A method for predicting the load bearing performance and service life of a crane metal structure

Abstract

The present application relates to the technical field of data processing, and more particularly to a crane metal structure bearing performance and life prediction method, which obtains stress data at each preset monitoring position at each sampling time during the crane hoisting operation of any object, obtains a stress data sequence, screens to-be-corrected stress data in the stress data sequence, obtains the wind interference probability of any to-be-corrected stress data and the corresponding wind stress, corrects any to-be-corrected stress data to obtain a stress data correction value, and obtains the stress data correction value corresponding to each to-be-corrected stress data; all to-be-corrected stress data in the stress data sequence is replaced by each stress data correction value to obtain a target stress data sequence; the wear degree of the crane is obtained according to the target stress data sequence, and the service life of the crane is predicted according to the wear degree, so that the stress data monitoring accuracy affected by the wind is reduced, and the prediction accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for predicting the load-bearing capacity and lifespan of a crane's metal structure. Background Technology

[0002] With the rapid development of modern industry, cranes, as important material handling equipment, play a vital role in various industrial sites. They not only solve logistics and transportation problems but also greatly reduce the workload of personnel and improve production efficiency. However, during use, especially under long-term alternating loads, the metal structure of cranes often suffers fatigue damage. If this fatigue damage is not detected and treated in time, it may lead to a decline in crane performance or even cause serious safety accidents.

[0003] In existing technologies, stress data of cranes during operation is typically monitored and acquired. This stress data is then used to assess the wear and tear of the crane and predict its service life. However, due to the influence of factors such as wind force when monitoring stress data, the monitored stress data may not match the actual stress data, thus affecting the assessment of the crane's wear and tear and ultimately leading to a significant deviation in the predicted service life.

[0004] Therefore, ensuring the accuracy of stress data monitoring in order to improve the accuracy of crane service life prediction has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method for predicting the load-bearing capacity and lifespan of a crane's metal structure, in order to solve the problem of how to ensure the monitoring accuracy of stress data in order to improve the accuracy of predicting the lifespan of the crane.

[0006] This invention provides a method for predicting the load-bearing capacity and lifespan of a crane's metal structure. The method includes the following steps: During the process of a crane lifting any object, stress data at each preset monitoring position at each sampling time is acquired to obtain a stress data sequence. Based on the theoretical allowable stress of the crane, at least one stress data to be corrected is selected from the stress data sequence. For any stress data to be corrected, the wind presence period at the sampling time of the stress data to be corrected is obtained. Based on the stress data and wind characteristics at each preset monitoring location at each sampling time in the wind presence period, the wind interference probability of the stress data to be corrected is obtained. Obtain the wind stress corresponding to any stress data to be corrected, and correct the stress data to be corrected according to the wind interference probability and the wind stress to obtain the stress data correction value. Obtain the stress data correction value corresponding to each stress data to be corrected. Replace all stress data to be corrected in the stress data sequence with each stress data correction value to obtain a target stress data sequence; obtain the wear degree of the crane based on the target stress data sequence, and predict the service life of the crane based on the wear degree.

[0007] Preferably, the step of filtering the stress data to be corrected from the stress data sequence based on the theoretical allowable stress of the crane includes: The maximum stress value that the crane can withstand is obtained as the theoretical allowable stress. A non-interference operation test is conducted on the crane to obtain a dynamic experimental value sequence of stress data. The mean value of the stress data in the dynamic experimental value sequence is calculated. The absolute value of the difference between the theoretical allowable stress and the mean value of the stress data is taken as the allowable stress error. A curve showing the relationship between the weight of the experimental object and the experimental stress data is constructed. Based on the curve, theoretical stress data corresponding to any object is obtained. For any stress data in the stress data sequence, if the absolute value of the difference between any stress data and the theoretical stress data is greater than the allowable stress error, then any stress data is taken as the stress data to be corrected.

[0008] Preferably, the period during which wind exists during the sampling time of acquiring any of the stress data to be corrected includes: The sampling time of any of the stress data to be corrected is taken as the target time, and the wind direction at the target time is obtained; In the sampling times before the target time, according to the wind direction at each sampling time, the first sampling time that has the same wind direction as the target time and is continuous with the target time is obtained and recorded as the starting time; In the sampling times after the target time, based on the wind direction at each sampling time, the last sampling time that has the same wind direction as the target time and is continuous with the target time is obtained and recorded as the termination time. The time period between the start time and the end time is recorded as the wind presence period at the target time.

[0009] Preferably, the step of obtaining the probability of wind interference of any stress data to be corrected based on the stress data and wind characteristics at each preset monitoring location at each sampling time during the wind existence period includes: Based on the stress data differences at each preset monitoring location at each sampling time during the wind-existing period, the consistency of local stress change trends at the target time is obtained. The preset monitoring location corresponding to any stress data to be corrected is taken as the target monitoring location. Based on the stress data and wind characteristics differences at each sampling time during the wind-existing period, the consistency of stress data fluctuation at the target monitoring location is obtained. The probability of wind interference for any stress data to be corrected is obtained by multiplying the consistency of the local stress change trend and the consistency of the stress data fluctuation.

[0010] Preferably, the step of obtaining the consistency of local stress change trends at the target time based on the stress data differences at each preset monitoring location at each sampling time during the wind's existence period includes: For any sampling time during the period when the wind is present, the absolute value of the difference between the stress data at every two preset monitoring locations at any sampling time is calculated to form a sequence of absolute difference values. The sequence of absolute difference values ​​corresponding to each sampling time during the period when the wind is present is obtained. Using the DTW algorithm, the similarity between every two sequences of absolute difference values ​​is calculated to obtain the mean similarity value, which is recorded as the consistency of the local stress change trend at the target time.

[0011] Preferably, the wind characteristics include wind speed and wind direction. Then, based on the stress data and wind characteristic differences at each sampling time during the wind's presence period at the target monitoring location, the stress data fluctuation consistency at the target monitoring location is obtained, including: The stress data of the target monitoring location at each sampling time during the period when the wind is present is obtained, and the stress data variance is calculated; the wind speed of the target monitoring location at each sampling time during the period when the wind is present is obtained, and the wind speed variance is calculated; the difference similarity is obtained based on the reciprocal of the absolute value of the difference between the stress data variance and the wind speed variance. The stress data at each preset monitoring location at the target time are sorted in descending order to obtain the first location number of each preset monitoring location. For any preset monitoring location, stress data for each sampling time between the previous sampling time and the target time during the wind-affected period are acquired, forming a stress time-series data sequence. The stress time-series data sequence is then subjected to first-order differencing to obtain the difference mean of the first-order differencing sequence. The difference between the reciprocal of the difference mean and a constant 1 is obtained. The mean between the reciprocal of the first location number of any preset monitoring location and the difference is calculated and denoted as the degree of influence of any preset monitoring location. The degree of influence of each preset monitoring location is then obtained, and the degree of influence of all preset monitoring locations is sorted in descending order to obtain the second location number of each preset monitoring location. Obtain the absolute value of the difference between the first and second location numbers of the target monitoring location, and perform a weighted summation of the difference similarity and the reciprocal of the absolute value of the number difference to obtain the stress data fluctuation consistency at the target monitoring location.

[0012] Preferably, the step of correcting any stress data to be corrected based on the wind disturbance probability and the wind stress to obtain a corrected stress data value includes: Obtain the product between the probability of wind disturbance and the wind stress, and record the difference between any stress data to be corrected and the product as the stress data correction value.

[0013] Preferably, obtaining the wear degree of the crane based on the target stress data sequence includes: Obtain the sum of the theoretical stress data and the allowable stress error; for any stress data in the target stress data sequence, obtain the difference between any stress data and the sum; obtain the maximum value between the difference and the constant 0, and record it as the error value of any stress data. The mean error value is obtained based on the error value of each stress data in the target stress data sequence. The wear degree of the crane is obtained based on the ratio of the mean error value to the allowable stress error.

[0014] Preferably, predicting the service life of the crane based on the degree of wear includes: Obtain the design service life of the crane, calculate the difference between the constant 1 and the wear level, and record the product of the design service life and the difference as the theoretical service life under the wear level. The crane's service life is obtained, and the difference between the theoretical service life and the service life is recorded as the crane's predicted remaining service life.

[0015] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention collects real-time stress data from multiple monitoring locations on a crane, filters out stress data to be corrected to reduce computational load, analyzes the probability of wind interference for each stress data point to be corrected, corrects the stress data to obtain a corrected value, reduces the impact of wind on stress data monitoring accuracy, accurately characterizes the stress situation during crane operation, and then assesses the wear degree of the crane during current operation based on the corrected stress data value, thereby reducing the error in predicting the crane's service life based on the wear degree. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for predicting the load-bearing capacity and lifespan of a crane metal structure, provided in Embodiment 1 of the present invention. Detailed Implementation

[0018] Embodiments of this disclosure are described in detail below, with examples of these embodiments illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting it.

[0019] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure.

[0020] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0021] See Figure 1 This is a flowchart of a method for predicting the load-bearing capacity and lifespan of a crane metal structure according to Embodiment 1 of the present invention. Figure 1 As shown, the method may include: Step S101: During the process of the crane lifting any object, stress data at each preset monitoring position at each sampling time is acquired to obtain a stress data sequence. Based on the theoretical allowable stress of the crane, stress data to be corrected is selected from the stress data sequence.

[0022] This invention primarily analyzes the wear degree of a crane based on real-time stress data monitored during the lifting of any object, in order to predict the crane's service life. However, considering that the collected real-time stress data may be affected by wind interference, the real-time stress data affected by wind interference is corrected before analyzing the wear degree, and the corrected real-time stress data is used for wear degree analysis.

[0023] Therefore, in this embodiment of the invention, strain gauge sensors are installed at the load-bearing structure bridge of the crane. For example, the bridge of a general-purpose bridge crane is installed at the box-shaped main beam and box-shaped end beam. Ten sensors are used, which can be set according to actual conditions. The sensors are evenly installed at ten preset monitoring positions on the bridge. Then, the sensors are used to acquire stress data at each preset monitoring position at each sampling time, resulting in a stress data sequence. It should be noted that the stress data collected when the crane lifts any object is collected for the entire process from lifting the object to lowering it, with a collection frequency of once per second, which can be set according to actual conditions.

[0024] Meanwhile, since the most direct factor affecting stress data is wind, wind direction and speed are collected using an anemometer. The collection time and sampling frequency are consistent with the stress data, that is, stress data, wind speed and wind direction are acquired at each preset monitoring location at each sampling time, and the wind speed and wind direction are the same at all preset monitoring locations at each sampling time.

[0025] Since wind is not constant and its speed varies at different times, the impact of wind on the stress data of cranes can be significant. Therefore, it is necessary to select stress data with a high degree of influence from the stress data sequence as the stress data to be corrected. The remaining stress data are assumed to be within the allowable error range and do not require further correction.

[0026] The method for filtering the stress data to be corrected in the stress data sequence is as follows: The maximum stress value that the crane can withstand is taken as the theoretical allowable stress. The maximum allowable stress value refers to the maximum stress that a part or component is allowed to withstand in mechanical or engineering structural design. Since the theoretical allowable stress of a crane is a static value, while the stress data in a stress data sequence is a dynamic value, there is an error between the static and dynamic values. This error can be corrected by conducting operational tests on the crane in a laboratory without external influences, obtaining a dynamic experimental value sequence of stress data, calculating the mean value of the stress data in the dynamic experimental value sequence, and using the absolute value of the difference between the theoretical allowable stress and the mean value of the stress data as the allowable stress error. .

[0027] By using an experimental crane to lift objects of different weights, the stress corresponding to the crane lifting objects of different weights was obtained and recorded as experimental stress data. It is worth noting that since the tensile force on the object is only related to the weight of the object itself and is independent of the lifting height, the stress theoretically remains unchanged during the lifting process. Although in reality, the crane hook and cable are not completely rigid and will deform to a certain extent due to the force, this deformation will cause a slight change in the tension of the cable on the object, and this slight change can be ignored.

[0028] After obtaining the experimental stress data corresponding to each object with different weights, a curve is constructed showing the relationship between the object weight and the experimental stress data. The horizontal axis of the curve represents the object weight, and the vertical axis represents the experimental stress data. Based on this curve, the theoretical stress data corresponding to any given object is obtained. For any stress data in the stress data sequence, if any stress data matches the theoretical stress data... If the absolute value of the difference between the stress data and the stress data is greater than the allowable stress error, then any stress data will be used as the stress data to be corrected.

[0029] Thus, the stress data to be corrected in the stress data sequence is obtained, which is the stress data that is greatly affected by wind interference.

[0030] Step S102: For any stress data to be corrected, obtain the wind presence period at the sampling time of any stress data to be corrected, and obtain the wind interference probability of any stress data to be corrected based on the stress data and wind characteristics at each preset monitoring location at each sampling time during the wind presence period.

[0031] Since the influence of wind affects the entire suspended object, the stress as a whole will change due to the thrust from the wind direction. However, this change will exhibit certain trends locally. Because wind changes are phased, stress data at similar times will show similar trends in different local areas. Meanwhile, due to the random distribution of wear, wear will cause significant and irregular differences in stress across different local areas. Wind acts on the object, and stress data collected at each preset monitoring location is affected by wind, with more pronounced changes in stress data at the windward side and less noticeable changes at the leeward side. Therefore, this embodiment of the invention analyzes the differences in stress data within a local time period at the sampling time of each stress data point to be corrected, and analyzes the degree of wind interference for each stress data point to be corrected.

[0032] Taking any stress data to be corrected as an example, the sampling time of the stress data to be corrected is taken as the target time, and the wind direction at the target time is obtained; in the sampling times before the target time, according to the wind direction at each sampling time, the first sampling time that is the same as the wind direction at the target time and is continuous with the target time is obtained and recorded as the start time; in the sampling times after the target time, according to the wind direction at each sampling time, the last sampling time that is the same as the wind direction at the target time and is continuous with the target time is obtained and recorded as the end time; the time period between the start time and the end time is recorded as the wind force existence period at the target time, where the wind force existence period refers to the duration of wind force influence under the same wind direction.

[0033] Based on the stress data fluctuations at various preset monitoring locations during the wind's presence, the consistency of stress change trends affected by wind within a local timeframe at the target time is detected, i.e., the consistency of local stress change trends at the target time. The specific detection method is as follows: For any sampling time during the period when the wind is present, the absolute value of the difference between the stress data at every two preset monitoring locations at any sampling time is calculated to form a sequence of absolute difference values. The sequence of absolute difference values ​​corresponding to each sampling time during the period when the wind is present is obtained. Using the DTW algorithm, the similarity between every two sequences of absolute difference values ​​is calculated to obtain the mean similarity value, which is recorded as the consistency of the local stress change trend at the target time.

[0034] In one embodiment, the DTW algorithm is used to calculate the DTW distance between each pair of absolute difference sequences. The DTW algorithm is an existing technology and will not be described in detail here. The smaller the DTW distance, the higher the similarity. Therefore, the difference between the constant 1 and the DTW distance is used as the similarity between each pair of absolute difference sequences.

[0035] The formula for calculating the consistency of the local stress change trend at the target time is: ; in, Indicates the consistency of local stress variation trends. Indicates the number of similarities. Let | represent the i-th similarity, and | denote the absolute value symbol.

[0036] It should be noted that, This indicates the similarity of stress data changes at all preset monitoring locations at two sampling times. The higher the similarity, the more consistent the trend of stress data changes during the period when wind is present. The greater the consistency of the local stress change trend at the target time, the greater the probability of being affected by wind at the target time.

[0037] Since the stress caused by wind remains essentially constant under constant wind speed, the corresponding monitored stress data may remain unchanged or show little change for a period of time. If there is no significant change in wind force, the stress data will also show no significant change. However, the influence will be more pronounced for preset monitoring locations closer to the wind. Therefore, the influence of wind force on the preset monitoring location corresponding to any stress data to be corrected can be detected based on the stress data changes and wind speed and direction changes during the wind's presence period. This reflects the consistency of stress fluctuations at the preset monitoring location corresponding to any stress data to be corrected. The preset monitoring location corresponding to any stress data to be corrected is then used as the target monitoring location. The stress data at each sampling time during the wind's presence period is obtained for the target monitoring location, and the stress data variance is calculated. The wind speed at each sampling time during the wind's presence period is also obtained for the target monitoring location, and the wind speed variance is calculated. The difference similarity is obtained by taking the reciprocal of the absolute value of the difference between the stress data variance and the wind speed variance. Since wind can exert a deflection force on the suspended object, causing the stress data to increase, the stress data collected at each preset monitoring position at the target time can be arranged in descending order, that is, the stress data at each preset monitoring position at the target time can be arranged in descending order to obtain the first position number of each preset monitoring position. For any preset monitoring location, stress data is acquired at each sampling time between the previous sampling time and the target time during the wind-affected period, forming a stress time-series data sequence. The stress time-series data sequence is then subjected to first-order differencing to obtain the mean difference of the first-order differencing sequence. Since the mean difference is affected by extreme values, if any preset monitoring location has a smaller first location number, it indicates a closer proximity to the wind and a more significant wind influence. Therefore, the stress difference between the previous sampling time and the start time of the wind-affected period will be more pronounced, and the mean difference will be affected by this stress difference. This will result in a larger mean difference for preset monitoring locations with smaller first location numbers. Therefore, the difference between the reciprocal of the mean difference and the constant 1 is obtained. The mean between the reciprocal of the first location number of any preset monitoring location and the difference is calculated and denoted as the degree of influence of any preset monitoring location. The degree of influence of each preset monitoring location is then obtained, and the degree of influence of all preset monitoring locations is sorted in descending order to obtain the second location number of each preset monitoring location. Obtain the absolute value of the difference between the first and second location numbers of the target monitoring location, and perform a weighted summation of the difference similarity and the reciprocal of the absolute value of the number difference to obtain the stress data fluctuation consistency at the target monitoring location.

[0038] In one embodiment, the formula for calculating the consistency of stress data fluctuations at the target monitoring location is: ; in, This indicates the consistency of stress data fluctuations at the target monitoring location. Indicates the first weight. Indicates the second weight. Indicates the variance of stress data. Indicates the variance of wind speed. Indicates the second position number. The first position number is indicated by | |, which represents the absolute value symbol.

[0039] It should be noted that, The smaller the value, the more consistent the fluctuations of the stress data and the wind data are, and the greater the consistency of the stress data fluctuations. The smaller the value, the more the stress data changes at the target monitoring location conform to the influence of the proximity to the wind, corresponding to a greater degree of wind influence and a greater consistency in stress data fluctuations at the target monitoring location. Since changes in wind and stress data are affected by location, The value of is weakly affected by position, thus exhibiting poor consistency; therefore, let There are no restrictions here; you can set them according to the specific scenario.

[0040] Furthermore, after obtaining the consistency of the local stress change trend at the sampling time of any stress data to be corrected and the consistency of the stress data fluctuation at the preset monitoring position corresponding to any stress data to be corrected, the probability of wind interference of any stress data to be corrected is obtained by multiplying the consistency of the local stress change trend and the consistency of the stress data fluctuation.

[0041] In one embodiment, the wind interference probability of any stress data to be corrected .

[0042] Step S103: Obtain the wind stress corresponding to any stress data to be corrected; correct any stress data to be corrected according to the probability of wind interference and wind stress to obtain the stress data correction value; obtain the stress data correction value corresponding to each stress data to be corrected.

[0043] Based on the wind speed at the target time and the surface area of ​​the suspended object, the theoretical thrust data caused by the wind is analyzed. The specific process is as follows: using the wind force calculation formula, the wind force corresponding to the wind speed at the target time is obtained; using the wind stress calculation formula and the wind force, the wind stress corresponding to any stress data to be corrected is obtained, which is used to characterize the magnitude of the theoretical thrust caused by the wind.

[0044] The formula for calculating wind force is as follows: ,in, Wind force, measured in Newtons (N). This refers to air density, typically taken as 1.225 kg / m³. 3 ), It is wind speed, measured in meters per second (m / s). It is the wind pressure coefficient, a dimensionless parameter that depends on the shape of the object and the wind direction. It is the windward surface area of ​​an object, measured in square meters (m²). 2 ), which is the surface area of ​​the object that is being suspended.

[0045] It is worth noting that the wind pressure coefficient is obtained using existing technology. It is known that the wind pressure coefficient of an isolated cylinder is between 0.7 and 1.2, while that of a lattice structure (such as a steel cage) is between 2.0 and 3.0. This invention uses suspended steel bars as an example, where the wind pressure coefficient is mainly related to the Reynolds number (Re), where Reynolds number (Re) = (V × D) / v, and v is the kinematic viscosity of air (approximately 1.5 × 10⁻⁶ at room temperature). -5 m 2 / s), D is the diameter of the rebar, V is the wind speed. Assuming the air temperature is 20 degrees Celsius, the rebar diameter D = 0.02 m, the wind speed V = 10 m / s, and Re is approximately 13333. Since Re is less than 2 × 10 5At that time, the wind pressure coefficient was 1.2, and Re was greater than or equal to 2 × 10⁻⁶. 5 When Re is approximately 13333, the wind pressure coefficient is 0.6-0.7. Therefore, when Re is approximately 13333, the wind pressure coefficient is 1.2. Furthermore, in this embodiment of the invention, the specific value of the wind pressure coefficient can be estimated experimentally based on the suspended object, and is not limited here.

[0046] The formula for calculating wind stress is as follows: Y represents wind stress, with the unit being Pascal (Pa), which is existing technology and will not be elaborated upon here.

[0047] Furthermore, based on the wind interference probability and wind stress of any stress data to be corrected, the stress data to be corrected is corrected to obtain a corrected stress data value. The correction process is as follows: obtain the product between the wind interference probability and the wind stress, and record the difference between the stress data to be corrected and the product as the corrected stress data value.

[0048] In one embodiment, the formula for calculating the stress data correction value is: ; in, This indicates the stress data correction value. This represents any stress data to be corrected. This represents the wind stress corresponding to any stress data point to be corrected. This represents the probability of wind disturbance for any stress data to be corrected.

[0049] Similarly, following the method for obtaining the stress data correction value of any stress data to be corrected, each stress data to be corrected is corrected to obtain the stress data correction value corresponding to each stress data to be corrected.

[0050] Step S104: Replace all stress data to be corrected in the stress data sequence with each stress data correction value to obtain the target stress data sequence; obtain the wear degree of the crane based on the target stress data sequence, and predict the service life of the crane based on the wear degree.

[0051] Based on step S103 above, the stress data correction value for each stress data to be corrected in the stress data sequence is obtained. Then, each stress data correction value replaces all stress data to be corrected in the stress data sequence to obtain the corrected stress data sequence. The actual stress monitoring value, excluding wind interference, is recorded as the target stress data sequence. Then, the wear degree of the crane is obtained based on the target stress data sequence, including: Obtain the sum of the theoretical stress data and the allowable stress error; for any stress data in the target stress data sequence, obtain the difference between any stress data and the sum; obtain the maximum value between the difference and the constant 0, and record it as the error value of any stress data. The mean error value is obtained based on the error value of each stress data in the target stress data sequence. The wear degree of the crane is obtained based on the ratio of the mean error value to the allowable stress error.

[0052] In one embodiment, the formula for calculating the wear level of the crane is: ; in, This indicates the degree of wear on the crane, and N represents the number of stress data points in the target stress data sequence. This represents the i-th stress data point in the target stress data sequence. This represents the theoretical stress data for any object. This represents the allowable stress error, and max() represents the function to take the maximum value.

[0053] It should be noted that, using The value represents the error value of the i-th stress data. Since the stress data produced by wear will exceed the allowable stress error, if the error value is negative, the i-th stress data is considered to belong to the stress data under non-wear conditions, and if the error value is positive, the i-th stress data is considered to belong to the stress data under wear conditions. Then, the max() function is used to exclude stress data with negative error values. In the error values ​​of each stress data in the target stress data sequence, data with positive error values ​​are selected to obtain the mean error value, which is used to characterize the degree of stress error generated by the crane under the current wear condition. The larger the mean error value, the greater the wear degree of the corresponding crane.

[0054] Furthermore, after obtaining the degree of wear on the crane, the crane's service life is predicted based on this degree of wear. The specific prediction method is as follows: The design service life of the crane is obtained, where the design service life refers to the period during which the crane is expected to operate safely and reliably under normal use and maintenance conditions. In this embodiment of the invention, the design service life is measured in days. Then, the difference between the constant 1 and the wear level is calculated, and the product of the design service life and the difference is recorded as the theoretical service life under the wear level. The actual service life of the crane is obtained, where the actual service life refers to the number of historical days the crane has been used, and the difference between the theoretical service life and the actual service life is recorded as the predicted remaining service life of the crane.

[0055] It is worth noting that predicting the service life of a crane based on the degree of wear includes, but is not limited to, the examples given in the embodiments of the present invention. Existing technologies can be selected for prediction based on the implementation scenario.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting the load-carrying capacity and service life of a crane metal structure, characterized by, The method includes: During the process of a crane lifting any object, stress data at each preset monitoring position at each sampling time is acquired to obtain a stress data sequence. Based on the theoretical allowable stress of the crane, at least one stress data to be corrected is selected from the stress data sequence. For any stress data to be corrected, the wind presence period at the sampling time of the stress data to be corrected is obtained. Based on the stress data and wind characteristics at each preset monitoring location at each sampling time in the wind presence period, the wind interference probability of the stress data to be corrected is obtained. Obtain the wind stress corresponding to any stress data to be corrected, and correct the stress data to be corrected according to the wind interference probability and the wind stress to obtain the stress data correction value. Obtain the stress data correction value corresponding to each stress data to be corrected. Replace all stress data to be corrected in the stress data sequence with each stress data correction value to obtain a target stress data sequence; obtain the wear degree of the crane based on the target stress data sequence, and predict the service life of the crane based on the wear degree; The step of obtaining the probability of wind interference for any stress data to be corrected based on the stress data and wind characteristics at each preset monitoring location at each sampling time during the wind existence period includes: Based on the stress data differences at each preset monitoring location at each sampling time during the wind-existing period, the consistency of local stress change trends at the target time is obtained. The preset monitoring location corresponding to any stress data to be corrected is taken as the target monitoring location. Based on the stress data and wind characteristics differences at each sampling time during the wind-existing period, the consistency of stress data fluctuation at the target monitoring location is obtained. The probability of wind interference for any stress data to be corrected is obtained by multiplying the consistency of the local stress change trend and the consistency of the stress data fluctuation. The step of obtaining the consistency of local stress change trends at a target time based on the stress data differences at each preset monitoring location at each sampling time during the wind-existing period includes: For any sampling time during the wind-existing period, the absolute value of the difference between stress data at every two preset monitoring locations at any sampling time is calculated to form a sequence of absolute difference values; the sequence of absolute difference values ​​corresponding to each sampling time during the wind-existing period is obtained, and the similarity between every two absolute difference value sequences is calculated using the DTW algorithm to obtain the mean similarity value, which is recorded as the consistency of the local stress change trend at the target time. The wind characteristics include wind speed and wind direction. Based on the stress data and wind characteristic differences at each sampling time during the wind's presence period at the target monitoring location, the stress data fluctuation consistency at the target monitoring location is obtained, including: The stress data of the target monitoring location at each sampling time during the period when the wind is present is obtained, and the stress data variance is calculated; the wind speed of the target monitoring location at each sampling time during the period when the wind is present is obtained, and the wind speed variance is calculated; the difference similarity is obtained based on the reciprocal of the absolute value of the difference between the stress data variance and the wind speed variance. The stress data at each preset monitoring location at the target time are sorted in descending order to obtain the first location number of each preset monitoring location. For any preset monitoring location, stress data for each sampling time between the previous sampling time and the target time during the wind-affected period are acquired, forming a stress time-series data sequence. The stress time-series data sequence is then subjected to first-order differencing to obtain the difference mean of the first-order differencing sequence. The difference between the reciprocal of the difference mean and a constant 1 is obtained. The mean between the reciprocal of the first location number of any preset monitoring location and the difference is calculated and denoted as the degree of influence of any preset monitoring location. The degree of influence of each preset monitoring location is then obtained, and the degree of influence of all preset monitoring locations is sorted in descending order to obtain the second location number of each preset monitoring location. Obtain the absolute value of the difference between the first and second location numbers of the target monitoring location, and perform a weighted summation of the difference similarity and the reciprocal of the absolute value of the number difference to obtain the stress data fluctuation consistency at the target monitoring location.

2. The method for predicting the load-bearing capacity and lifespan of a crane metal structure according to claim 1, characterized in that, The step of filtering at least one stress data point to be corrected from the stress data sequence based on the theoretical allowable stress of the crane includes: The maximum stress value that the crane can withstand is obtained as the theoretical allowable stress. A non-interference operation test is conducted on the crane to obtain a dynamic experimental value sequence of stress data. The mean value of the stress data in the dynamic experimental value sequence is calculated. The absolute value of the difference between the theoretical allowable stress and the mean value of the stress data is taken as the allowable stress error. A curve showing the relationship between the weight of the experimental object and the experimental stress data is constructed. Based on the curve, theoretical stress data corresponding to any object is obtained. For any stress data in the stress data sequence, if the absolute value of the difference between any stress data and the theoretical stress data is greater than the allowable stress error, then any stress data is taken as the stress data to be corrected.

3. The method for predicting the load-bearing capacity and lifespan of a crane metal structure according to claim 1, characterized in that, The period during which wind force exists at the sampling time when any of the stress data to be corrected is obtained includes: The sampling time of any of the stress data to be corrected is taken as the target time, and the wind direction at the target time is obtained; In the sampling times before the target time, according to the wind direction at each sampling time, the first sampling time that has the same wind direction as the target time and is continuous with the target time is obtained and recorded as the starting time; In the sampling times after the target time, based on the wind direction at each sampling time, the last sampling time that has the same wind direction as the target time and is continuous with the target time is obtained and recorded as the termination time. The time period between the start time and the end time is recorded as the wind presence period at the target time.

4. The method for predicting the load-bearing capacity and lifespan of a crane metal structure according to claim 1, characterized in that, The step of correcting any stress data to be corrected based on the wind disturbance probability and the wind stress to obtain a corrected stress data value includes: Obtain the product between the probability of wind disturbance and the wind stress, and record the difference between any stress data to be corrected and the product as the stress data correction value.

5. The method for predicting the load-bearing capacity and lifespan of a crane metal structure according to claim 2, characterized in that, The step of obtaining the wear degree of the crane based on the target stress data sequence includes: Obtain the sum of the theoretical stress data and the allowable stress error; for any stress data in the target stress data sequence, obtain the difference between any stress data and the sum; obtain the maximum value between the difference and the constant 0, and record it as the error value of any stress data. The mean error value is obtained based on the error value of each stress data in the target stress data sequence. The wear degree of the crane is obtained based on the ratio of the mean error value to the allowable stress error.

6. The method for predicting the load-bearing capacity and lifespan of a crane metal structure according to claim 1, characterized in that, The prediction of the crane's service life based on the degree of wear includes: Obtain the design service life of the crane, calculate the difference between the constant 1 and the wear level, and record the product of the design service life and the difference as the theoretical service life under the wear level. The crane's service life is obtained, and the difference between the theoretical service life and the service life is recorded as the crane's predicted remaining service life.

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