Crane operation safety on-line monitoring system

Abstract

The invention relates to the technical field of engineering, and provides a crane operation safety online monitoring system which comprises the following steps: acquiring point cloud image data; acquiring a ground rugged index according to the point cloud image data; acquiring a wheel support force uniformity index according to the ground rugged index; obtaining the boom span suitability of the crane according to the wheel supporting force uniformity index; the optimal extension angle and the optimal extension arm length are obtained according to the boom extension suitability of the crane; according to the optimal stretching angle and the optimal stretching arm length, obtaining a high-altitude operation danger index; key point operation stability is obtained according to the high-altitude operation danger index; and obtaining a predicted value of the key point operation stability based on the key point operation stability by using an autoregressive moving average model, and carrying out online alarming on the overhead working personnel based on the predicted value of the key point operation stability. On the basis of the prediction result of the key point operation stability, an alarm is given to the high-altitude operation personnel on line, and the accuracy and efficiency of on-line safety monitoring are improved.

Description

Technical Field

[0001] This application relates to the field of engineering technology, and particularly to an online safety monitoring system for crane operations. Background Art

[0002] With the rapid development of infrastructure construction, the construction sites of engineering operations tend to be large-scale, complex, and modular. Cranes are the main key equipment for material lifting. During the crane engineering operation process, the lifting height and tonnage are continuously increasing, and the overall safety operation management difficulty is also continuously increasing, making it extremely easy to occur major safety accidents. Therefore, in order to ensure the safety and reliability of cranes during the hoisting operation process, it is necessary to dynamically monitor the crane engineering operation status.

[0003] In the prior art, various sensors such as position sensors and pressure sensors and mechanical models are mostly used to analyze the operation range of cranes, but this method is slow and inefficient, and is more used for simulation analysis with little effect in actual applications. With the further development of optical remote sensing technology, active sensors represented by lidar and depth cameras can be very conveniently installed on the boom to quickly obtain the working state of the crane. Summary of the Invention

[0004] This application provides an online safety monitoring system for crane operations to solve the problem that the monitoring accuracy rate and the recognition efficiency cannot be coordinated and unified. The specific technical solutions adopted are as follows: An online safety monitoring system for crane operations according to an embodiment of this application, the system includes the following modules: A working data acquisition module, which acquires denoised point cloud image data; An optimal working state acquisition module, which acquires each crane working window according to the point cloud image data, and acquires the ground roughness index of the important point cloud data point set of each sampling of each crane working window according to each crane working window; acquires the wheel support force uniformity index of each crane working window according to the ground roughness index of all samplings of each crane working window; acquires the boom extension suitability of each crane working window according to the wheel support force uniformity index of each crane working window, acquires the optimal working area of the crane according to the boom extension suitability of each crane working window; acquires the optimal extension angle and the optimal extension length of the crane boom according to the optimal working area of the crane; The key-point operation analysis module obtains the actual extension angle and the actual extension arm length according to the actual working area of the crane; obtains the high-altitude operation risk index of the crane during actual operation according to the optimal extension angle, the optimal extension arm length, the actual extension angle and the actual extension arm length of the crane's robotic arm; obtains the key-point operation stability of each actual working moment of the crane according to the high-altitude operation risk index of the crane during actual operation. The warning module uses the autoregressive moving average model to obtain the predicted value of the key-point operation stability based on the key-point operation stability, and alarms the high-altitude operators online based on the predicted value of the key-point operation stability.

[0005] Preferably, the method for obtaining the ground ruggedness index of the important point cloud data point set for each sampling of each crane working window according to the point cloud image data is as follows: Set a sliding window equal in size to the rectangular working area of the crane, and slide the sliding window in the point cloud image in the order from left to right and from top to bottom without overlap. Take the result of each slide of the sliding window as a crane working window. For each crane working window, sample the first preset parameter times in the corresponding area of the crane working window according to the random sampling principle, and sample the second preset parameter of point cloud data points each time. Take the set composed of all the point cloud data points extracted each time as the important point cloud data point set for each extraction. For any two point cloud data points in the important point cloud data point set extracted each time, take the reciprocal of the sum of 1 and the metric distance between the coordinates of the two point cloud data points projected onto the horizontal ground in the vertical direction as the wheel support force influence weight between the two point cloud data points. Calculate the absolute value of the difference in height between the two point cloud data points from the horizontal ground, and take the mean of the quadratic cumulative sum of the product of the absolute value and the wheel support force influence weight on the important point cloud data point set as the ground ruggedness index of the important point cloud data point set.

[0006] Preferably, the method for obtaining the wheel support force uniformity index of each crane working window according to the ground ruggedness index of the important point cloud data point sets for all samplings of each crane working window is as follows: For each crane working window, take the sequence composed of the ground ruggedness indexes of the important point cloud data point sets for all samplings of the crane working window in the order of sampling as the ruggedness index sequence of the crane working window. Take the difference between the value of each data point in the ruggedness index sequence except the first data point and the value of the previous data point as the numerator, and take the maximum value between the value of each data point in the ruggedness index sequence except the first data point and the value of the previous data point as the denominator, and calculate the normalized result of the ratio of the numerator to the denominator; Calculate the absolute value of the difference between the normalized result and 0.5, calculate the reciprocal of the sum of the absolute value and 1, and take the cumulative sum of the reciprocal on the ruggedness index sequence as the wheel support force uniformity index of the crane working window.

[0007] Preferably, the method for obtaining the boom extension suitability of each crane working window according to the wheel support force uniformity index of each crane working window and obtaining the optimal working area of the crane according to the boom extension suitability of each crane working window is as follows: Take the Euclidean distance between the projection points of the inflection point of the crane's robotic arm and the construction target point on the ground when the crane is in each crane working window as the working horizontal distance of the crane working window; Take the ratio between the angle between the robotic arm and the horizontal plane and the preset angle value when the crane is working in each crane working window as the working matching rate of the crane working window; For each crane working window, calculate the negative mapping result with the natural constant as the base and the working horizontal distance as the exponent, and take the product of the negative mapping result, the working matching rate, and the wheel support force uniformity index as the boom extension suitability of the crane working window; Take the crane working window corresponding to the maximum value of the boom extension suitability of all crane working windows as the optimal working area of the crane.

[0008] Preferably, the method for obtaining the optimal extension angle and the optimal extension arm length of the crane's robotic arm according to the optimal working area of the crane is as follows: Based on the optimal working area of the crane, collect the working horizontal distance of the crane in the optimal working area, the inflection point height of the crane's robotic arm inflection point from the horizontal ground, and the working height between the target construction point and the horizontal ground respectively; Take the ratio of the difference between the working height and the inflection point height to the working horizontal distance as the mapping object, and take the result of the arctangent mapping of the mapping object as the optimal extension angle of the crane's robotic arm; Take the square root of the sum of the square of the mapping object and the square of the working horizontal distance as the optimal extension arm length of the crane's robotic arm.

[0009] Preferably, the method for obtaining the actual extension angle and the actual extension arm length according to the actual working area of the crane is as follows: Obtain the pre - processed image of the crane during actual operation, use the Otsu threshold segmentation technique to segment the crane from the pre - processed image of the crane operation, and use the Canny edge detection algorithm to identify the edge image of the crane; Count the number of pixel points in each direction in the edge image of the crane, take the direction with the largest number of pixel points as the actual mechanical arm extension direction, take the angle between the actual mechanical arm extension direction and the horizontal direction as the actual extension angle, and take the length of the mechanical arm of the crane in the actual mechanical arm extension direction as the actual extension arm length.

[0010] Preferably, the method for obtaining the high - altitude operation risk index of the crane during actual operation according to the optimal extension angle, optimal extension arm length, actual extension angle and actual extension arm length of the crane mechanical arm is as follows: Calculate the absolute value of the difference between the actual extension angle and the optimal extension angle, and take the ratio of the absolute value to the preset angle value as the first ratio parameter; obtain the second ratio parameter according to the actual extended arm length and the optimal extended arm length; Take the product of the first ratio parameter and the second ratio parameter as the high - altitude operation risk index of the crane during actual operation.

[0011] Preferably, the method for obtaining the second ratio parameter according to the actual extended arm length and the optimal extended arm length is as follows: Calculate the negative mapping result with the natural constant as the base and the difference between 1 and the ratio of the optimal extended arm length to the actual extended arm length as the exponent; Calculate the sum of the wheel support force uniformity index and the crane arm span suitability of the crane during actual operation in the actual working area; calculate the product of the negative mapping result and the sum; Take the reciprocal of the sum of the product and 1 as the second ratio parameter.

[0012] Preferably, the method for obtaining the key - point operation stability of each actual working moment of the crane according to the high - altitude operation risk index of the crane during actual operation is as follows: Sample the third preset parameter of actual working moments of the crane during actual operation, and calculate the high - altitude operation risk index of each actual working moment of the crane; Take the fourth preset parameter of actual working moments with the closest time interval to each actual working moment of the crane as each adjacent actual working moment of each actual working moment of the crane; For each actual working moment of the crane, calculate the absolute value of the difference between the high - altitude operation risk index of each adjacent actual working moment except the first adjacent actual working moment of the actual working moment and the previous adjacent actual working moment, and calculate the product of the absolute value and the high - altitude operation risk index of the actual working moment; The cumulative sum of the reciprocals of the sum of the product and 1 at all the adjacent actual working moments is used as the key-point operation stability at the actual working moment.

[0013] Preferably, the method for obtaining the predicted value of the key-point operation stability based on the key-point operation stability by using the autoregressive moving average model and performing online alarm on the high-altitude operation personnel based on the predicted value of the key-point operation stability is as follows: The key-point operation stability at all the actual working moments of the crane is used as the input of the Otsu threshold segmentation algorithm, the output of the Otsu threshold segmentation algorithm is used as the segmentation threshold of the key-point operation stability at all the actual working moments of the crane, and the segmentation threshold of the key-point operation stability at all the actual working moments of the crane is used as the safety threshold for the crane to work. The key-point operation stability at all the actual working moments of the crane is used as the input of the autoregressive moving average model, and the output of the autoregressive moving average model is used as the predicted value of the key-point operation stability at the current actual working moment of the crane. When the predicted value of the key-point operation stability at the current actual working moment of the crane is greater than the safety threshold for the crane to work, the crane can continue to work at this time; when the predicted value of the key-point operation stability at the current actual working moment of the crane is less than or equal to the safety threshold for the crane to work, an alarm is given through the warning module, and the crane is not suitable to continue working at this time.

[0014] The beneficial effects of this application are as follows: By analyzing the point cloud data in the workable area of the crane, this application constructs a ground ruggedness index to reflect the ruggedness of the ground within the working window of the crane; based on this, a wheel support force uniformity index is constructed in combination with the ground ruggedness indexes corresponding to the point cloud data obtained by multiple samplings to reflect the uniformity of the support force provided by the ground to the wheels within the working window; based on this, a crane boom extension suitability is constructed in combination with the height of the construction target point from the ground and the horizontal distance from the crane, and then the best working area of the crane is determined, and the best extension angle and the best extension length of the crane boom are obtained. By identifying the actual extension angle and the actual extension length of the crane boom in the crane working process image, a high-altitude operation danger index is constructed accordingly, and a key-point work stability is constructed by using the high-altitude operation danger index to reflect the difference between the actual working state and the best working state of the crane, and the obtained key-point work stability is uploaded to the crane control system, so as to realize the online monitoring of the key-point operation of the crane. By considering the change of ground ruggedness and the uniformity of the support force provided by different wheels when the crane is working, this application constructs the key-point operation stability, and then uses the autoregressive moving average model to obtain the predicted value of the key-point operation stability based on the key-point operation stability, so as to realize more accurate warning for high-altitude operation personnel and improve the accuracy and efficiency of online safety detection. Brief Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flow chart of an online safety monitoring system for crane operations provided by an embodiment of the present application; Figure 2 It is an implementation flow chart of an online safety monitoring system for crane operations provided by an embodiment of the present application. Detailed Embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0018] Please refer to Figure 1 , which shows a flow chart of an online safety monitoring system for crane operations provided by an embodiment of the present application. The system includes a working data acquisition module, an optimal working state acquisition module, a key point operation analysis module, and a warning module.

[0019] The working data acquisition module uses a camera to obtain the image data to be processed, preprocesses the image data, and completes the image data acquisition work.

[0020] The depth camera is used to collect the point cloud image of the working area of the crane at the construction site, and the high-definition camera is used to collect the image of the crane during operation. Since the construction site environment is complex, the collected images may have relatively large noise. To avoid the large impact of image noise on subsequent analysis, the present application performs denoising processing on the collected images. Traditional denoising methods include mean filtering, bilateral filtering, Gaussian filtering, etc. To retain the edge detail information in the image to a greater extent, the present application uses the bilateral filtering denoising technology to perform denoising processing on the collected images, and obtains the preprocessed images corresponding to the original images. Among them, the bilateral filtering denoising technology is a well-known technology, and the specific process will not be elaborated in the present application.

[0021] So far, the point cloud image of the workable area of the crane at the construction site and the image when the crane is working are obtained. It should be noted that the point cloud image of the workable area refers to the point cloud image of the workable area of the crane on the ground, and the image when the crane is working is a color image in the RGB space.

[0022] The optimal working state acquisition module obtains the ground ruggedness index according to the point cloud image data, obtains the wheel support force uniformity index according to the ground ruggedness index, obtains the crane boom extension suitability according to the wheel support force uniformity index, and obtains the optimal extension angle and the optimal extension boom length according to the crane boom extension suitability.

[0023] When the crane is working at high altitude, the ground in its working area needs to be relatively flat so that the four wheels of the crane can be evenly stressed and provide a stable support force for the crane to ensure the safety of high-altitude operations. Therefore, this application constructs a wheel support force uniformity index to reflect the stability of the four wheels of the crane providing support force for the crane.

[0024] The preprocessed point cloud image of the workable area of the crane is denoted as the preprocessed point cloud image A of the workable area. Since the working area of the crane is rectangular, a sliding window with the same size as the rectangular working area of the crane is set. The sliding window slides in the point cloud image A in the order from left to right and from top to bottom to obtain each crane working window.

[0025] According to the point cloud data in each crane working window, since the point cloud data points belong to the space, when the ground flatness is poor, the distribution of the Euclidean space distances between the point cloud data points is quite different. Here, a small amount of point cloud data points in the crane working window can reflect the overall characteristics. Here, according to the point cloud data in each crane working window area, the point cloud data in the crane working window is sampled K times according to the random sampling principle, and the number of point cloud data points for each sampling is N. The set of N point cloud data points for each sampling is used as the important point cloud data point set for each sampling. The value of N in this application takes an empirical value of 100, and the value of K in this application takes an empirical value of 50.

[0026] Based on the above analysis, calculate the ground ruggedness index of the important point cloud data point set during each sampling: In the formula, represents the wheel support force influence weight between the a-th point cloud data point and the c-th point cloud data point in the important point cloud data point set during the p-th sampling, represents the Euclidean distance function, 、 respectively represent the coordinates of the a-th and c-th point cloud data points in the important point cloud data set during the p-th sampling projected onto the horizontal ground in the vertical direction, represents the Euclidean distance between the coordinates of the a-th and c-th point cloud data points in the important point cloud data set during the p-th sampling projected onto the horizontal ground in the vertical direction, is an error parameter to avoid the denominator from taking a value of 0, and the empirical value of the error parameter is 1; represents the ground roughness index of the important point cloud data set during the p-th sampling, N represents the number of point cloud data points in the important point cloud data set during the p-th sampling, and the empirical value is taken as 100 in this application, 、 respectively represent the heights of the a-th and c-th point cloud data points in the important point cloud data set during the p-th sampling from the horizontal ground.

[0027] In the important point cloud data set during the p-th sampling, if the Euclidean distance between the projections of two point cloud data points onto the two-dimensional horizontal ground is smaller, that is, is smaller, it indicates that the two point cloud data points are closer in the horizontal plane. Since the horizontal distance between the two point cloud data points is closer, the influence degree of the height on the wheel support force is greater at this time, so the weight when calculating the ground roughness index is greater, and thus the calculated wheel support force influence weight is greater. At the same time, if the difference between the heights of the two point cloud data points from the horizontal ground is greater, that is, is greater, it indicates that the fluctuation degree of the two point cloud data points in height is greater, that is, the ruggedness of the ground is greater, so the calculated ground roughness index is greater.

[0028] Based on the above analysis, the same processing is performed on the important point cloud data sets obtained during each sampling, and the ground roughness index of the important point cloud data set for each sampling is obtained. If the ground within the crane working window is relatively flat, enabling the four wheels of the crane to provide stable and uniform support for the crane, then the values of the ground roughness index calculated for each sampling should be smaller.

[0029] Furthermore, in order to reflect the working conditions of the crane in each crane working window area, further analysis is carried out. Usually, a small number of point cloud data points in the crane working window can reflect the overall characteristics. However, when the overall complexity is relatively high, the randomness of the results of a single sampling is relatively large, and it is impossible to accurately reflect the working conditions of the crane in each crane working window area.

[0030] Specifically, in order to more accurately reflect the working conditions of the crane within each crane working window area, for each crane working window, a sequence formed by arranging the ground roughness indices of all sub-sampled important point cloud data points of the crane working window in the order of sampling is used as the roughness index sequence of the crane working window.

[0031] Based on the above analysis, calculate the wheel support force uniformity index for each crane working window to reflect the working conditions of the crane within each crane working window area: In the formula, represents the wheel support force uniformity index of the d-th crane working window in the preprocessed point cloud image A of the workable area of the crane, U represents the number of data points in the roughness index sequence of the d-th crane working window in the preprocessed point cloud image A of the workable area of the crane, represents the Sigmoid normalization function, 、 respectively represent the numerical values of the q-th and (q - 1)-th data points in the roughness index sequence of the d-th crane working window in the preprocessed point cloud image A of the workable area of the crane, represents the maximum value function, represents the identification factor, which is used to center the Sigmoid function. In this application, the empirical value is taken as 0.5, represents the error parameter, which is used to avoid the situation where the denominator is zero and cannot be calculated. In this application, the empirical value is taken as 1.

[0032] If the difference between the ground roughness indices of two important point cloud data point sets is large, that is the larger it is, it indicates that in this crane working window, the randomly selected point cloud data points are different, and the ground roughness is also different. That is, in the crane working window, the stability of the support force provided by the ground at different positions to the crane is different, indicating that the overall crane working window cannot provide a stable support force to the wheels of the crane. For example, if one side of the crane working window has a high terrain and the other side has a low terrain, when the crane works here, dangerous situations such as imbalance may occur. Therefore, the calculated wheel support force uniformity index is smaller.

[0033] Furthermore, based on the wheel support force uniformity index, construct the crane boom reach suitability to determine the optimal working area of the crane.

[0034] Specifically, the Euclidean distance between the inflection point of the crane boom and the projection point of the construction target point on the ground is used as the working horizontal distance from the crane working window to the construction target point, and the ratio of the angle between the crane boom and the horizontal plane when the crane is working in each crane working window to the preset angle value is used as the working matching rate. The preset angle value is 90 degrees.

[0035] When the crane operates in different working windows, the working states of its robotic arm, such as the extended arm length and angle, are different. In different working states, the rationality of the force on the robotic arm is different, and the corresponding degree of danger is also different. Accordingly, the suitability of the crane armspan can be constructed to reflect the degree to which each crane working window is suitable as a crane working area: In the formula, represents the suitability of the crane armspan for the i-th crane working window in the workable area of the crane, represents the work matching rate of the i-th crane working window in the workable area of the crane, represents the horizontal working distance between the i-th crane working window and the construction target point, represents the wheel support force uniformity index of the i-th crane working window.

[0036] The horizontal working distance between the crane and the construction target point when the crane operates in the i-th working window The smaller it is, the closer the angle between the extended and inclined direction of the robotic arm and the ground is to 90 degrees when the high-altitude operator stands on the crane platform structure for high-altitude operation. Then, the component of the robotic arm in the direction perpendicular to the ground is larger, and the corresponding stability is stronger, so the suitability of the crane armspan is larger. At the same time, the work matching rate of the i-th crane working window in the workable area of the crane is The larger it is, the shorter the length of the robotic arm that the crane needs to extend during construction, the greater the support force provided by the crane for the robotic arm, and the stronger the stability, so the suitability of the crane armspan is larger. The wheel support force uniformity index

[0037] of the crane in the i-th working window is

[0038] The larger it is, the more evenly and stably the chassis can support the crane when the crane operates in this window, that is, the stronger the stability of the crane, the more suitable this crane working window is as a working area for the crane, so the suitability of the crane armspan is larger. When the crane is working within the optimal working window, the height between the construction target point and the ground is taken as the working height H, the height between the inflection point of the robotic arm and the ground is taken as the inflection point height L, and the horizontal distance between the inflection point of the robotic arm and the construction target point is taken as the working horizontal distance 。

[0039] Calculate the optimal extension angle of the robotic arm according to the Pythagorean theorem and the optimal extended arm length ,and their calculation formula is as follows: In the formula, represents the optimal extension angle of the robotic arm when the crane is working within the optimal working window, represents the working height between the construction target point and the ground, represents the working horizontal distance between the optimal working window and the construction target point, represents the inflection point height between the inflection point of the robotic arm and the ground, represents the optimal extended arm length of the robotic arm when the crane is working within the optimal working window.

[0040] So far, obtaining the optimal extension angle and the optimal extended arm length of the crane's robotic arm in the optimal working area characterizes the optimal working state of the crane.

[0041] The key point operation analysis module obtains the high-altitude operation danger index according to the optimal extension angle and the optimal extended arm length, and obtains the stability of the key point operation according to the high-altitude operation danger index.

[0042] Furthermore, based on the obtained optimal working state of the crane's robotic arm, compare it with the actual working state of the crane to construct the high-altitude operation danger index.

[0043] Since the color of the crane is usually mainly engineering yellow, it is relatively easy to identify the crane in the image through machine vision algorithms whether it is day or night. Therefore, in this application, images of the crane during operation are collected through a high-definition camera, that is, the obtained preprocessed images of the actual operation of the crane. The Otsu threshold segmentation technique is used to segment the crane from the preprocessed images of the actual operation of the crane, and the canny edge detection algorithm is used to identify the edge image of the crane. According to the edge image of the crane, the sum of the number of pixel points in each direction is statistically calculated, and the direction with the largest sum of the number of pixel points is used as the actual extension direction of the mechanical arm. The angle between the actual extension direction of the mechanical arm of the crane and the horizontal direction is used as the actual extension angle, and the length of the mechanical arm in the actual extension direction of the crane is used as the actual extension arm length. Among them, the Otsu threshold segmentation technique and the canny edge detection algorithm are both well-known techniques and will not be elaborated further. It should be noted that when statistically calculating the sum of the number of pixel points in each direction, each direction is in the range of 0 degrees to 180 degrees, with a step size of 10 degrees, for a total of 19 directions.

[0044] Based on the actual and theoretical optimal extension angles and optimal extension arm lengths of the mechanical arm obtained from the above steps, combined with the wheel support force uniformity index and the crane arm span suitability, the high-altitude operation danger index of the crane during actual operation can be constructed. The construction process is as follows: In the formula, represents the high-altitude operation danger index of the crane during actual operation, represents the optimal extension angle of the crane's mechanical arm, represents the optimal extension arm length of the crane's mechanical arm, represents the actual extension angle of the crane's mechanical arm, represents the actual extension arm length of the crane's mechanical arm, represents the preset angle value, represents the error parameter, 、 respectively represent the wheel support force uniformity index and the crane arm span suitability when the crane is working in the optimal working area. It should be noted that the preset angle value is 90 degrees and the error parameter is 1.

[0045] When the crane is actually operating, the greater the difference between the actual extension angle and the optimal extension angle of the mechanical arm, and the greater the difference between the actual extension arm length and the optimal extension arm length of the mechanical arm, that is, the greater, the smaller, it indicates that the working stability of the mechanical arm is worse, the corresponding working state is more dangerous, and the calculated high-altitude operation danger index is greater; the sum of the wheel support force uniformity index and the crane arm span suitability The smaller it is, to a certain extent, it indicates that the uniformity of the wheel support force and the suitability of the crane boom length are smaller. That is, the higher the danger level of the aerial work at this time, the greater the aerial work danger index. By collecting the working images of the crane at X actual working moments in the above manner, in this application, X takes the empirical value of 100, and the acquisition time interval is denoted as Y. In this application, Y takes the empirical value of 10s. Through the above method of calculation, the aerial work danger index of each actual working moment of the crane can be obtained. In addition, the T actual working moments with the closest time intervals to each actual working moment are used as the adjacent working moments of each actual working moment respectively, where the empirical value of T is 10.

[0046] Furthermore, calculate the key point operation stability of each actual working moment of the crane: In the formula, represents the key point operation stability of the t-th actual working moment of the crane, T represents the number of adjacent actual working moments of the t-th actual working moment of the crane, and the empirical value of T is 10. represents the aerial work danger index of the t-th actual working moment of the crane. 、 respectively represent the aerial work danger indexes of the j-th and j - 1-th adjacent actual working moments of the t-th actual working moment of the crane. represents the error parameter.

[0047] Aerial work danger index The smaller it is, and at the same time the smaller the difference between the aerial work danger indexes of adjacent actual working moments, that is The smaller it is, it indicates that the stability degree of the crane during work at this time is higher, the safety of key point selection is higher, that is, the safety of the crane during work at this time is higher. Therefore, the calculated key point operation stability is greater.

[0048] Thus, the key point operation stability of each actual working moment of the crane is obtained.

[0049] Warning module, using the autoregressive moving average model to obtain the predicted value of the key point operation stability based on the key point operation stability, and alarm the aerial work personnel online based on the predicted value of the key point operation stability.

[0050] Further, the key-point operation stability at all actual working moments of the crane is used as the input of the Otsu threshold segmentation algorithm. The output of the Otsu threshold segmentation algorithm is used as the segmentation threshold for the key-point operation stability at all actual working moments of the crane. The segmentation threshold for the key-point operation stability at all actual working moments of the crane is used as the safety threshold for the crane's work. The Otsu threshold segmentation algorithm is a well-known technology, and the specific process will not be elaborated here. The implementation flowchart of this application is as Figure 2 shown.

[0051] The key-point operation stability at all actual working moments of the crane is used as the input of the autoregressive moving average model, that is, the key-point operation stability at the previous X actual working moments of the crane is used as the input. The output of the autoregressive moving average model is used as the predicted value of the key-point operation stability at the (X + 1)-th actual working moment of the crane, that is, the predicted value of the key-point operation stability at the current actual working moment. The autoregressive moving average model is a well-known technology, and the specific process will not be elaborated here.

[0052] The predicted value of the key-point operation stability at the current actual working moment of the crane is transmitted to the warning module. When the predicted value of the key-point operation stability at the current actual working moment of the crane is greater than the safety threshold for the crane's work, it indicates that the stability degree of the crane during operation at this time is relatively high, and the crane can continue to work. When the predicted value of the key-point operation stability at the current actual working moment of the crane is less than or equal to the safety threshold for the crane's work, the warning module issues an alarm, indicating that the stability degree of the crane during operation at this time is relatively low, and the crane is not suitable for continuing to work. At this time, the working position of the crane should be adjusted to a safe working position and then continue to work.

[0053] Thus, a safety online monitoring system for crane operation is completed.

[0054] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modification, equivalent replacement, improvement, etc. made within the principle of this application shall be included in the protection scope of this application.

Claims

1. A crane operation safety online monitoring system, characterized in that: The system includes the following modules: Working data acquisition module, obtaining point cloud image data after denoising; The module for acquiring the best working state obtains each crane working window according to the point cloud image data, obtains the ground roughness index of each sampled important point cloud data point set of each crane working window according to each crane working window; obtains the wheel support force uniformity index of each crane working window according to the ground roughness index of all sub-sampled important point cloud data point sets of each crane working window; obtains the crane arm span suitability of each crane working window according to the wheel support force uniformity index of each crane working window, and obtains the best working area of ​​the crane according to the crane arm span suitability of each crane working window; obtains the best extension angle and the best extension arm length of the crane mechanical arm according to the best working area of ​​the crane; The key point operation analysis module obtains the actual extension angle and actual extension arm length according to the actual working area of ​​the crane; obtains the high-altitude operation hazard index of the crane during actual operation according to the optimal extension angle, optimal extension arm length, actual extension angle and actual extension arm length of the crane mechanical arm; obtains the key point operation stability of the crane at each actual working moment according to the high-altitude operation hazard index of the crane during actual operation; The warning module uses the autoregressive moving average model to obtain the predicted value of the key point operation stability based on the key point operation stability, and alarms the high-altitude workers online based on the predicted value of the key point operation stability.

2. The crane operation safety online monitoring system according to claim 1 is characterized in that: The method of obtaining each crane working window according to the point cloud image data and obtaining the ground roughness index of each important point cloud data point set sampled in each crane working window according to each crane working window is: A sliding window of the same size as the rectangular working area of ​​the crane is set, and the sliding window is used to slide non-overlappingly in the point cloud image from left to right and from top to bottom, and the result of each sliding of the sliding window is used as a crane working window; For each crane working window, a first preset parameter number of points are sampled in the area corresponding to the crane working window by using the random sampling principle, and a second preset parameter number of point cloud data points are sampled each time, and a set consisting of all the point cloud data points sampled each time is used as an important point cloud data point set sampled each time; For any two point cloud data points in the important point cloud data point set extracted each time, the reciprocal of the sum of the metric distance between the two point cloud data points projected in the vertical direction onto the horizontal ground and 1 is used as the wheel support force influence weight between the two point cloud data points; The absolute value of the height difference between two point cloud data points and the horizontal ground is calculated, and the average of the quadratic sum of the product of the absolute value and the wheel support force influence weight on the important point cloud data point set is taken as the ground roughness index of the important point cloud data point set.

3. The crane operation safety online monitoring system according to claim 1 is characterized in that: The method for obtaining the wheel support force uniformity index of each crane working window according to the ground roughness index of all sub-sampled important point cloud data point sets of each crane working window is: For each crane working window, the sequence of ground roughness indexes of all sub-sampled important point cloud data point sets of the crane working window in the order of sampling is used as the roughness index sequence of the crane working window; The difference between the value of each data point except the first data point in the rugged index sequence and the value of the previous data point is used as the numerator, and the maximum value between the value of each data point except the first data point in the rugged index sequence and the value of the previous data point is used as the denominator, and a normalized result of the ratio of the numerator to the denominator is calculated; The absolute value of the difference between the normalized result and 0.5 is calculated, the reciprocal of the sum of the absolute value and 1 is calculated, and the cumulative sum of the reciprocals on the rugged index sequence is used as the wheel support force uniformity index of the crane working window.

4. The crane operation safety online monitoring system according to claim 1 is characterized in that: The method for obtaining the crane arm span suitability of each crane working window according to the wheel support force uniformity index of each crane working window and obtaining the optimal working area of ​​the crane according to the crane arm span suitability of each crane working window is: The Euclidean distance between the turning point of the crane arm and the projection point of the construction target point on the ground in each crane working window is taken as the working horizontal distance of the crane working window; The ratio of the angle between the mechanical arm and the horizontal plane when the crane is working in each crane working window to the preset angle value is taken as the working matching rate of the crane working window; For each crane working window, a negative mapping result with a natural constant as the base and the working horizontal distance as the exponent is calculated, and the product of the negative mapping result, the working matching rate and the wheel supporting force uniformity index is used as the crane arm span suitability of the crane working window; The crane working window corresponding to the maximum value of the crane arm span suitability of all crane working windows is taken as the optimal working area of ​​the crane.

5. The crane operation safety online monitoring system according to claim 1 is characterized in that: The method for obtaining the optimal extension angle and the optimal extension arm length of the crane mechanical arm according to the optimal working area of ​​the crane is: Based on the optimal working area of ​​the crane, the horizontal working distance of the crane in the optimal working area, the inflection point height of the crane mechanical arm inflection point from the horizontal ground, and the working height between the target construction point and the horizontal ground are collected respectively; The ratio of the difference between the working height and the inflection point height to the working horizontal distance is used as a mapping object, and the result of the inverse tangent mapping of the mapping object is used as the optimal extension angle of the crane mechanical arm; The quadratic square root of the sum of the square of the mapped object and the square of the working horizontal distance is taken as the optimal extension arm length of the crane mechanical arm.

6. The crane operation safety online monitoring system according to claim 1, characterized in that: The method for obtaining the actual extension angle and the actual extension arm length according to the actual working area of ​​the crane is: Obtain the actual working preprocessed image of the crane, use the Otsu threshold segmentation technique to segment the crane from the working preprocessed image of the crane, and use the Canny edge detection algorithm to identify the edge image of the crane; The number of pixels in each direction of the edge image of the crane is counted, and the direction with the largest number of pixels is taken as the actual extension direction of the robotic arm, the angle between the actual extension direction of the robotic arm and the horizontal direction is taken as the actual extension angle, and the length of the crane's robotic arm in the actual extension direction of the robotic arm is taken as the actual extension arm length.

7. The crane operation safety online monitoring system according to claim 1, characterized in that: The method for obtaining the high-altitude operation hazard index of the crane during actual operation according to the optimal extension angle, optimal extension arm length, actual extension angle and actual extension arm length of the crane mechanical arm is: Calculate the absolute value of the difference between the actual extension angle and the optimal extension angle, and use the ratio of the absolute value to the preset angle value as a first ratio parameter; obtain a second ratio parameter according to the actual extension arm length and the optimal extension arm length; The product of the first ratio parameter and the second ratio parameter is used as the high-altitude operation hazard index of the crane during actual operation.

8. The crane operation safety online monitoring system according to claim 7, characterized in that: The method for obtaining the second ratio parameter according to the actual extension arm length and the optimal extension arm length is: Calculate the negative mapping result with the natural constant as the base and the difference between 1 and the ratio of the optimal elongated arm length to the actual elongated arm length as the exponent; Calculating the sum of the wheel support force uniformity index and the crane arm span suitability when the crane is working in the actual working area; calculating the product of the negative mapping result and the sum; The reciprocal of the sum of the product and 1 is taken as the second ratio parameter.

9. The crane operation safety online monitoring system according to claim 1, characterized in that: The method for obtaining the key point operation stability of the crane at each actual working moment according to the high-altitude operation hazard index of the crane during actual working is: When the crane is actually working, a third preset parameter of actual working moments of the crane are sampled, and a high-altitude operation hazard index of each actual working moment of the crane is calculated; The fourth preset parameter actual working moments closest to each actual working moment of the crane are used as each adjacent actual working moment of each actual working moment of the crane; For each actual working moment of the crane, the absolute value of the difference between the high-altitude work hazard index of each adjacent actual working moment except the first adjacent actual working moment and the previous adjacent actual working moment is calculated, and the product of the absolute value and the high-altitude work hazard index of the actual working moment is calculated; The cumulative sum of the reciprocal of the sum of the product and 1 at all the adjacent actual working moments is taken as the key point operation stability of the actual working moment.

10. The crane operation safety online monitoring system according to claim 1, characterized in that: The method of using the autoregressive moving average model to obtain the predicted value of the key point operation stability based on the key point operation stability, and alarming the high-altitude operation personnel online based on the predicted value of the key point operation stability is: The key point operation stability of all actual working moments of the crane is used as the input of the Otsu threshold segmentation algorithm, the output of the Otsu threshold segmentation algorithm is used as the segmentation threshold of the key point operation stability of all actual working moments of the crane, and the segmentation threshold of the key point operation stability of all actual working moments of the crane is used as the safety threshold of the crane operation; The key point operation stability of all actual working moments of the crane is used as the input of the autoregressive moving average model, and the output of the autoregressive moving average model is used as the predicted value of the key point operation stability of the crane at the current actual working moment; When the predicted value of the key point operation stability at the current actual working moment of the crane is greater than the safety threshold of the crane's operation, the crane can continue to work; when the predicted value of the key point operation stability at the current actual working moment of the crane is less than or equal to the safety threshold of the crane's operation, an alarm is issued through the warning module, and the crane is not suitable to continue working.

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