CN117422296B - Standard knot use priority determining method and system - Google Patents

Standard knot use priority determining method and system Download PDF

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CN117422296B
CN117422296B CN202311308861.1A CN202311308861A CN117422296B CN 117422296 B CN117422296 B CN 117422296B CN 202311308861 A CN202311308861 A CN 202311308861A CN 117422296 B CN117422296 B CN 117422296B
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CN117422296A (en
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金晓春
王魏
陆彩虹
董国
黄亚南
黄春勇
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Jiangsu Jiuhe Machinery Co ltd
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Abstract

The embodiment of the specification provides a standard knot use priority determining method and a system, wherein the method comprises the following steps: acquiring detection data of at least one standard section to be evaluated based on a detection unit; the detection unit at least comprises at least one of a moving mechanism, a manipulator mechanism and an image detection mechanism, and the detection data at least comprises image data; determining structural features and surface features of at least one standard segment to be evaluated based on the detection data; evaluating the availability of at least one standard segment to be evaluated based on the structural features and the surface features; determining a use priority of the at least one target standard section based on at least one of availability, production time and historical use data, general availability, time interval from a most recent ultrasonic flaw detection, and accumulated days of use for other projects within the time interval; wherein the versatility is determined based on maintenance risks when the target standard section is installed at different heights of the tower crane with different installation parameters.

Description

Standard knot use priority determining method and system
Description of the division
The application provides a divisional application aiming at China application with the application date of 2023, 04 month and 19 days, the application number of 202310423391.7 and the name of 'a standard knot availability management method, system, device and storage medium'.
Technical Field
The specification relates to the technical field of hoisting machinery, in particular to a method and a system for determining the use priority of a standard knot.
Background
The standard section plays an important role in equipment such as a crane, a tower crane, a lifter and the like, and mainly plays roles in height adjustment, support and installation of the tower crane. The proper standard section is very important for constructing the tower crane, so that the safety of the tower crane is improved, the standard section can be reasonably used, and the maximum use value is exerted.
In order to improve the safety of a tower crane, CN213416071U discloses a standard section management system of a tower crane, reads and uploads the use state information of the standard section, acquires the management data of the standard section, transmits the management data to a data display terminal for relevant management departments to review, and provides a standard section state information safety management means for the standard section of the tower crane. But not how to determine availability and usage priority of standard knots.
Therefore, it is desirable to provide a method and a system for determining the usage priority of the available standard knots and the usage priority of the available standard knots, so that the standard knots are more scientifically and reasonably selected to build the tower crane, the safety of the tower crane is ensured, the usage value of the standard knots is maximized, and the usage cost of the standard knots is reduced.
Disclosure of Invention
One or more embodiments of the present specification provide a standard knot use priority determining method, including: acquiring detection data of at least one standard section to be evaluated based on a detection unit; the detection unit at least comprises at least one of a moving mechanism, a manipulator mechanism and an image detection mechanism, and the detection data at least comprises image data; determining structural features and surface features of at least one standard segment to be evaluated based on the detection data; evaluating the availability of at least one standard segment to be evaluated based on the structural features and the surface features; and determining at least one target standard section based on the availability; determining a usage priority of the at least one target standard section based on at least one of the availability of the at least one target standard section, production time and historical usage data of the at least one target standard section, general usage of the at least one target standard section, a time interval of a most recent ultrasonic inspection of a distance of the at least one target standard section, and accumulated usage days of the at least one target standard section for other projects within the time interval, the general usage of the at least one target standard section being determined based on maintenance risk when the at least one target standard section is installed at different heights of a tower crane with different installation parameters.
One or more embodiments of the present specification provide a standard knot use priority determination system, including an acquisition module configured to acquire detection data of at least one standard knot to be evaluated based on a detection unit; the detection unit may include at least one of a moving mechanism, a manipulator mechanism, and an image detection mechanism, and the detection data may include at least image data; an extraction module configured to determine structural and surface features of at least one standard segment to be evaluated based on the detection data; an evaluation module configured to evaluate the availability of at least one standard segment to be evaluated based on the structural features and the surface features; a determining module configured to determine at least one target standard section based on the availability; determining a usage priority of the at least one target standard section based on at least one of the availability of the at least one target standard section, production time and historical usage data of the at least one target standard section, general usage of the at least one target standard section, a time interval of a most recent ultrasonic inspection of a distance of the at least one target standard section, and accumulated usage days of the at least one target standard section for other projects within the time interval, the general usage of the at least one target standard section being determined based on maintenance risk when the at least one target standard section is installed at different heights of a tower crane with different installation parameters.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a block diagram of a standard knot availability management system shown in accordance with some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a standard knot availability management method shown in accordance with some embodiments of the present description;
FIG. 3 is a schematic diagram of a risk prediction model shown in accordance with some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
FIG. 1 is a block diagram of a standard knot availability management system, shown in accordance with some embodiments of the present description.
In some embodiments, the standard knot availability management system 100 may include an acquisition module 110, an extraction module 120, an evaluation module 130, and a determination module 140. In some embodiments, one or more modules in the standard knot availability management system 100 may be interconnected. The connection may be wireless or wired.
The acquisition module 110 may acquire detection data of at least one standard segment to be evaluated based on the detection unit. The detection unit at least comprises at least one of a moving mechanism, a manipulator mechanism and an image detection mechanism. The detection data includes at least image data.
The extraction module 120 may determine structural features and surface features of at least one standard segment to be evaluated based on the detection data. In some embodiments, the surface features may include surface sub-features of at least one cell structure in the standard section to be evaluated.
The evaluation module 130 may evaluate the availability of at least one standard section to be evaluated based on the structural features and the surface features. In some embodiments, the evaluation module 130 may evaluate the availability of at least one standard segment to be evaluated based on the structural features and the surface sub-features of at least one unit structure. In some embodiments, the assessment module 130 may also determine the availability of at least one standard section under evaluation based on the most current ultrasonic inspection data of the at least one standard section under evaluation. In some embodiments, the assessment module 130 may also predict a maintenance risk of installing at least one target standard section on the tower crane based on the structural features and the surface sub-features of the at least one unit structure.
The determination module 140 may determine at least one target criteria section and its corresponding usage priority based on availability. In some embodiments, the determination module 140 may update the usage priority of the at least one target standard knot based on the maintenance risk. In some embodiments, the determination module 140 may also determine a usage priority of the at least one target standard knot based on the availability of the at least one target standard knot, the production time of the at least one target standard knot, and the historical usage data.
The relevant content can be seen in fig. 2,3 and their related description.
It should be understood that the system shown in fig. 1 and its modules may be implemented in a variety of ways. For example, in some embodiments, the system described in fig. 1 and its modules may be executed by a computing device, either jointly or independently, with two different CPUs and/or processors.
It should be noted that the above description of the standard knot availability management system and its modules is for convenience of description only and is not intended to limit the specification to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the acquisition module 110, the extraction module 120, the evaluation module 130, and the determination module 140 disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.
FIG. 2 is an exemplary flow chart of a standard knot availability management method shown in accordance with some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by the standard knot availability management system 100.
Step 210, obtaining detection data of at least one standard section to be evaluated based on the detection unit.
The detection unit can be used for detecting the standard section to be evaluated to obtain relevant detection data.
In some embodiments, the detection unit includes at least one of a movement mechanism, a robotic mechanism, and an image detection mechanism.
The moving mechanism refers to a movable device. Such as mobile robots, tracked chassis, etc. The moving mechanism can drive the manipulator mechanism and the image detection mechanism to move.
The robot mechanism refers to an apparatus for fixing the image detection mechanism. Such as a robotic arm, etc. The manipulator mechanism may be fixed to the moving mechanism.
The image detection mechanism refers to an apparatus for performing image detection. Such as cameras, etc. The image detection mechanism may be fixed to the manipulator mechanism.
In some embodiments, the detection unit may further comprise an ultrasonic detection mechanism. Such as ultrasonic flaw detectors, etc. The ultrasonic detection mechanism may be fixed to the manipulator mechanism.
The standard section to be evaluated refers to a standard section for which usability needs to be evaluated.
The detection data means data obtained by detection by the detection unit. For example, an image containing the standard section to be evaluated and the environment in which the standard section to be evaluated is located, and the like.
In some embodiments, the detection data includes at least image data.
The image data refers to data of an image related to a standard section to be evaluated. For example, data of a whole image or a partial image of a standard knot to be evaluated.
In some embodiments, the detection data may also include ultrasound data.
The detection data is obtained in a variety of ways. In some embodiments, the acquisition module 110 may acquire the detection data of the at least one standard segment to be evaluated based on the detection unit.
In some embodiments, the acquiring module 110 may acquire image data of at least one standard section to be evaluated by capturing all or part of the standard section to be evaluated from one or more different angles through an image detection mechanism.
In some embodiments, the acquisition module 110 may also scan the whole or part of the standard section to be evaluated from one or more angles by an ultrasound detection mechanism to acquire ultrasound data of at least one standard section to be evaluated.
In some embodiments, the obtaining module 110 may also obtain the detection data of at least one standard section to be evaluated by other means. For example, user input, etc.
Step 220, determining structural features and surface features of at least one standard segment to be evaluated based on the detection data.
Structural features refer to features that relate to the structure of the standard section being evaluated.
In some embodiments, the structural features include at least one of a quantity, a texture parameter, and a specification parameter of at least one unit structure in the standard section to be evaluated.
The unit structure refers to a structure constituting a standard section to be evaluated. Such as main chords, diagonal web members, straight web members, connection sleeves, steel sheet mesh, steps, ladders, etc.
In some embodiments, the unit structure may further include welds on the standard section to be evaluated. Different weld types are formed between different unit structures.
In some embodiments of the present disclosure, in determining the availability of the standard joint to be evaluated, not only the structural features and the surface sub-features of the unit structures constituting the standard joint to be evaluated, but also the surface sub-features of the weld joint are considered, thereby improving the accuracy of the availability.
The texture parameters may include color, texture, smoothness, transparency, luminosity, hardness, roughness, etc. of the cell structure surface.
The specification parameters may include volume, length, shape, weight, model, etc. of the unit structure.
The surface features refer to features reflecting the overall apparent surface condition of the standard section to be evaluated. For example, no deformation, no rust, no corrosion, etc.
In some embodiments, the surface features may include at least one of deformation features, crack features, rust features, and corrosion features.
The deformation feature is used to evaluate whether at least a portion of the standard knot is deformed. The deformation characteristics may include whether to deform, the severity of the deformation, and the like.
The crack signature is used to evaluate whether a crack exists in at least a portion of the standard joint. The deformation characteristics may include the presence or absence of a crack, crack severity, etc.
The rust feature is used to evaluate whether at least a portion of the standard knot is rusted. Rust characteristics may include whether rust has occurred, the severity of the rust, etc.
The corrosion characteristics are used to evaluate whether corrosion of at least a portion of the standard knot has occurred. Corrosion characteristics may include whether corrosion has occurred or not, and the severity of the corrosion.
In some embodiments, the surface features may include surface sub-features of at least one cell structure in the standard section to be evaluated.
The surface sub-feature refers to a feature reflecting the appearance surface condition of the unit structure constituting the standard knot to be evaluated. For example, the main chord is free of deformation, the main chord is free of rust, and the like. The surface sub-features may include at least one of deformation features, crack features, rust features, and corrosion features of the cell structure in the standard section to be evaluated.
The extraction module 120 may determine structural and surface features of at least one standard segment to be evaluated in a variety of ways based on the detection data. In some embodiments, the extraction module 120 may process a set of image data through two image recognition models (e.g., two convolutional neural network models), respectively, to determine structural features and surface features of the standard knot to be evaluated.
In some embodiments, the extraction module 120 may control the detection unit to shoot at least one unit structure of the standard section to be evaluated at different angles based on the identified structural features of the standard section to be evaluated, so as to obtain image data of the unit structure. In some embodiments, the extraction module 120 may process the obtained image data of the cell structure through an image recognition model to determine surface sub-features of the cell structure. Based on a similar approach, the structural sub-features of the cell structure may be determined.
In some embodiments, the extraction module 120 may train to obtain the image recognition model through a first training sample with a first tag. The first training sample may be historical image data of a standard knot, and the first label is a historical structural feature and a historical surface feature corresponding to the historical image data. In some embodiments, the first tag may be a history structure sub-feature and a history surface sub-feature corresponding to the history image data.
At step 230, the availability of at least one standard segment to be evaluated is evaluated based on the structural features and the surface features.
Availability refers to the criteria reflecting whether the standard section under evaluation is available for installing the tower crane. Including available or unavailable. Availability may be represented by a numerical value, e.g., 1 for availability and 0 for unavailability.
In some embodiments, the evaluation module 130 may evaluate the availability of at least one standard section to be evaluated in a variety of ways based on the structural features and the surface features. Such as vector database matching. In some embodiments, the evaluation module 130 may construct a first feature vector corresponding to the standard segment to be evaluated based on the structural features and the surface features of the standard segment to be evaluated. For example, the first feature vector p (x, y) may be constructed based on the structural features x and the surface features y of the standard section to be evaluated.
The database comprises a plurality of first reference vectors and reference availability corresponding to each first reference vector.
The first reference vector is constructed based on the structural features and the surface features corresponding to each sampling standard node, and the reference availability corresponding to the first reference vector is the availability of the corresponding sampling standard node. The construction method of the first reference vector refers to the construction method of the first feature vector.
In some embodiments, the evaluation module 130 may calculate a vector distance between the first reference vector and the first feature vector, respectively, and determine availability of the standard segment to be evaluated corresponding to the first feature vector. For example, a first reference vector whose vector distance from the first feature vector satisfies a preset condition is taken as a target vector, and the reference availability corresponding to the target vector is taken as the availability of the standard section to be evaluated corresponding to the first feature vector. The preset conditions may be set according to circumstances. For example, the preset condition may be that the vector distance is minimum or that the vector distance is less than a distance threshold, or the like.
In some embodiments, the evaluation module 130 may evaluate the availability of at least one standard segment to be evaluated based on the structural features and the surface sub-features of at least one unit structure.
In some embodiments, the evaluation module 130 may evaluate the availability of at least one standard segment to be evaluated in a variety of ways based on the structural features and the surface sub-features of the at least one unit structure. Such as vector database matching.
In some embodiments, the evaluation module 130 may construct a second feature vector corresponding to the standard segment to be evaluated based on the structural features of the standard segment to be evaluated and the surface sub-features of the at least one unit structure. For example, the second feature vector q constructed based on the structural feature x corresponding to the standard section to be evaluated, the surface sub-feature y 1 of the unit structure 1, the surface sub-features y 2, … … of the unit structure 2, and the surface sub-feature y n of the unit structure n may be (x, y 1,y2,……,yn).
In some embodiments, the database may further include a plurality of second reference vectors and reference availability corresponding to each of the second reference vectors. The second reference vector is constructed based on the corresponding structural features of each sampling standard section and the surface sub-features of at least one unit structure. The construction of the second reference vector is referred to as the construction of the second feature vector.
In some embodiments, the evaluation module 130 may calculate the vector distance between the second reference vector and the second feature vector, respectively, and determine the availability of the standard segment to be evaluated corresponding to the second feature vector. For more on the determination of the availability of the standard section to be evaluated based on the second reference vector and the second feature vector, reference is made to the above-mentioned related description of the determination of the availability of the standard section to be evaluated based on the first reference vector and the first feature vector.
In some embodiments, the availability of the at least one standard section under evaluation is also related to the most current ultrasound inspection data of the at least one standard section under evaluation.
The ultrasonic flaw detection data are flaw detection data obtained by detecting standard sections to be evaluated by adopting equipment such as an ultrasonic flaw detector. The latest ultrasonic flaw detection data refers to ultrasonic flaw detection data which is the latest from the current time. The ultrasonic flaw detector detects standard knots to be evaluated in a certain period.
In some embodiments, the evaluation module 130 may evaluate the availability of the standard section to be evaluated in a variety of ways based on the structural features, the surface sub-features of at least one structural element, and the most current ultrasonic inspection data.
In some embodiments, the evaluation module 130 may evaluate the availability of the standard knot to be evaluated in a preset manner based on the structural features, the surface sub-features of at least one structural unit, and the latest ultrasound inspection data. If the latest ultrasonic flaw detection data are preset to indicate that the inside of the structure of the standard section to be evaluated is damaged and is larger than the damage threshold, the availability of the standard section to be evaluated is unavailable. Wherein the damage threshold may be empirically determined.
In some embodiments, the evaluation module 130 may evaluate the availability of the standard knot to be evaluated by the vector database based on the structural features, the surface sub-features of at least one structural element, and the most recent ultrasonic inspection data.
In some embodiments, the evaluation module 130 may construct a third feature vector corresponding to the standard section to be evaluated based on the cell features of each cell structure in the standard section to be evaluated and the latest ultrasonic inspection data, and determine availability of the standard section to be evaluated corresponding to the third feature vector based on the third reference vector and the database. The elements of the third feature vector may include elements included in the second feature vector and elements corresponding to the latest ultrasonic flaw detection data, and the construction mode of the third feature vector refers to the construction mode of the second feature vector, which is not described herein. The database may also contain a plurality of third reference vectors and reference availability corresponding to each third reference vector. For more on the determination of the availability of the standard section to be evaluated based on the third reference vector and the third feature vector, reference is made to the above-mentioned related description of the determination of the availability of the standard section to be evaluated based on the first reference vector and the first feature vector.
According to the method and the device for determining the availability of the standard section to be evaluated based on the structural features, the surface sub-features of at least one structural unit and the latest ultrasonic flaw detection data, defects inside the structure of the standard section to be evaluated are comprehensively considered, and the determined availability of the standard section to be evaluated is more in line with actual conditions.
Step 240, based on the availability, determines at least one target criteria section and its corresponding usage priority.
The target standard section refers to a standard section to be evaluated, the availability of which meets preset conditions. For example, the preset condition may be availability or 1.
In some embodiments, the determination module 140 may determine one or more standard sections to be evaluated whose availability satisfies a preset condition as at least one target standard section.
The usage priority refers to the usage priority of the target standard section.
In some embodiments, the determination module 140 may determine the usage priority of the at least one target standard knot based on the availability of the at least one target standard knot. For example, the same preset value may be given priority of use of the target standard section whose availability is available or 1.
In some embodiments, the assessment module 130 may also predict a maintenance risk of installing at least one target standard section on the tower crane based on the structural features and the surface sub-features of the at least one unit structure. Further, the determination module 140 may update the usage priority of the at least one target standard knot based on the maintenance risk.
Maintenance risk refers to the probability of maintenance of at least one target standard section mounted on the tower crane.
In some embodiments, the maintenance risk may include a replacement risk and a reinforcement risk.
The risk of replacement refers to the probability of replacing at least one target standard section mounted on the tower crane.
Reinforcement risk refers to the probability of reinforcing at least one target standard section mounted on the tower crane.
In some embodiments, for any one of the at least one target standard section, its corresponding maintenance risk may be represented as a maintenance risk profile.
In some embodiments, the maintenance risk profile may include maintenance risk when any of the target standard knots are installed at different heights of the tower machine with different installation parameters. See below for description of installation parameters.
In some embodiments, the assessment module 130 may construct a maintenance risk profile in various ways based on the maintenance risk of any target standard knot installed at different heights of the tower crane. For example, the manner in which the sequence is constructed, and the like.
In some embodiments, the assessment module 130 may predict the maintenance risk of installing at least one target standard section on the tower crane in a variety of ways based on the structural features and the surface sub-features of the at least one unit structure. Such as vector database matching. The database may further include reference maintenance risks corresponding to the respective reference vectors (e.g., the first reference vector, the second reference vector, and the third reference vector), and the maintenance risks may be determined simultaneously when determining the availability, and more reference is made to the related description.
In some embodiments, the assessment module 130 may also determine a maintenance risk of the at least one target standard knot installed on the tower crane via a risk prediction model. See fig. 3 for more description and related description.
In some embodiments, the determination module 140 may update the usage priority of the at least one target standard knot in a number of ways based on the maintenance risk. For example, when the maintenance risk is greater than the risk threshold, the usage priority is lowered. The risk threshold may be preset by the system.
In some embodiments of the present disclosure, the method for updating the usage priority of the target standard section based on the maintenance risk further considers the possible maintenance cost after using the target standard section, so as to avoid using the standard section with a larger maintenance cost.
In some embodiments, the determination module 140 may also determine a usage priority of the at least one target standard knot based on the availability of the at least one target standard knot, the production time of the at least one target standard knot, and the historical usage data.
Production time refers to the time that the target standard knot is produced.
The history use data refers to history data that the target standard section was used in the past. For example, the number of days of use is accumulated.
In some embodiments, the determination module 140 may determine the usage priority of the at least one target standard knot in a number of ways based on the availability of the at least one target standard knot, the production time of the at least one target standard knot, and the historical usage data. For example, the closer the production time of the target standard knot is to the current time, the smaller the cumulative number of days of use of the target standard knot is, and the higher the priority of use of the target standard knot is.
In some embodiments of the present disclosure, a method for determining a usage priority based on a production time and historical usage data of at least one target standard knot may enable the determined usage priority to be more accurate, and facilitate reasonable usage of the target standard knot.
In some embodiments, the priority of use of the at least one target standard node is further related to a time interval of a most recent ultrasonic inspection of the distance of the at least one target standard node, and a cumulative number of days of use of the at least one target standard node for other projects during the time interval. For example, the greater the time interval of the latest ultrasonic flaw detection of the target standard knot, the more the target standard knot is used for the accumulated number of days of other projects in the time interval, and the lower the use priority of the target standard knot.
In some embodiments of the present disclosure, based on a time interval of an ultrasonic flaw detection of a latest distance from at least one target standard node, and a priority of use of at least one target standard node for accumulated days of use of other projects in the time interval, an influence of use of the ultrasonic flaw detection on the target standard node is considered, so that the determined priority of use accords with a practical situation more.
In some embodiments, the usage priority of the at least one target standard knot is also related to the versatility of the at least one target standard knot. For example, the usage priority of the target criteria section is inversely related to the versatility.
The universality refers to the possibility that the target standard section is used for different projects. For example, a greater versatility indicates that the target standard section is applicable to a greater variety of projects.
In some embodiments, the versatility of the at least one target standard knot may be determined based on maintenance risks when the at least one target standard knot is installed at different heights of the tower crane with different installation parameters.
The installation parameters refer to parameters for installing at least one target standard knot. For example, the number of upper standard knots, the number of lower standard knots, the weight bearing of the installed target standard knots, etc.
The upper standard knot number refers to the number of standard knots located above the installed target standard knot. The lower standard knot number refers to the number of standard knots located below the installed target standard knot.
The tower crane height may be measured by the number of lower standard knots of the target standard knots.
In some embodiments, for multiple maintenance risks when a target standard knot is installed at different heights of the tower crane with different installation parameters, the assessment module 130 may determine the versatility of the target standard knot based on the number of maintenance risks therein that are less than a preset threshold. For example, the greater the number, the greater the versatility.
The preset threshold value can be determined through a preset table according to the structural characteristics of the target standard section. The preset table may be determined based on a priori knowledge or historical data.
In some embodiments of the present disclosure, the usage priority is determined by considering the popularity, and the popularity is determined based on the maintenance risks of the target standard sections installed at different heights of the tower crane, and the larger popularity indicates that the target standard sections can be installed at more heights of the tower crane, and the usage priority of the target standard sections is inversely related to the popularity, so that the target standard sections with smaller popularity are preferentially used, and the target standard sections with larger popularity are left, so that the situation that no standard section is available in new engineering is avoided.
In some embodiments of the present disclosure, structural features and surface features are considered by the above standard knot availability management method, so that the standard knots meeting the availability are safer and more reasonable to use.
It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.
FIG. 3 is a schematic diagram of a risk prediction model according to some embodiments of the present description.
In some embodiments, the assessment module 130 may determine the maintenance risk 330 of any of the at least one target standard knot installed at different heights of the tower crane via the risk prediction model 320.
The risk prediction model 320 may be a machine learning model. Such as convolutional neural network models, and the like.
In some embodiments, the inputs to the risk prediction model 320 include at least structural features 311 of at least one target standard section, surface sub-features 312 of at least one unit structure, and different installation parameters 313, and the output may be a maintenance risk 330 of any one of the at least one target standard section when installed at different heights of the tower crane.
In some embodiments, the installation parameters 313 entered into the risk prediction model 320 may include an upper standard knot number 313-1, a lower standard knot number 313-2, and a load bearing 313-3 of the installed target standard knot of the at least one target standard knot. In some embodiments, the maintenance risk 330 output by the risk prediction model 320 may include a replacement risk 331 and a reinforcement risk 332. For more details regarding maintenance risk, see step 240 and its associated description.
In some embodiments, the input of the risk prediction model 320 further includes up-to-date ultrasound inspection data 314 for at least one target standard knot. For more information on the latest ultrasound inspection data see step 230 and its associated description.
In some embodiments, risk prediction model 320 includes an embedding layer 321 and a determining layer 323. Embedding layer 321 may determine an embedding vector 322 based on structural features 311 of at least one target standard knot, surface sub-features 312 of at least one unit structure. The determined embedded vector 322 may be used as an input to a determination layer 323.
The determination layer 323 may determine the maintenance risk 330 for each target standard section installed at different heights of the tower crane based on the embedded vector 322 and the different installation parameters 313.
In some embodiments, the input to the determination layer 323 may also include up-to-date ultrasound inspection data 314 for at least one target standard knot.
In some embodiments, parameters of embedding layer 321 and determining layer 323 may be obtained by joint training. In some embodiments, the second training sample for training risk prediction model 320 may include sample structural features of a sample standard knot, sample surface sub-features of at least one sample unit structure, a plurality of sample installation parameters, and sample up-to-date ultrasound inspection data. In some embodiments, the second label corresponding to the second training sample may include actual maintenance risk when the sample standard knot is installed at different heights of the tower crane with different sample installation parameters.
An exemplary joint training process includes: inputting the sample structure characteristics and the sample surface sub-characteristics of at least one sample unit structure into an initial embedding layer to obtain an embedding vector output by the initial embedding layer; inputting the embedded vector, a plurality of sample installation parameters and the latest ultrasonic flaw detection data of the samples into an initial determination layer to obtain maintenance risks when the sample standard sections output by the initial determination layer are installed at different heights of the tower crane; and constructing a loss function based on the output of the second label and the initial determination layer, and synchronously updating parameters of the initial embedding layer and the initial determination layer until the preset condition is met and training is completed. The preset condition may be that the loss function is smaller than a threshold, converges, or the training period reaches the threshold. Through parameter updating, a trained embedded layer 321 and a deterministic layer 323 are obtained.
In some embodiments of the present description, it can be seen that the features affecting the usage priority of the target standard section are more numerous, including structural features, surface features, installation parameters, and up-to-date ultrasonic inspection data. If the usage priority of the target standard section is determined by adopting a simple rule, the usage priority is limited by the complexity of the target standard section, can only be based on fewer characteristics, is limited by manually specified rules, cannot consider all the characteristics, and is difficult to determine more accurate usage priority. In some embodiments of the present disclosure, a risk prediction model is used to predict maintenance risks when the target standard section is installed at different heights of the tower crane based on a large number of extensive features, so that the predicted maintenance risks have higher accuracy, and further the determined use priority of the target standard section is more in line with the actual situation, so that the target standard section is more reasonably used.
Some embodiments of the present specification also provide a standard knot availability management device. In some embodiments, an apparatus may include at least one processor and at least one memory, the memory may be configured to store computer instructions, and the at least one processor may be configured to execute at least some of the computer instructions to implement a standard node availability management method.
Some embodiments of the present description also provide a computer-readable storage medium. In some embodiments, the storage medium may store computer instructions that, when executed by the processor, may implement a standard node availability management method.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (4)

1. A method for determining a priority of use of a standard knot, comprising:
acquiring detection data of at least one standard section to be evaluated based on a detection unit; the detection unit at least comprises at least one of a moving mechanism, a manipulator mechanism and an image detection mechanism, and the detection data at least comprises image data;
Determining structural features and surface features of the at least one standard segment to be evaluated based on the detection data; the surface features comprise surface sub-features of at least one unit structure in the standard section to be evaluated, the unit structure refers to a structure forming the standard section to be evaluated, and the unit structure comprises welding seams on the standard section to be evaluated;
Constructing a first feature vector corresponding to the standard section to be evaluated based on the structural features and the surface features;
searching in a vector database based on the first feature vector, and determining a first reference vector of which the vector distance meets a preset condition; the vector database comprises a plurality of first reference vectors and reference availability corresponding to the first reference vectors, and the first reference vectors are constructed based on the structural features and the surface features corresponding to the sampling standard nodes;
Determining the reference availability corresponding to the first reference vector, of which the vector distance meets the preset condition, as the availability of the standard section to be evaluated corresponding to the first feature vector;
determining one or more standard sections to be evaluated, of which the usability meets preset conditions, as at least one target standard section;
Assigning a usage priority of the at least one target standard knot based on the availability of the at least one target standard knot;
Determining maintenance risk of installing the at least one target standard knot on a tower crane through a risk prediction model based on the structural features, the surface sub-features of the at least one unit structure and different installation parameters, wherein the risk prediction model is a machine learning model; the maintenance risk is expressed as a maintenance risk distribution including the maintenance risk when any one of the target standard knots is installed at different heights of the tower crane with the different installation parameters;
in response to the maintenance risk being greater than a risk threshold, the usage priority of the at least one target standard knot is reduced.
2. The method of claim 1, wherein the risk prediction model comprises an embedding layer and a determining layer,
Wherein the input of the embedding layer comprises the structural features, the surface sub-features of the at least one cell structure, and the output comprises an embedding vector;
The input of the determining layer comprises the embedded vector and the installation parameters, and the output comprises the maintenance risks when the target standard knots are installed at different heights of the tower crane.
3. A standard knot usage priority determination system, the system comprising:
The acquisition module is configured to acquire detection data of at least one standard section to be evaluated based on the detection unit; the detection unit at least comprises at least one of a moving mechanism, a manipulator mechanism and an image detection mechanism, and the detection data at least comprises image data;
An extraction module configured to determine structural and surface features of the at least one standard segment to be evaluated based on the detection data; the surface features comprise surface sub-features of at least one unit structure in the standard section to be evaluated, the unit structure refers to a structure forming the standard section to be evaluated, and the unit structure comprises welding seams on the standard section to be evaluated;
An evaluation module configured to:
Constructing a first feature vector corresponding to the standard section to be evaluated based on the structural features and the surface features;
searching in a vector database based on the first feature vector, and determining a first reference vector of which the vector distance meets a preset condition; the vector database comprises a plurality of first reference vectors and reference availability corresponding to the first reference vectors, and the first reference vectors are constructed based on the structural features and the surface features corresponding to the sampling standard nodes;
determining the reference availability corresponding to the first reference vector, of which the vector distance meets the preset condition, as the availability of the standard section to be evaluated corresponding to the first feature vector; and
A determination module configured to:
determining one or more standard sections to be evaluated, of which the usability meets preset conditions, as at least one target standard section;
Assigning a usage priority of the at least one target standard knot based on the availability of the at least one target standard knot;
Determining maintenance risk of installing the at least one target standard knot on a tower crane through a risk prediction model based on the structural features, the surface sub-features of the at least one unit structure and different installation parameters, wherein the risk prediction model is a machine learning model; the maintenance risk is expressed as a maintenance risk distribution including the maintenance risk when any one of the target standard knots is installed at different heights of the tower crane with the different installation parameters;
in response to the maintenance risk being greater than a risk threshold, the usage priority of the at least one target standard knot is reduced.
4. The system of claim 3, wherein the risk prediction model comprises an embedding layer and a determining layer,
Wherein the input of the embedding layer comprises the structural features, the surface sub-features of the at least one cell structure, and the output comprises an embedding vector;
The input of the determining layer comprises the embedded vector and the installation parameters, and the output comprises the maintenance risks when the target standard knots are installed at different heights of the tower crane.
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