CN113393145A - Model similarity obtaining method and device, electronic equipment and storage medium - Google Patents
Model similarity obtaining method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a model similarity obtaining method, a model similarity obtaining device, electronic equipment and a storage medium, and relates to the technical field of model data processing, wherein the method comprises the following steps: if the target model is the first type model, obtaining the target model attribute of the target model; comparing in a first model database according to the target model attribute to obtain a first comparison result; if the target model is the second type model, obtaining target model parameters of the target model; comparing in a second model database according to the target model parameters to obtain a second comparison result; and obtaining a first similarity result according to the target model attribute and the first comparison result, or obtaining a second similarity result according to the target model parameter and the second comparison result. According to the method, the models are classified and compared, the similar results are obtained, and the similarity between the models is obtained, so that the multiplexing rate of the models and the efficiency of design work are improved.
Description
Technical Field
The application relates to the technical field of model data processing, in particular to a method and a device for obtaining model similarity, electronic equipment and a storage medium.
Background
In the operation process of an enterprise, various material data can be related. The material generally refers to all materials used in the production, operation and running processes of enterprises, and can be a real object (such as raw materials, semi-finished products, low-value consumable products and the like) or a model of a part and the like.
When the engineer design model of the enterprise of design class, it has existed usually to appear partial material (part model data), or the material that obtains comes from the outsourcing part, because can't obtain the information of existing material, or can't obtain the data of outsourcing part, therefore among the prior art, can't carry out corresponding use and design to existing material according to material information, cause the material recovery rate of storage in the enterprise to be low, the repetition rate is high and adverse effect such as data of partial material can't obtain, lead to the material to pile up too much, the cost of production and design has been increased, the rate of utilization of material model and the efficiency of design have been reduced.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide a method and an apparatus for obtaining model similarity, an electronic device, and a storage medium, so as to solve the problem of low model utilization rate in the prior art.
In a first aspect, to solve the above problem, an embodiment of the present application provides a method for obtaining model similarity, where the method includes:
if the target model is a first type model, acquiring the target model attribute of the target model;
comparing in a first model database according to the target model attributes to obtain a first comparison result, wherein the first comparison result comprises at least one first similar model;
if the target model is the second type model, obtaining target model parameters of the target model;
comparing the target model parameters in a second model database to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model;
and obtaining a first similarity result according to the target model attribute and the first comparison result, or obtaining a second similarity result according to the target model parameter and the second comparison result.
In the implementation process, the target models are classified according to the types of the target models, so that the two types of target models can be classified, compared and calculated, and at least one first similar model or second similar model similar to the target models is obtained through comparison. By carrying out classification calculation on the similarity between the target model and each first similar model or each second similar model, the similarity result between different types of target models and each first similar model or each second similar model can be obtained, so that the model database can be selected and used in the design according to the similarity result. The utilization rate and the reuse rate of the models in the model database are improved, the design efficiency of workers is improved, modeling work on corresponding materials is not needed, the condition of material accumulation is effectively reduced, and the production cost of the corresponding materials is reduced.
Optionally, the comparing in the first model database according to the target model attribute to obtain a first comparison result includes:
performing first comparison in the first model database based on the target model attribute to obtain a screening result;
and carrying out second comparison in the screening result to obtain the first comparison result.
In the implementation process, when the target models belonging to the first type of models are compared, a first comparison result is finally obtained through secondary comparison, and secondary comparison is carried out in the screening results obtained through the primary comparison. The secondary comparison can effectively screen and remove at least one model with different model attributes from the target model in the first model database, and the accuracy of at least one first similar model in the first comparison result is improved.
Optionally, the first model database includes at least one internal model, and the target model attribute includes basic information of the target model and geometric information of the target model; the first comparison is performed in the first model database based on the target model attribute to obtain a screening result, and the method comprises the following steps:
comparing the basic information of the target model with the basic information of each internal model in the first model database, and screening out at least one first screening model meeting a basic similarity threshold, wherein the screening result is a set of the first screening models;
or comparing the geometric information of the target model with the geometric information of each internal model in the first model database, and screening out at least one second screening model meeting a geometric similarity threshold, wherein the screening result is a set of the second screening models.
In the implementation process, the target model attributes of the target model include basic information and geometric information of the target model, and the first model database includes at least one existing internal model and corresponding model data. In the first comparison of the attributes of the target model, the basic information or the geometric information of the target model is compared respectively, so that screening results respectively formed by two models which are similar to the basic information or the geometric information of the target model can be obtained. In the first comparison, corresponding selection can be carried out between the two model attributes, and the method is suitable for various situations.
Optionally, the performing the second comparison in the screening result to obtain the first comparison result includes:
comparing the geometric information of the target model with the model geometric information of each first screening model in the screening results, screening out at least one third screening model meeting the geometric similarity threshold, and taking the third screening model as the first similar model to obtain the first comparison result;
or comparing the basic information of the target model with the basic information of each second screening model in the screening results, screening out at least one fourth screening model meeting the basic similarity threshold, and taking the fourth screening model as the first similar model to obtain the first comparison result.
In the implementation process, after the first comparison, according to the comparison parameter of the basic information or the geometric information of the target model selected by the first comparison, in the second comparison, another target model attribute is selected as the comparison parameter, so as to implement the second comparison of the two model data of the basic information and the geometric information of the target model contained in the target model attribute. At least one first similar model similar to the basic information and the geometric information of the target model can be further obtained in the screening result, corresponding selection can be carried out in the two model attributes in the second comparison, the method is suitable for various conditions, and the accuracy of the first comparison result is effectively improved.
Optionally, the obtaining a first similarity result according to the target model attribute and the first comparison result includes:
obtaining the similarity between the basic information of the target model and the basic information of the models of the first similar models in the first comparison result to obtain a basic similarity result;
obtaining the similarity between the geometric information of the target model and the model geometric information of each first similar model in the first comparison result to obtain a geometric similarity result;
and obtaining the first similarity result according to a preset proportion, the basic similarity result and the geometric similarity result.
In the implementation process, the similarity results of the two model data are respectively obtained by respectively calculating the two model data in the model attributes of the target model and each first similar model in the first comparison result. The similarity result of the two types of model data can be subjected to proportional calculation through a preset proportion, so that a first similarity result is obtained, the similarity result of the target model belonging to the first type of model is obtained, and the accuracy of the first similarity result is effectively improved.
Optionally, the obtaining target model parameters of the target model includes:
sketching the target model to obtain model sketching data;
and obtaining the target model parameters of the target model according to the model sketch data.
In the implementation process, when the target model is the second-class model, the second-class model is an external model, and therefore the model parameters of the target model need to be acquired. By sketching the target model, the target model parameters can be quickly acquired from the model sketching data, the time for acquiring the model data of the target model is effectively reduced, and the acquisition efficiency of the target model parameters is improved.
Optionally, the second model database comprises at least one external model; the comparing in a second model database according to the target model parameter to obtain a second comparison result includes:
classifying the functionality of the target model according to the target model parameters to obtain a classification result;
and comparing the external models in the second model database according to the classification result, and screening to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model belonging to the classification result.
In the implementation process, when the target model is the second-type model, namely the external model, the functional types of the target model are classified through the target model parameters, and at least one second similar model which belongs to the same functional type as the target model can be obtained by screening in each external model of the second model database, so that adverse effects of the external models with different functional types on similarity are reduced, and the accuracy of the second comparison result is improved.
Optionally, the obtaining a similarity result according to the target model parameter and the second comparison result includes:
and obtaining the second similarity result according to the corresponding model parameter rule, the target model parameter and the model parameter of each second similar model in the second comparison result.
In the implementation process, the target model parameters of the target model and the model parameters of each second similar model are obtained. According to the functional types of the target models, the corresponding model parameter rules are adopted for calculation, the target models with different functional types can be calculated respectively, and the pertinence and the accuracy of the second similarity result are improved.
In a second aspect, an embodiment of the present application further provides a device for obtaining model similarity, where the device includes:
the data acquisition module is used for acquiring the target model attribute of the target model if the target model is a first type model;
the first comparison module is used for comparing in a first model database according to the target model attribute to obtain a first comparison result and obtain a first comparison result, wherein the first comparison result comprises at least one first similar model;
the data acquisition module is further used for acquiring target model parameters of the target model if the target model is a second type model;
the second comparison module is used for comparing in a second model database according to the target model parameters to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model;
and the similarity obtaining module is used for obtaining a first similarity result according to the target model attribute and the first comparison result, or obtaining a second similarity result according to the target model parameter and the second comparison result.
In the implementation process, the similarity between different types of target models and at least one model stored in the model database can be compared and calculated through each module respectively, so that the model database can be selected and used in the design according to the similarity result. The utilization rate and the reuse rate of the models in the model database and the design efficiency of workers are improved, the corresponding materials do not need to be modeled, the accumulation condition of the materials is effectively reduced, and the production cost of the corresponding materials is reduced.
In a third aspect, an embodiment of the present application further provides an electronic device, where the electronic device includes a memory and a processor, where the memory stores program instructions, and the processor executes the steps in any one of the foregoing implementation manners when reading and executing the program instructions.
In a fourth aspect, an embodiment of the present application further provides a readable storage medium, where computer program instructions are stored, and when the computer program instructions are read and executed by a processor, the steps in any of the above implementation manners are performed.
In summary, the embodiments of the present application provide a method and an apparatus for obtaining model similarity, an electronic device, and a storage medium, which can obtain similarity results of models of different types by respectively comparing and obtaining similarities between models of different types, thereby effectively improving the usage rate and the reuse rate of the models in a model database and the design efficiency of workers. The modeling work of the corresponding materials is not needed, the accumulation condition of the materials is effectively reduced, and the production cost of the corresponding materials is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a model similarity obtaining method according to an embodiment of the present disclosure;
fig. 2 is a detailed flowchart of step S2 according to an embodiment of the present disclosure;
fig. 3 is a detailed flowchart of step S5 according to an embodiment of the present disclosure;
fig. 4 is a detailed flowchart of step S4 according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a model similarity obtaining apparatus according to an embodiment of the present application.
Icon: model similarity obtaining device-100; a data acquisition module-110; a first comparison module-120; a second comparison module-130; and a similarity obtaining module-140.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of them. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without any creative effort belong to the protection scope of the embodiments of the present application.
The embodiment of the application provides a model similarity obtaining method, which is applied to a server, wherein the server can be an electronic device with a logic calculation function, such as a Personal Computer (PC), a tablet computer, a smart phone, a Personal Digital Assistant (PDA) and the like. In the design work in the prior art, due to the reasons that field materials are too much to retreat, the design of material models is repeated, the data of material models in enterprises are not circulated, and warehouse extrusion parts are too many, when workers design, the required material models cannot be accurately acquired in the existing material models to be used, so that the overstocking of the material models in the enterprises is too much, the utilization rate and the reuse rate of each model are low, the workers need to re-model the target model when designing, and the efficiency of the design work is reduced. Therefore, according to the model similarity obtaining method provided by the embodiment of the application, the similarity between the target model and at least one model in the model database can be calculated, the required model can be selected from the obtained similarity result for design and application, and the multiplexing rate and the utilization rate of the model in the model database are effectively improved.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for obtaining model similarity according to an embodiment of the present application, including the following steps:
step S1, if the target model is the first type model, the target model attribute of the target model is obtained.
Wherein, different model data are adopted for comparison when the comparison is carried out due to different types of the target models.
Optionally, before the model data of the target model is obtained, the type of the target model may be identified and determined according to the identification information of the target model. The identification information may be information such as a number and a name of the target model, or may be encoded information set in the material corresponding to the target model, such as information such as a two-dimensional code and a barcode. And acquiring information such as the number and the name of the target model from the coding information in a scanning mode so as to judge whether the target model belongs to the first type model or the second type model.
After the execution of step S1, the execution of step S2 is continued.
And step S2, comparing in a first model database according to the target model attributes to obtain a first comparison result.
And when more than one first similar model exists, the first comparison result is a set of more than one first similar model.
It should be noted that, when comparing the first type of model, the selected comparison parameter is the model attribute of the model. For example, the model attributes may include model data such as basic information and geometric information of the model, and by comparing the model attributes of the target model with the model attributes of more than one model in the first model database, at least one first similar model similar to the attributes of the target model can be obtained, so as to obtain a corresponding first comparison result.
And step S3, if the target model is the second type model, obtaining the target model parameters of the target model.
It should be noted that, when the model type of the target model is determined, the first-class model may be an internal model, including multiple models related to processing-class information inside an enterprise; the second type of model may be an external model, including a variety of models from outside the enterprise that do not involve process-type information. Accordingly, the first model database may store at least one internal model and corresponding model data for storing a material model designed in the enterprise, such as a material model rolled back on site in a design work of the enterprise, an internal standard part model, a repetitive model with at least one material code, and the like. The second type model database can store at least one external model and corresponding model data, and stores material models from the outside of the enterprise, such as part outsourcing part models which are not consumed in corresponding projects, external material models generated by cooperation of cooperative enterprises, and the like.
Optionally, before comparing the first type of model, model attributes of a plurality of internal models, such as a model of recycled materials, a model of basic materials, a model of standard parts designed by an enterprise, etc., may be obtained to form a first model database, and the first model database may be updated when a new material is designed or manufactured inside an enterprise.
Optionally, since the target model belongs to an external model and the target model parameters of the target model cannot be directly obtained, before step S2, the method further includes the following steps:
and step Sa1, sketching the target model to obtain model sketching data.
The model parameters of the target model can be sketched through design tools such as CAD (computer-aided design) and the like to obtain corresponding sketched data.
And step Sa2, obtaining the target model parameters of the target model according to the model sketch data.
The method comprises the steps of extracting the obtained sketch data, removing invalid data irrelevant to model parameters, keeping valid data, sorting the valid data, and obtaining target model parameters of a target model.
It should be noted that, since the second model database includes a plurality of external models, when the external models are put in storage, each external model may be sketched to obtain model parameters of each external model. The target model parameters can be quickly acquired from the model sketch data, the time for acquiring the model data of the target model is effectively reduced, the acquisition efficiency of the target model parameters is improved, and the accuracy of the second model database is improved.
After the execution of step S3, the execution of step S4 is continued.
And step S4, comparing in a second model database according to the target model parameters to obtain a second comparison result.
And when more than one second similar model exists, the second comparison result is a set of more than one second similar model.
It should be noted that, since the plurality of models in the second model database come from the outside, the model attributes of the external models cannot be directly obtained in the design work, and therefore, in the comparison process of the second type of model, the selected comparison parameters are parameter information of the model. For example, the target model parameters of the target model may include performance parameters of the model, such as a number of different performance parameters of the target model, such as speed, power, model, and so on. By comparing the model parameters of the target model with the model parameters of at least one model in the second model database, at least one second similar model similar to the target model parameters can be obtained, and a corresponding second comparison result is obtained.
Step S5, obtaining a first similarity result according to the target model attribute and the first comparison result, or obtaining a second similarity result according to the target model parameter and the second comparison result.
According to different types of the target models, the first comparison result or the second comparison result obtained after the target models are compared is respectively calculated, and the similarity results of the two comparison modes can be respectively obtained, so that the classification comparison and the classification calculation of the target models are realized, and the accuracy of the obtained similarity results is effectively improved.
Alternatively, when the first comparison result and the second comparison result are calculated, different methods may be used to calculate the two comparison results, respectively.
It is to be noted that the first similarity result and the second similarity result obtained by calculation may be presented in various forms. For example, the similarity result may be a result in a numerical form, for example, the similarity between the model one of the first comparison result and the second comparison result and the target model is 0.95, the similarity between the model two and the target model is 0.85, and the like. Alternatively, the similarity result may also be a result in a hierarchical form, for example, the similarity between the model one and the target model is high, the similarity between the model two and the target model is general, and the like.
Optionally, after step S5, the method may further include the steps of:
and step S6, recording the first similarity result or the second similarity result to obtain at least one corresponding target similarity model.
It is worth explaining that the first similarity result and the second similarity result can be recorded and integrated and output in a form or text mode, so that the staff can check and understand the similarity result to obtain at least one corresponding target similarity model with higher similarity to the target model, so that the staff can directly use the target similarity model, and can quickly obtain the required model for design.
In the embodiment shown in fig. 1, the target models are classified, compared and calculated according to the types of the target models, and the similarity results between the different types of target models and each of the first similar models or the second similar models can be obtained, so that the model database can be selected and used in the design according to the similarity results. The utilization rate and the reuse rate of the models in the model database are improved, the design efficiency of workers is improved, modeling work on corresponding materials is not needed, the condition of material accumulation is effectively reduced, and the production cost of the corresponding materials is reduced.
Referring to fig. 2, the step S2 in the present embodiment may include steps S21-S22:
and step S21, performing first comparison in the first model database based on the target model attribute to obtain a screening result.
The model attributes comprise model data of various models, and the number of models in the first model database is large, so that secondary comparison is performed with at least one internal model in the first model database according to various different model data in the model attributes. The accuracy of the first comparison result is increased through a secondary comparison mode, so that the influence of the irrelevant model in the first model database on similarity calculation is reduced.
By way of example, the secondary screening is performed in the first model database, so that a first comparison result similar to the model attribute of the target model can be obtained through screening, and the accuracy of the first comparison result can be effectively improved through the secondary screening, and the accuracy of the first similarity settlement result is further improved. The adverse effects of reducing the accuracy of the similarity result, increasing the calculation range of the similarity result, having large calculation workload and the like when the similarity result is calculated by an irrelevant model in the first model database due to data errors, model deviation and the like in the first model database are reduced.
Optionally, step S21 may further include step S211 and step S212:
step S211, comparing the basic information of the target model with the basic information of each internal model in the first model database, and screening out at least one first screening model that meets a basic similarity threshold.
When more than one first screening model exists, the screening result is a set of more than one first screening model. For example, the basic information of the model may include various model data such as material information, type information, shape information, size information, weight information, and a hollow ratio of the model. And comparing the basic information of the target model with the basic information of each internal model, removing at least one internal model which does not meet the comparison threshold value, and screening to obtain a first screening model with the basic information of various models similar to the target model.
Optionally, when the size information in the basic information of the model is compared, an internal model of which the size information is larger than that of the target model can be screened from the first model database, so that reprocessing is arranged in the subsequent design and processing process to meet the requirements of the current part, the limitation of undersize to the part is reduced, and the times of ordering new material purchase is reduced.
Optionally, when comparing the material information, the type information, and the shape information in the basic information of the model, the staff may set different comparison thresholds according to different requirements and actual situations. For example, when comparing material information, since a difference between each material is large, resulting in a large difference in performance between parts, the comparison of material information in which the materials are completely the same satisfies a comparison threshold. Or, when comparing the type information, the working position and the functional characteristic of the part are different due to different functional types of each model, so that the comparison threshold is met when the functional types are completely the same in the comparison of the type information. In the comparison of the shape information, because the shapes of some models are similar, the similar models can complete the same task and have the same functionality in some loose design parts, and the requirements of some design parts on the shapes are strict, the similar models cannot complete the same task and have different functionalities, so that the comparison threshold can be adjusted by a worker according to the design condition.
Or, in step S212, comparing the geometric information of the target model with the model geometric information of each internal model in the first model database, and screening out at least one second screening model satisfying a geometric similarity threshold.
When more than one second screening model exists, the screening result is a set of more than one second screening model. For example, the geometric information of the model may include drawing information for processing in a processing drawing of the model, such as geometric identification features, such as positions of holes and grooves and the number of features in the drawing. And comparing the geometric information of the target model with at least one internal model, removing at least one internal model which does not meet a comparison threshold value, and screening to obtain a second screening model with various model geometric information similar to the target model.
Optionally, geometric information of the model may be used as a comparison parameter in the first comparison, and since the target model is an internal model designed by the enterprise itself, the processing drawing data of the model may be acquired. The processing drawing data of the target model and the processing drawing data of the internal model are compared, and different comparison thresholds can be set by workers according to different requirements and actual conditions to compare various data in the processing drawing data.
And step S22, carrying out second comparison in the screening result to obtain the first comparison result.
After the basic information or the geometric information of the target model in the target attribute is compared for the first time, the second comparison can be performed according to another different model data to obtain a first comparison result.
Optionally, step S22 may further include step S221 and step S222:
step S221, comparing the geometric information of the target model with the model geometric information of each of the first screening models in the screening results, screening out at least one third screening model that satisfies the geometric similarity threshold, and obtaining the first comparison result by using the third screening model as the first similarity model.
And after the basic information of the target model meets the comparison threshold value and at least one first screening model similar to the basic information of the target model is screened out, the geometric information of the target model can be acquired. And comparing the geometric information of the target model with the geometric information of each model in the screening result to realize the second comparison, wherein the comparison mode is the same as the mode of the geometric information of the model in the step S212.
Or, in step S222, comparing the basic information of the target model with the basic information of each second screening model in the screening results, screening out at least one fourth screening model that meets the basic similarity threshold, and taking the fourth screening model as the first similar model to obtain the first comparison result.
And after at least one second screening model similar to the geometric information of the target model is screened out when the geometric information of the target model meets the comparison threshold, the basic information of the target model can be acquired. And comparing the basic model information of the target model with the basic model information of each model in the screening result to realize the second comparison, wherein the comparison mode is the same as the mode of the geometric model information in the step S211.
It is worth mentioning that through the second comparison, the first comparison result with the basic information and the geometric information similar to the target model can be obtained.
Optionally, the model attribute may further include other various data, and in the secondary comparison performed when the target model belongs to the first-class model, the worker may correspondingly select and adjust the comparison parameter selected in the secondary comparison according to the design requirement and the actual situation.
In the embodiment shown in fig. 2, the target models belonging to the first class of models are compared by the secondary comparison, so that at least one model with a model attribute different from that of the target model in the first model database can be effectively screened and removed, and the accuracy of at least one first similar model in the first comparison result is improved.
Referring to fig. 3, the step S5 in the present embodiment may include steps S51-S53:
step S51, obtaining similarity between the basic information of the target model and the basic information of each of the first similar models in the first comparison result, and obtaining a basic similarity result.
And calculating multiple groups of values according to a preset algorithm to obtain a basic similarity result between each first similar model and the target model in the first comparison result.
For example, the preset algorithm may be a calculation manner such as a proportional algorithm performed on specific values of the basic information of the target model.
Step S52, obtaining similarity between the geometric information of the target model and the geometric information of each of the first similar models in the first comparison result, and obtaining a geometric similarity result.
And calculating multiple groups of values according to a preset algorithm to obtain a geometric similarity result between each first similar model and the target model in the first comparison result.
For example, the preset algorithm may be a calculation manner such as a proportional algorithm performed on specific values of the geometric information of the target model.
And step S53, obtaining the first similarity result according to a preset proportion, the basic similarity result and the geometric similarity result.
It should be noted that the worker may set different preset proportions in the server according to the design requirement or the actual situation to calculate the first similarity result, for example: 0.3a +0.7b ═ c, where a represents the basic similarity result, b represents the geometric similarity result, and c represents the first similarity result. The above calculation manner may be expressed in that basic information of the model (a difference in model size, weight, hollowing ratio, etc.) accounts for 30% of the first similarity result, and geometric information of the model (the number, size, position of holes, grooves, etc.) accounts for 70% of the first similarity result. Optionally, the value range of the preset ratio of a may also be between 0.2 and 0.4, and the value range of the preset ratio of b may also be between 0.6 and 0.8.
In the embodiment shown in fig. 3, the similarity result of the target model belonging to the first type of model is obtained by respectively calculating two types of model data in the model attributes and proportionally calculating the similarity result of the two types of model data by adopting a preset calculation proportion, so that the accuracy of the first similarity result is effectively improved.
Referring to fig. 4, the step S4 in the embodiment of the present application may include steps S41-S42:
and step S41, classifying the functionality of the target model according to the target model parameters to obtain a classification result.
The type of functionality of the target model can be obtained according to the parameters of the target model, for example, the target model belongs to different functions such as a connecting piece, a cylinder, a bearing piece and the like.
And step S42, comparing the external models in the second model database according to the classification result, and screening to obtain the second comparison result.
The second comparison result comprises at least one second similar model belonging to the classification result, the second similar model is an external model belonging to the same functional type as the target model, other internal models different from the target model can be removed, and the calculation workload is reduced.
Optionally, after step S42, the method may further include:
step S43, obtaining the second similarity result according to the corresponding model parameter rule, the target model parameter, and the model parameter of each second similarity model in the second comparison result.
When the similarity of the models with different functionalities is calculated, the models have different model parameter rules, such as cylinder comparison (cylinder diameter, stroke) and the like, and joint comparison (trachea diameter, threaded interface) and the like. According to the functional types of the target models, the corresponding model parameter rules are adopted for calculation, the target models with different functional types can be calculated respectively, and the pertinence and the accuracy of the second similarity result are improved.
For example, when the target model is a cylinder model, for example, the target cylinder diameter is 10, the stroke is 20, the cylinder diameter of the comparison cylinder in the second comparison result is 12, and the stroke is 25, the second similarity result is similar.
In the embodiment shown in fig. 4, by classifying the functional types of the models, at least one second similar model belonging to the same functional type as the target model is obtained by screening in each external model of the second model database, so that adverse effects on similarity caused by external models of different functional types are reduced, and the accuracy of the second comparison result is improved.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a model similarity obtaining apparatus according to an embodiment of the present application, where the model similarity obtaining apparatus 100 includes: a data acquisition module 110, a first comparison module 120, a second comparison module 130, and a similarity acquisition module 140.
A data obtaining module 110, configured to obtain a target model attribute of a target model if the target model is a first type model;
a first comparison module 120, configured to perform comparison in a first model database according to the target model attribute to obtain a first comparison result, and obtain a first comparison result, where the first comparison result includes at least one first similar model;
the data obtaining module 110 is further configured to obtain a target model parameter of the target model if the target model is a second type model;
a second comparison module 130, configured to perform comparison in a second model database according to the target model parameter to obtain a second comparison result, where the second comparison result includes at least one second similar model;
the similarity obtaining module 140 is configured to obtain a first similarity result according to the target model attribute and the first comparison result, or obtain a second similarity result according to the target model parameter and the second comparison result.
The first comparison module 120 further includes: a first comparison submodule and a second comparison submodule;
the first comparison sub-module is used for carrying out first comparison in the first model database based on the target model attribute to obtain a screening result;
and the second comparison submodule is used for carrying out second comparison on the screening result to obtain the first comparison result.
The first model database comprises at least one internal model, and the target model attribute comprises basic information of a target model and geometric information of the target model;
the first comparison sub-module is further configured to compare the basic information of the target model with the basic information of each internal model in the first model database, and screen out at least one first screening model that meets a basic similarity threshold, where the screening result is a set of the first screening models;
or comparing the geometric information of the target model with the geometric information of each internal model in the first model database, and screening out at least one second screening model meeting a geometric similarity threshold, wherein the screening result is a set of the second screening models.
The second comparison sub-module is further configured to compare the geometric information of the target model with the model geometric information of each of the first screening models in the screening results, screen out at least one third screening model that meets the geometric similarity threshold, and obtain the first comparison result by using the third screening model as the first similar model;
or comparing the basic information of the target model with the basic information of each second screening model in the screening results, screening out at least one fourth screening model meeting the basic similarity threshold, and taking the fourth screening model as the first similar model to obtain the first comparison result.
The similarity obtaining module 140 further includes: a basic submodule, a geometric submodule and a scale submodule;
the basic submodule is used for obtaining the similarity between the basic information of the target model and the basic information of the models of the first similar models in the first comparison result to obtain a basic similarity result;
the geometric submodule is used for acquiring the similarity between the geometric information of the target model and the model geometric information of each first similar model in the first comparison result to obtain a geometric similarity result;
and the proportion submodule is used for obtaining the first similarity result according to a preset proportion, the basic similarity result and the geometric similarity result.
The model similarity obtaining apparatus 100 further includes: a sketch module;
the sketch module is used for sketching the target model to obtain model sketch data;
and obtaining the target model parameters of the target model according to the model sketch data.
The second comparison module further comprises: a classification submodule and a comparison submodule;
the classification submodule is used for classifying the functionality of the target model according to the target model parameters to obtain a classification result;
and the comparison submodule is used for comparing the external models in the second model database according to the classification result and screening to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model belonging to the classification result.
The similarity obtaining module 140 further includes: a parameter submodule;
and the parameter submodule is used for obtaining the second similarity result according to the corresponding model parameter rule, the target model parameter and the model parameter of each second similarity model in the second comparison result.
Since the principle of solving the problem of the apparatus in the embodiment of the present application is similar to that of the embodiment of the model similarity obtaining method, the implementation of the apparatus in the embodiment of the present application may refer to the description in the embodiment of the method, and repeated details are not repeated.
In the embodiment shown in fig. 5, through cooperation between the functional modules, the similarity between the target models of different types and at least one model stored in the model database is respectively compared and calculated to obtain the similarity result of the model, so that the utilization rate and the reuse rate of the models in the model database and the design efficiency of workers are improved.
The embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores program instructions, and when the processor reads and runs the program instructions, the processor executes the steps in any one of the method for obtaining model similarity provided in this embodiment.
It should be understood that the electronic device may be a Personal Computer (PC), a tablet PC, a smart phone, a Personal Digital Assistant (PDA), or other electronic device having a logical computing function.
The embodiment of the application also provides a readable storage medium, wherein computer program instructions are stored in the readable storage medium, and the computer program instructions are read by a processor and executed when the computer program instructions are executed, so that the steps in the model similarity obtaining method are executed.
To sum up, the embodiment of the application provides a method and a device for obtaining model similarity, an electronic device and a storage medium, wherein similarity between models of different types is respectively compared and calculated, so that similarity results of the models of different types can be obtained, the utilization rate and the reuse rate of the models in a model database and the design efficiency of workers are effectively improved, modeling work on corresponding materials is not needed, the condition of material accumulation is effectively reduced, and the production cost of the corresponding materials is reduced.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, the block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices according to at least one embodiment of the present application. In this regard, each block in the block diagrams may represent a module, segment, or portion of code, which comprises at least one executable instruction for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams, and combinations of blocks in the block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Therefore, the present embodiment further provides a readable storage medium, in which computer program instructions are stored, and when the computer program instructions are read and executed by a processor, the computer program instructions perform the steps of any of the block data storage methods. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RanDom Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (11)
1. A model similarity obtaining method is characterized by comprising the following steps:
if the target model is a first type model, acquiring the target model attribute of the target model;
comparing in a first model database according to the target model attributes to obtain a first comparison result, wherein the first comparison result comprises at least one first similar model;
if the target model is the second type model, obtaining target model parameters of the target model;
comparing the target model parameters in a second model database to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model;
and obtaining a first similarity result according to the target model attribute and the first comparison result, or obtaining a second similarity result according to the target model parameter and the second comparison result.
2. The method of claim 1, wherein said comparing in a first model database according to said target model attribute, resulting in a first comparison result, comprises:
performing first comparison in the first model database based on the target model attribute to obtain a screening result;
and carrying out second comparison in the screening result to obtain the first comparison result.
3. The method of claim 2, wherein the first model database contains at least one internal model, and the object model attributes comprise object model basic information and object model geometric information; the first comparison is performed in the first model database based on the target model attribute to obtain a screening result, and the method comprises the following steps:
comparing the basic information of the target model with the basic information of each internal model in the first model database, and screening out at least one first screening model meeting a basic similarity threshold, wherein the screening result is a set of the first screening models;
or comparing the geometric information of the target model with the geometric information of each internal model in the first model database, and screening out at least one second screening model meeting a geometric similarity threshold, wherein the screening result is a set of the second screening models.
4. The method of claim 3, wherein said performing a second comparison on said screening results to obtain said first comparison result comprises:
comparing the geometric information of the target model with the model geometric information of each first screening model in the screening results, screening out at least one third screening model meeting the geometric similarity threshold, and taking the third screening model as the first similar model to obtain the first comparison result;
or comparing the basic information of the target model with the basic information of each second screening model in the screening results, screening out at least one fourth screening model meeting the basic similarity threshold, and taking the fourth screening model as the first similar model to obtain the first comparison result.
5. The method of claim 4, wherein obtaining a first similarity result from the target model attribute and the first comparison result comprises:
obtaining the similarity between the basic information of the target model and the basic information of the models of the first similar models in the first comparison result to obtain a basic similarity result;
obtaining the similarity between the geometric information of the target model and the model geometric information of each first similar model in the first comparison result to obtain a geometric similarity result;
and obtaining the first similarity result according to a preset proportion, the basic similarity result and the geometric similarity result.
6. The method of claim 1, wherein the obtaining target model parameters of the target model comprises:
sketching the target model to obtain model sketching data;
and obtaining the target model parameters of the target model according to the model sketch data.
7. The method of claim 1, wherein the second model database contains at least one external model; the comparing in a second model database according to the target model parameter to obtain a second comparison result includes:
classifying the functionality of the target model according to the target model parameters to obtain a classification result;
and comparing the external models in the second model database according to the classification result, and screening to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model belonging to the classification result.
8. The method of claim 1, wherein obtaining a similarity result according to the target model parameter and the second comparison result comprises:
and obtaining the second similarity result according to the corresponding model parameter rule, the target model parameter and the model parameter of each second similar model in the second comparison result.
9. A model similarity acquisition apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring the target model attribute of the target model if the target model is a first type model;
the first comparison module is used for comparing in a first model database according to the target model attribute to obtain a first comparison result and obtain a first comparison result, wherein the first comparison result comprises at least one first similar model;
the data acquisition module is further used for acquiring target model parameters of the target model if the target model is a second type model;
the second comparison module is used for comparing in a second model database according to the target model parameters to obtain a second comparison result, wherein the second comparison result comprises at least one second similar model;
and the similarity obtaining module is used for obtaining a first similarity result according to the target model attribute and the first comparison result, or obtaining a second similarity result according to the target model parameter and the second comparison result.
10. An electronic device comprising a memory having stored therein program instructions and a processor that, when executed, performs the steps of the method of any of claims 1-8.
11. A readable storage medium having stored thereon computer program instructions for executing the steps of the method according to any one of claims 1 to 8 when executed by a processor.
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