improves the guide rail quality detection precision, but also greatly improves the construction efficiency and safety.

[0071] The above merely describes the specific implementation of the present application, but the protection scope of the present application is not limited to this, any skilled person in the art can easily think of changes or replacements within the technical range 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 subject to the protection scope of the claims.

[0072] Finally, the above only describes the preferred scheme of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A hoistway type construction method based on cloud intelligent construction, characterized in that, The method comprises the following steps: Step 1, linear scanning of the guide rail before and after transportation along the length direction by a laser displacement sensor, setting a fixed distance to collect information of the guide rail, obtaining guide rail point cloud data, and obtaining guide rail image data before transportation and guide rail image data after transportation; Step 2, extracting the guide rail image data before transportation as first guide rail image data and the guide rail image data after transportation as second guide rail image data, calculating a guide rail image damage coefficient according to the first guide rail image data and the second guide rail image data, and the specific steps are as follows: 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, and the calculation formula of the guide rail image damage coefficient is: ； In the formula: is a rail image damage coefficient, is a first rail image data number, is the e-th first rail image data at position d, is the r-th second rail image data at position d, is a maximum value in the second rail image data, is a minimum value in the second rail image data; Step 3, extracting the guide rail point cloud data before transportation as first guide rail point cloud data and the guide rail point cloud data after transportation as second guide rail point cloud data, 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, and analyzing whether the guide rail is deformed; Step 4, when the guide rail is detected to be deformed, marking the guide rail and triggering an alarm to remind.

2. The cloud-based intelligent construction hoistway method according to claim 1, wherein, In step 3, the guide rail point cloud data before transportation is extracted as first guide rail point cloud data and the guide rail point cloud data after transportation is extracted as second guide rail point cloud data, a multi-dimensional guide rail damage model is constructed according to the guide rail image damage coefficient, the first guide rail point cloud data and the second guide rail point cloud data, and whether the guide rail is deformed is analyzed, and the specific steps are as follows: Step 31, extract the guide rail point cloud data before transportation as the first guide rail point cloud data and the completed guide rail point cloud data as the second guide rail point cloud data, align the first guide rail point cloud data and the second guide rail point cloud data by using the iterative closest point algorithm, and ensure that the data collected at different time points are located in the same coordinate system; the first guide rail point cloud data is wherein, is the length direction position coordinate of the guide rail before transportation, is the horizontal direction position coordinate of the guide rail before transportation, is the vertical direction position coordinate of the guide rail before transportation; the second guide rail point cloud data is wherein, is the length direction position coordinate of the guide rail after completion, is the horizontal direction position coordinate of the guide rail after completion, is the vertical direction position coordinate of the guide rail after completion. Step 32, slice the first guide rail point cloud data and the second guide rail point cloud data at equal intervals along the length direction of the guide rail, and the slice interval is set to Each slice contains a fixed number of point cloud data, forming a Y-Z plane perpendicular to the X axis, and in each slice, the guide rail surface point cloud data is extracted, and a reference straight line in the horizontal direction is fitted using the least square method Wherein, 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, is the length direction position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, is the inclination degree 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 deviation between the second guide rail point cloud data in each slice and the reference straight line fitted in the horizontal direction: Step 34, calculate the deviation between the first guide rail point cloud data in each slice and the reference straight line fitted in the horizontal direction: ； In the formula: is the deviation amount between the jth second guide rail point cloud data in the qth slice and the reference straight line fitted in the horizontal direction ; record the deviation amount between the second guide rail point cloud data in each slice and the reference straight line fitted in the horizontal direction extract the maximum deviation amount between the second guide rail point cloud data in each slice and the reference straight line fitted in the horizontal direction , calculate the mean value as the final horizontal direction deviation value of the slice according to the maximum deviation amount between the second guide rail point cloud data in each slice and the reference straight line fitted in the horizontal direction calculate the mean value as the final horizontal direction deviation value of the slice according to the maximum deviation amount between the second guide rail point cloud data in each slice and the reference straight line fitted in the horizontal direction​​ Step 34, at the same slice interval Next, the point cloud data of the guide rail in the vertical direction, i.e., the X-Z plane, is extracted, and a vertical direction reference straight line is fitted using a least square method wherein, is the horizontal direction position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, is the vertical direction position coordinate of the guide rail in the first guide rail point cloud data and the second guide rail point cloud data, is the inclination degree 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 deviation between the second guide rail point cloud data in each slice and the reference straight line fitted in the vertical direction: between the second guide rail point cloud data in each slice and the reference straight line fitted in the vertical direction: ； In the formula: is the deviation amount between the jth second guide rail point cloud data in the qth slice and the reference straight line fitted in the vertical direction; is the deviation amount between the jth second guide rail point cloud data in the qth slice and the reference straight line fitted in the vertical direction; record the deviation amount between the second guide rail point cloud data in each slice and the reference straight line fitted in the vertical direction extract the maximum deviation amount between the second guide rail point cloud data in each slice and the reference straight line fitted in the vertical direction as the final deviation value of the vertical direction of the slice​ Step 36, extracting the horizontal direction final deviation value of the slice and the vertical direction final deviation value of the slice respectively, comparing the horizontal direction final deviation value of the slice with the preset horizontal direction final deviation threshold value of the slice, if the horizontal direction final deviation value of the slice is greater than or equal to the preset horizontal direction final deviation threshold value of the slice, it is detected that the guide rail is deformed; if the horizontal direction final deviation value of the slice is less than the preset horizontal direction final deviation threshold value of the slice, it is detected that the guide rail is not deformed; comparing the vertical direction final deviation value of the slice with the preset vertical direction final deviation threshold value of the slice, if the vertical direction final deviation value of the slice is greater than or equal to the preset vertical direction final deviation threshold value of the slice, it is detected that the guide rail is deformed; if the vertical direction final deviation value of the slice is less than the preset vertical direction final deviation threshold value of the slice, it is detected that the guide rail is not deformed. 3.The cloud-based intelligent construction hoistway method of claim 2, wherein, In step 32, The inclination degree of the guide rail in the horizontal direction is calculated by the formula: ； ； In the formula: is the number of slices of the first guide rail point cloud data, is the number of slices of the second guide rail point cloud data. 4.The cloud-based intelligent construction method of claim 2, wherein In step 35, The inclination degree of the guide rail in the horizontal direction is calculated by the formula: ； ； In the formula, is the number of slices of the first guide rail point cloud data, is the number of slices of the second guide rail point cloud data.

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