Cargo boom size extraction method based on multi-dimensional algorithm, medium and program product

Abstract

The invention discloses a cargo boom size extraction method based on a multi-dimensional algorithm, a medium and a program product, the cargo boom size extraction method comprises the steps that a cargo boom three-dimensional model is obtained, the cargo boom three-dimensional model comprises a plurality of sub-models, and each sub-model corresponds to a component; on the basis of a preset target component recognition algorithm, a sub-model of a target component is recognized from the three-dimensional model of the cargo boom; and based on a preset geometric feature recognition algorithm, recognizing a sub-model with a target geometric feature from the sub-models of the target component, and further calculating a target size. According to the method, the efficiency and accuracy of extracting the critical dimension of the cargo boom are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of boom size extraction, and in particular relates to a boom size extraction method, a medium and a program product based on a multi-dimensional algorithm. Background Art

[0002] In the design and manufacturing process of truck cranes, the key dimension information of the boom is essential to ensure the performance and safety of the crane. The traditional dimension extraction method relies on manual measurement and drawing, which is not only inefficient but also prone to errors. With the development of computer-aided design technology, the demand for automated extraction of key dimensions of the boom is growing.

[0003] Therefore, proposing an automated crane arm size extraction method has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above problems, the present invention proposes a boom dimension extraction method, medium and program product based on a multi-dimensional algorithm, which improves the efficiency and accuracy of boom key dimension extraction.

[0005] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a method for extracting boom dimensions based on a multi-dimensional algorithm, comprising: Acquire a three-dimensional model of a crane arm, wherein the three-dimensional model of the crane arm includes a plurality of sub-models, each sub-model corresponding to a component; Based on a preset target component recognition algorithm, a sub-model of a target component is identified from the three-dimensional model of the boom; Based on a preset geometric feature recognition algorithm, a sub-model having a target geometric feature is identified from the sub-models of the target component, and then the target size is calculated.

[0006] In combination with the first aspect, optionally, the target component identification algorithm includes a model tree identification algorithm, a parameter identification algorithm and / or a model space position calculation algorithm.

[0007] In combination with the first aspect, optionally, the model tree recognition algorithm performs the following steps on the three-dimensional model of the boom: Obtaining a pre-built model tree, wherein the model tree is built based on a hierarchical relationship between components in a standard model of a crane arm; According to the model tree, a sub-model of a target component is identified from the three-dimensional model of the boom.

[0008] In combination with the first aspect, optionally, the parameter identification algorithm performs the following steps on the three-dimensional model of the boom: According to a preset model drawing number naming rule, a sub-model of a target component is identified from the three-dimensional model of the boom; Or, according to a preset corresponding table of components in the boom and their weight intervals, the three-dimensional model of the boom is searched according to weight polling comparison to identify the sub-model of the target component; Or, according to a preset corresponding table of components in the boom and their volume intervals, the three-dimensional model of the boom is searched according to volume polling comparison to identify a sub-model of the target component; Or, according to a preset corresponding table of components in the boom and their area intervals, the three-dimensional model of the boom is searched according to area polling comparison to identify the sub-model of the target component; And / or, according to a preset corresponding table of components in the boom and their attributes, the three-dimensional model of the boom is searched according to attribute polling comparison to identify the sub-model of the target component.

[0009] In combination with the first aspect, optionally, the model space position calculation algorithm performs the following steps on the three-dimensional model of the boom: Based on the crane origin, calculating the relative position of each sub-model in the three-dimensional model of the crane arm; Based on the relative positions of the sub-models, the sub-model of the target component is identified from the three-dimensional model of the boom.

[0010] In combination with the first aspect, optionally, if the target geometric feature is a plane, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are planes; All the identified sub-models of geometric features are sorted in descending order of area, and the sorting results are compared with the preset corresponding table of the components and plane elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the direction offset from the boom origin coordinate system, and the sorting results are compared with the preset corresponding table of each component in the boom and its plane elements to identify the sub-model with the target geometric features; The corresponding table of each component and its plane element is constructed by the following steps: Obtain all plane elements of each component, wherein the plane elements include area and a spatial normal of the plane relative to the origin of the boom; First, the plane elements are sorted in descending order according to the area, and then the plane elements are sorted in descending order according to the direction offset from the crane arm origin coordinate system to generate a corresponding table of each component and its plane element.

[0011] In combination with the first aspect, optionally, if the target geometric feature is a cylinder, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are cylinders; All the identified sub-models of geometric features are sorted in descending order of area, and the sorting results are compared with the preset corresponding table of the components and cylindrical elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the cylinder diameter, and the sorting results are compared with the preset corresponding table of each component in the boom and its cylinder element to identify the sub-model with the target geometric features; Among them, the corresponding table of each component and its cylindrical element is constructed by the following steps: Obtain all cylinder elements of each component, wherein the cylinder elements include area, cylinder diameter, and cylinder height; First, the cylindrical elements are sorted in descending order according to the area, and then the cylindrical elements are sorted in descending order according to the cylinder diameter, and a corresponding table of each component and its cylindrical element is generated.

[0012] In combination with the first aspect, optionally, if the target geometric feature is a hole, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are holes; All the sub-models of the identified geometric features are sorted in descending order according to the nominal size, and the sorting results are compared with the preset corresponding table of the components and their hole elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the spatial offset distance of the hole axis relative to the origin coordinate system, and the sorting results are compared with the preset corresponding table of each component in the boom and its hole element to identify the sub-model with the target geometric features; The corresponding table of each component and its hole element is constructed by the following steps: Obtain all hole elements of each component, wherein the hole elements include nominal size, hole center axis direction, and space offset distance of the hole axis relative to the origin coordinate system; First, the hole elements are sorted in descending order according to the nominal size, and then sorted in descending order based on the spatial offset distance of the hole axis relative to the origin coordinate system to generate a corresponding table of each component and its hole element.

[0013] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for extracting boom dimensions based on a multi-dimensional algorithm as described in any one of the first aspects.

[0014] In a third aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the method for extracting boom dimensions based on a multi-dimensional algorithm as described in any one of the first aspects.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention adopts multi-dimensional algorithms including model tree recognition algorithm, parameter recognition algorithm and model space position calculation algorithm to identify the sub-model of the target component. The model tree recognition algorithm extracts the model tree based on the relationship between the boom components, and can accurately locate the relevant components. The parameter recognition algorithm extracts through various corresponding tables such as drawing number naming rules, weight, volume, area and attributes, ensuring the accuracy of component recognition from different angles. The model space position calculation algorithm extracts by calculating the relative position of the sub-model based on the crane origin, further improving the recognition accuracy. The comprehensive use of multiple algorithms avoids the limitations of a single algorithm, improves the accuracy of the target component sub-model recognition, and thus lays the foundation for the accurate extraction of key dimensions. The extracted target component sub-model is further processed by the geometric feature recognition algorithm. For different geometric features such as planes, cylinders, holes, etc., corresponding tables of corresponding components and their geometric elements are respectively constructed and sorted according to specific rules. In this way, the sub-model with the target geometric features is accurately identified, and then the target size is accurately calculated, thereby improving the accuracy of the key dimension extraction of the boom. These algorithms can quickly process the three-dimensional model of the crane arm without the need for manual search and measurement one by one, reducing the time of manual intervention and improving the efficiency of extracting key dimensions of the crane arm.

[0016] Traditional methods rely on manual measurement and drawing to extract boom dimensions, and manual operation is prone to human errors such as measurement errors and drawing errors. The present invention realizes automated extraction based on a multi-dimensional algorithm, automatically extracts the target component sub-model and the sub-model with the target geometric features from the three-dimensional model of the boom and calculates the dimensions. The entire process does not require manual measurement and drawing, avoiding the occurrence of human errors, thereby improving the quality and reliability of the product.

[0017] During the design phase of a truck crane, the present invention can quickly and accurately extract the key dimensions of the boom. Designers do not need to spend a lot of time on manual measurement and calculation as in traditional methods, and can directly obtain accurate dimension information, which speeds up the design process. Accurate boom dimension information ensures that the dimensions of parts meet the requirements during the manufacturing process, reduces rework and scrap caused by dimensional errors, and reduces production costs. At the same time, speeding up the design process also indirectly reduces time and labor costs, thereby reducing the design and manufacturing costs of the entire truck crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor, among which: Figure 1 The present invention is a flowchart of a method for extracting boom dimensions based on a multi-dimensional algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention. Example 1

[0021] A method for extracting boom dimensions based on a multi-dimensional algorithm comprises the following steps: (1) Obtaining a three-dimensional model of a crane arm, wherein the three-dimensional model of the crane arm includes a plurality of sub-models, each sub-model corresponding to a component; (2) identifying a sub-model of the target component from the three-dimensional model of the boom based on a preset target component recognition algorithm; (3) Based on a preset geometric feature recognition algorithm, a sub-model having a target geometric feature is identified from the sub-models of the target component, and then the target size is calculated.

[0022] In a specific implementation of the embodiment of the present invention, the target component recognition algorithm includes a model tree recognition algorithm, a parameter recognition algorithm and / or a model space position calculation algorithm. In the specific implementation process, the target component recognition algorithm includes at least two algorithms among the model tree recognition algorithm, the parameter recognition algorithm and the model space position calculation algorithm, and only when the sub-models of the target component recognized by the two algorithms are consistent, it means that the recognition is successful, otherwise it means that the recognition fails and the sub-model of the target component needs to be re-recognized to ensure the accuracy of the extraction result.

[0023] In a specific implementation of the embodiment of the present invention, the model tree recognition algorithm performs the following steps on the three-dimensional model of the boom: Obtaining a pre-built model tree, wherein the model tree is built based on the relationship between components in the boom; According to the model tree, a sub-model of a target component is identified from the three-dimensional model of the boom.

[0024] In the specific implementation process, the specific method of using the model tree includes: (1) Establish a hierarchical structure of the crane's three-dimensional model, construct a multi-dimensional positional relationship of the crane arm in the crane model tree, solidify the position of the crane arm in the whole machine model, solidify the relationship between the crane's various boom sections, and ensure that the positions of the various booms under the boom assembly in the model tree are arranged in order.

[0025] Or, (2) establish a naming standard for the whole machine + first-level component + second-level component + location information, and identify the order of the model tree: inspect the boom assembly model from top to bottom, the first component is defined as the first-section boom assembly; the second component is defined as the second-section boom assembly, and so on. After the support is positioned on the boom, identify the first-level sub-components in the boom as a basis for locating the target object.

[0026] In a specific implementation of the embodiment of the present invention, the parameter identification algorithm performs the following steps on the three-dimensional model of the boom: According to the preset model drawing number naming rules, the sub-model of the target component is identified from the three-dimensional model of the crane arm; in the specific implementation process, the model drawing number naming rules are established, each model has its own drawing number, which is stored in the form of a file name, and the drawing number naming rules of each boom section in the crane, the drawing number naming rules of the cylinder and the boom head and boom tail in each boom section, and the drawing number change rules after the model changes are formulated, and attribute features are added to the naming. Keywords in the drawing number are used to assist in identifying and locating the model; Or, according to the preset corresponding table of each component and its weight range in the boom, the three-dimensional model of the boom is searched according to the weight polling comparison, and the sub-model of the target component is identified; in the specific implementation process, since the boom in the crane is basically made of steel with high density, and the weight difference between different parts is obvious, a rule is established to assist in locating the target object by obtaining the weight parameters of the model, and a corresponding table of each component and its weight range is formulated, and the model is searched according to the weight polling comparison. At the same time, a maximum value screening method is established to quickly locate a boom assembly, a boom tail welded body, and a lower groove plate. The rule is to quickly find the first-level sub-component with the largest weight under the boom assembly and define it as a boom assembly, quickly find the heaviest model in each boom and define it as the boom tail welded body, and quickly find the heaviest model in the cylinder assembly and define it as the lower groove plate.

[0027] Or, according to a preset corresponding table of components in the boom and their volume intervals, the three-dimensional model of the boom is searched according to volume polling comparison to identify a sub-model of the target component; Or, according to the preset corresponding table of components and their area intervals in the boom, the three-dimensional model of the boom is searched according to the area polling comparison, and the sub-model of the target component is identified; in the specific implementation process, for example, for the plate model, a judgment rule based on the area is formulated, such as the area of ​​the main surface of the cylinder is larger than the area of ​​the main surface of the reinforcement, etc., and the solidification area comparison method is used to distinguish and locate by comparing the area of ​​the main surface of the model; And / or, according to the preset corresponding table of each component and its attribute in the boom, the three-dimensional model of the boom is searched according to the attribute polling comparison, and the sub-model of the target component is identified. In the specific implementation process, a classification tree of the boom model is established, and an attribute library is established based on the classification attributes, and rubber and plastic parts are quickly excluded by identifying the boom section model and material attributes.

[0028] In the embodiment of the present invention, multiple parameter recognition algorithms are provided to realize the recognition of the sub-model of the target component, in order to prevent certain model tree recognition algorithms from being unable to realize the recognition of the sub-model of the target component, and to improve the stability of the boom size extraction method in the embodiment of the present invention; In a specific implementation of the embodiment of the present invention, the model space position calculation algorithm performs the following steps on the three-dimensional model of the boom: Based on the crane origin, calculating the relative position of each sub-model in the three-dimensional model of the crane arm; Based on the relative position of each sub-model, the sub-model of the target component is identified from the three-dimensional model of the crane arm. In the specific implementation process, after screening the list of target objects, an identification method based on the spatial position of the model is established to locate the target model: a position calculation method for each main model of the boom is established, the origin (center point) of the crane is defined, the relative position of the model is calculated, and the model is identified, such as the boom head is located at the upper left of the boom tail; the boom head vertical plate farthest from the crane origin is the boom head outer vertical plate, and the closest one is the boom head inner vertical plate.

[0029] In a specific implementation of the embodiment of the present invention, if the target geometric feature is a plane, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are planes; All the identified sub-models of geometric features are sorted in descending order of area, and the sorting results are compared with the preset corresponding table of the components and plane elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the direction offset from the boom origin coordinate system, and the sorting results are compared with the preset corresponding table of each component in the boom and its plane elements to identify the sub-model with the target geometric features; The corresponding table of each component and its plane element is constructed by the following steps: Obtain all plane elements of each component, wherein the plane elements include area and a spatial normal of the plane relative to the origin of the boom; First, the plane elements are sorted in descending order according to the area, and then the plane elements are sorted in descending order according to the direction offset from the crane arm origin coordinate system to generate a corresponding table of each component and its plane element.

[0030] In a specific implementation of the embodiment of the present invention, if the target geometric feature is a cylinder, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are cylinders; All the identified sub-models of geometric features are sorted in descending order of area, and the sorting results are compared with the preset corresponding table of the components and cylindrical elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the cylinder diameter, and the sorting results are compared with the preset corresponding table of each component in the boom and its cylinder element to identify the sub-model with the target geometric features; Among them, the corresponding table of each component and its cylindrical element is constructed by the following steps: Obtain all cylinder elements of each component, wherein the cylinder elements include area, cylinder diameter, and cylinder height; First, the cylindrical elements are sorted in descending order according to the area, and then the cylindrical elements are sorted in descending order according to the cylinder diameter, and a corresponding table of each component and its cylindrical element is generated.

[0031] In a specific implementation of the embodiment of the present invention, if the target geometric feature is a hole, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are holes; All the sub-models of the identified geometric features are sorted in descending order according to the nominal size, and the sorting results are compared with the preset corresponding table of the components and their hole elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the spatial offset distance of the hole axis relative to the origin coordinate system, and the sorting results are compared with the preset corresponding table of each component in the boom and its hole element to identify the sub-model with the target geometric features; The corresponding table of each component and its hole element is constructed by the following steps: Obtain all hole elements of each component, wherein the hole elements include nominal size, hole center axis direction, and space offset distance of the hole axis relative to the origin coordinate system; First, the hole elements are sorted in descending order according to the nominal size, and then sorted in descending order according to the spatial offset distance of the hole axis relative to the origin coordinate system, and a corresponding table of each component and its hole element is generated.

[0032] A method for extracting boom dimensions based on a multi-dimensional algorithm in an embodiment of the present invention is described in detail below in conjunction with a specific implementation.

[0033] like Figure 1 As shown, the boom size extraction method based on the multi-dimensional algorithm includes: Step 1: Obtain 3D model Specifically, a 3D model of the boom of the truck crane is created or imported in Creo.

[0034] Step 2: Multi-dimensional algorithm to identify objects Multi-dimensional algorithms are designed, including model tree recognition algorithm, parameter recognition algorithm, model space position calculation algorithm, and geometric feature recognition algorithm, to quickly identify sub-models of target components and sub-models with target geometric features.

[0035] Step 3: Size Extraction Based on the extracted sub-model of the target geometric features, the target size of the crane arm is calculated.

[0036] Step 4: Data verification and optimization Verify the extracted target size to ensure the accuracy and reliability of the data, and make necessary optimization adjustments.

[0037] Step 5: Output the results The extracted target dimensions are output in digital or graphical form for use in design and manufacturing. Example 2

[0038] In an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for extracting boom dimensions based on a multi-dimensional algorithm as described in any one of Embodiment 1 is implemented. Example 3

[0039] A computer program product is provided in an embodiment of the present invention, including a computer program / instruction, which, when executed by a processor, implements the method for extracting boom dimensions based on a multi-dimensional algorithm as described in any one of Embodiment 1.

[0040] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0041] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0042] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0044] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

[0045] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for extracting boom dimensions based on a multi-dimensional algorithm, characterized in that: include: Acquire a three-dimensional model of a crane arm, wherein the three-dimensional model of the crane arm includes a plurality of sub-models, each sub-model corresponding to a component; Based on a preset target component recognition algorithm, a sub-model of a target component is identified from the three-dimensional model of the boom; Based on a preset geometric feature recognition algorithm, a sub-model having a target geometric feature is identified from the sub-models of the target component, and then the target size is calculated.

2. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 1, characterized in that: The target component recognition algorithm includes a model tree recognition algorithm, a parameter recognition algorithm and / or a model space position calculation algorithm.

3. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 2, characterized in that: The model tree recognition algorithm performs the following steps on the three-dimensional model of the boom: Obtaining a pre-built model tree, wherein the model tree is built based on a hierarchical relationship between components in a standard model of a crane arm; According to the model tree, a sub-model of a target component is identified from the three-dimensional model of the boom.

4. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 2, characterized in that: The parameter identification algorithm performs the following steps on the three-dimensional model of the boom: According to a preset model drawing number naming rule, a sub-model of a target component is identified from the three-dimensional model of the boom; Or, according to a preset corresponding table of components in the boom and their weight intervals, the three-dimensional model of the boom is searched according to weight polling comparison to identify the sub-model of the target component; Or, according to a preset corresponding table of components in the boom and their volume intervals, the three-dimensional model of the boom is searched according to volume polling comparison to identify a sub-model of the target component; Or, according to a preset corresponding table of components in the boom and their area intervals, the three-dimensional model of the boom is searched according to area polling comparison to identify the sub-model of the target component; And / or, according to a preset corresponding table of components in the boom and their attributes, the three-dimensional model of the boom is searched according to attribute polling comparison to identify the sub-model of the target component.

5. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 2, characterized in that: The model space position calculation algorithm performs the following steps on the three-dimensional model of the boom: Based on the crane origin, calculating the relative position of each sub-model in the three-dimensional model of the crane arm; Based on the relative positions of the sub-models, the sub-model of the target component is identified from the three-dimensional model of the boom.

6. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 2, characterized in that: If the target geometric feature is a plane, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are planes; All the identified sub-models of geometric features are sorted in descending order of area, and the sorting results are compared with the preset corresponding table of the components and plane elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the direction offset from the boom origin coordinate system, and the sorting results are compared with the preset corresponding table of each component in the boom and its plane elements to identify the sub-model with the target geometric features; The corresponding table of each component and its plane element is constructed by the following steps: Obtain all plane elements of each component, wherein the plane elements include area and a spatial normal of the plane relative to the origin of the boom; First, the plane elements are sorted in descending order according to the area, and then the plane elements are sorted in descending order according to the direction offset from the crane arm origin coordinate system to generate a corresponding table of each component and its plane element.

7. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 2, characterized in that: If the target geometric feature is a cylinder, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are cylinders; All the identified sub-models of geometric features are sorted in descending order of area, and the sorting results are compared with the preset corresponding table of the components and cylindrical elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the cylinder diameter, and the sorting results are compared with the preset corresponding table of each component in the boom and its cylinder element to identify the sub-model with the target geometric features; Among them, the corresponding table of each component and its cylindrical element is constructed by the following steps: Obtain all cylinder elements of each component, wherein the cylinder elements include area, cylinder diameter, and cylinder height; First, the cylindrical elements are sorted in descending order according to the area, and then the cylindrical elements are sorted in descending order according to the cylinder diameter, and a corresponding table of each component and its cylindrical element is generated.

8. The method for extracting boom dimensions based on a multi-dimensional algorithm according to claim 2, characterized in that: If the target geometric feature is a hole, the geometric feature recognition algorithm performs the following steps on the sub-model of the target component: Identify all sub-models of geometric features of the target component whose geometric features are holes; All the sub-models of the identified geometric features are sorted in descending order according to the nominal size, and the sorting results are compared with the preset corresponding table of the components and their hole elements in the boom; If the number of the identified sub-models of the geometric feature is 1, the sub-model of the geometric feature is determined to be a sub-model having the target geometric feature; If the number of sub-models with identified geometric features is greater than 1, all sub-models with identified geometric features are sorted in descending order according to the spatial offset distance of the hole axis relative to the origin coordinate system, and the sorting results are compared with the preset corresponding table of each component in the boom and its hole element to identify the sub-model with the target geometric features; The corresponding table of each component and its hole element is constructed by the following steps: Obtain all hole elements of each component, wherein the hole elements include nominal size, hole center axis direction, and space offset distance of the hole axis relative to the origin coordinate system; First, the hole elements are sorted in descending order according to the nominal size, and then sorted in descending order based on the spatial offset distance of the hole axis relative to the origin coordinate system to generate a corresponding table of each component and its hole element.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for extracting boom dimensions based on a multi-dimensional algorithm as described in any one of claims 1 to 8 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for extracting boom dimensions based on a multi-dimensional algorithm as described in any one of claims 1 to 8 is implemented.

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