1. A method of sorting parts, the method comprising:

acquiring target model data corresponding to a part model to be classified currently;

identifying the target model data to determine a processing type corresponding to the target model data;

updating a bill of materials where the current part model to be classified is located according to the machining type to obtain an updated bill of materials, wherein the bill of materials comprises at least one part model to be classified and corresponding model data;

verifying the updated bill of materials;

and if the verification of the updated bill of materials is passed, taking the updated bill of materials as a classification result.

2. The method of claim 1, wherein the identifying the target model data to determine the machining type corresponding to the target model data comprises:

classifying the target model data based on a preset classification rule to obtain classified data;

and performing multi-stage identification on the classified data to obtain a processing type, wherein the multi-stage identification at least comprises two-stage identification.

3. The method of claim 2, wherein the secondary identification comprises a first level identification and a second level identification; the multi-stage identification is carried out on the classified data to obtain the processing type, and the method comprises the following steps:

performing the first-stage identification on the classified data according to a preset priority identification rule, and matching in a processing type library according to a first-stage identification result to obtain a corresponding processing type when the identification is successful;

and when the first-stage identification fails, performing second-stage identification on the classified data according to a preset detailed identification rule, and when the identification succeeds, matching in the machining type library according to a second-stage identification result to obtain a corresponding machining type.

4. The method of claim 1, wherein the validating the updated bill of materials comprises:

verifying whether each part model to be classified in the updated bill of materials is matched with a corresponding machining type;

when the part model to be classified is matched with the corresponding machining type, the updated bill of materials is verified to be passed;

and when the part model to be classified is not matched with the corresponding machining type, representing that the verification of the updated bill of material is failed.

5. The method of claim 1, wherein after the validating the updated bill of materials, the method further comprises:

if the verification of the updated bill of materials is not passed, screening out unmatched part models which are not matched with the corresponding processing types from the updated bill of materials;

and generating corresponding prompt information aiming at the unmatched part model.

6. The method of claim 5, further comprising:

and updating the model data of the unmatched part model according to the prompt information to obtain updated model data.

7. The method of claim 1, wherein after said updating the bill of materials as a result of the classification, the method further comprises:

generating corresponding purchasing demand data according to the classification result;

and matching in a supply database according to the purchase demand data to obtain a corresponding supply source.

8. The method according to claim 1, wherein before the obtaining of the target model data corresponding to the part model to be classified currently, the method further comprises:

acquiring a current part model to be classified from a part database;

judging whether the current part model to be classified meets the processing requirements;

and when the part model to be classified currently meets the machining requirement, storing the part model to be classified currently.

9. A parts sorting apparatus, the apparatus comprising:

the acquisition module is used for acquiring target model data corresponding to the current part model to be classified;

the identification module is used for identifying the target model data to determine the processing type corresponding to the target model data;

the updating module is used for updating a bill of materials where the current part model to be classified is located according to the machining type to obtain an updated bill of materials, wherein the bill of materials comprises at least one part model to be classified and corresponding model data;

the verification module is used for verifying the updated bill of materials; and if the verification of the updated bill of materials is passed, taking the updated bill of materials as a classification 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.

Background

In the customized production of the equipment manufacturing industry, due to the fact that the parts contained in the complex equipment are various in types, the manufacturing process is complex, and the assembly flow is complex, the number of the parts in an equipment manufacturing enterprise is large, the machining technology required by each part is different, and the parts of different types need to be classified for classified manufacturing and use.

In the prior art, machining parts need workers to manually open drawings, the machining types of the parts are classified after the appearances of the parts are checked one by one, the workers need to find corresponding manufacturers capable of machining the parts according to the machining technical requirements corresponding to the parts after classification, and outside resources need to be arranged in advance according to the types of the parts and the productivity of internal machining departments of the companies. Due to the fact that the machining type classification efficiency of the parts is low, enterprises consume a large amount of manpower and high cost, business orders are increasing day by day, and the operation efficiency of the enterprises is reduced.

Disclosure of Invention

In view of the above, an object of the embodiments of the present application is to provide a method and an apparatus for sorting components, an electronic device, and a storage medium, so as to solve the problem of low efficiency of sorting components in the prior art.

In a first aspect, an embodiment of the present application provides a part classification method, where the method includes:

acquiring target model data corresponding to a part model to be classified currently;

identifying the target model data to determine a processing type corresponding to the target model data;

updating a bill of materials where the current part model to be classified is located according to the machining type to obtain an updated bill of materials, wherein the bill of materials comprises at least one part model to be classified and corresponding model data;

verifying the updated bill of materials;

and if the verification of the updated bill of materials is passed, taking the updated bill of materials as a classification result.

In the implementation process, the target model data of the part model needing to be classified is identified, the corresponding machining type is determined, the bill of materials where the part model is located is updated according to the machining type, and the classification result of the machining type of the part can be obtained by verifying the updated bill of materials. The time and the cost required when the machining types of the parts are classified are reduced, the efficiency and the accuracy of classifying the machining types of the parts are effectively improved, the rapid and accurate classification of the machining types of the material parts by enterprises is facilitated, and the working efficiency of the enterprises is improved.

Optionally, the identifying the target model data to determine the processing type corresponding to the target model data includes:

classifying the target model data based on a preset classification rule to obtain classified data;

and performing multi-stage identification on the classified data to obtain a processing type, wherein the multi-stage identification at least comprises two-stage identification.

In the implementation process, the classified data is recognized through multi-stage recognition, the processing type corresponding to the target model data is obtained, and the pertinence and the effectiveness of the multi-stage recognition can be effectively improved. The influence of irrelevant data in the target model data on the identification is reduced, and the accuracy and pertinence of the machining type are improved.

Optionally, the secondary identification comprises a first level identification and a second level identification; the multi-stage identification is carried out on the classified data to obtain the processing type, and the method comprises the following steps:

performing the first-stage identification on the classified data according to a preset priority identification rule, and matching in a processing type library according to a first-stage identification result to obtain a corresponding processing type when the identification is successful;

and when the first-stage identification fails, performing second-stage identification on the classified data according to a preset detailed identification rule, and when the identification succeeds, matching in the machining type library according to a second-stage identification result to obtain a corresponding machining type.

In the implementation process, the secondary identification comprises primary identification and secondary identification, when the multi-stage identification is carried out, the primary identification is carried out on the classified data through the priority identification rule, the secondary identification is carried out on the classified data through the detailed identification rule after the primary identification fails, and the identification modes of the two grades of the priority identification or the detailed identification can be provided for the classified data. And the second-stage identification is carried out after the first-stage identification fails, so that the identification efficiency and accuracy are effectively improved, the identification range is expanded, the method is suitable for the identification requirements of various different types of classified data, and various different conditions are met.

Optionally, the verifying the updated bill of materials includes:

verifying whether each part model to be classified in the updated bill of materials is matched with a corresponding machining type;

when the part model to be classified is matched with the corresponding machining type, the updated bill of materials is verified to be passed;

and when the part model to be classified is not matched with the corresponding machining type, representing that the verification of the updated bill of material is failed.

In the implementation process, whether each part model to be classified in the updated bill of materials is matched with the corresponding machining type is verified, so that the updated bill of materials is verified, and the matching condition of each part model to be classified in the updated bill of materials can be verified. And the verification result of the updated bill of materials is judged according to the matching condition, so that the verification accuracy and pertinence are effectively improved.

Optionally, after verifying the updated bill of materials, the method further includes:

if the verification of the updated bill of materials is not passed, screening out unmatched part models which are not matched with the corresponding processing types from the updated bill of materials;

and generating corresponding prompt information aiming at the unmatched part model.

In the implementation process, when the verification of the updated material list is not passed, the unmatched part models which are not matched with the machining types are screened out from the updated material list, and corresponding prompt information can be generated for the unmatched part models so as to prompt the relevant states of unmatched part model matching failure, data missing and the like. The unmatched part models are efficiently processed, the whole process of part classification in enterprises is perfected, and the method is suitable for various different part models.

Optionally, the method further comprises:

and updating the model data of the unmatched part model according to the prompt information to obtain updated model data.

In the implementation process, the state of the unmatched part model which is failed in matching can be determined through the prompt information, and the model data of the unmatched part model can be supplemented and updated according to the prompt information. The subsequent updating process of the unmatched model is perfected, and the feedback and processing efficiency of the unmatched part model is effectively improved.

Optionally, after taking the updated bill of materials as the classification result, the method further includes:

generating corresponding purchasing demand data according to the classification result;

and matching in a supply database according to the purchase demand data to obtain a corresponding supply source.

In the implementation process, after the classification result is obtained, the corresponding purchase demand data is generated through the classification result, and a supply source meeting the purchase demand can be matched in the supply database so as to provide a basis for purchasing at least one part model in the classification result. The follow-up purchasing process of the part model is perfected, the time for manually matching supply sources required by purchasing in an enterprise is saved, and the working efficiency of a purchasing end of the enterprise is improved.

Optionally, before obtaining target model data corresponding to a part model to be currently classified, the method further includes:

acquiring a current part model to be classified from a part database;

judging whether the current part model to be classified meets the processing requirements;

and when the part model to be classified currently meets the machining requirement, storing the part model to be classified currently.

In the implementation process, the processing requirement of the current part model to be classified acquired from the part database is judged in the storage process, so that whether the current part model to be classified needs to be stored can be judged, and the subsequent classification treatment is carried out on the current part model to be classified. The influence of other irrelevant part models in the part database on the part classification is effectively reduced, and the pertinence of the part classification is improved.

In a second aspect, an embodiment of the present application further provides a part sorting apparatus, where the apparatus includes:

the acquisition module is used for acquiring target model data corresponding to the current part model to be classified;

the identification module is used for identifying the target model data to determine the processing type corresponding to the target model data;

the updating module is used for updating a bill of materials where the current part model to be classified is located according to the machining type to obtain an updated bill of materials, wherein the bill of materials comprises at least one part model to be classified and corresponding model data;

the verification module is used for verifying the updated bill of materials; and if the verification of the updated bill of materials is passed, taking the updated bill of materials as a classification result.

In the implementation process, the target model data of the current part model to be classified is acquired through the acquisition module, the target model data is identified through the identification module to obtain the processing type, the updated bill of materials updated according to the processing type is obtained through the updating module, the updated bill of materials is verified through the verification module, and the classification result is determined. The time and the cost required when the machining types of the parts are classified can be reduced, the efficiency and the accuracy of classifying the machining types of the parts are effectively improved, the rapid and accurate classification of the machining types of the material parts by enterprises is facilitated, and the working efficiency of the enterprises is improved.

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 implementation manner of the above-mentioned part classification method 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 implementation manner of the above-mentioned part classification method are executed.

In summary, the embodiment of the application provides a part classification method, a device, an electronic device and a storage medium, by acquiring the processing types of various material part models and verifying the processing types of various part models to be classified in a bill of materials, the time and cost required for classifying the processing types of parts are reduced, the efficiency and accuracy for classifying the processing types of the parts can be effectively improved, and the working efficiency of enterprises is improved.

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 flow chart of a part classification method according to an embodiment of the present application;

fig. 2 is a detailed flowchart of step S2 according to an embodiment of the present disclosure;

fig. 3 is a detailed flowchart of a part classification method according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a part sorting apparatus according to an embodiment of the present application.

Icon: 100-a part sorting device; 110-an obtaining module; 120-an identification module; 130-an update module; 140-authentication module.

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 part classification method, which is applied to a server, wherein electronic equipment can be Personal Computers (PCs), tablet computers, smart phones, Personal Digital Assistants (PDAs) and other electronic equipment with a logic calculation function, and can quickly and accurately classify processing types of various material part models in an enterprise, so that the resource cost of the enterprise is effectively reduced, and the working efficiency of the enterprise is improved.

Referring to fig. 1, fig. 1 is a schematic flow chart of a part classification method according to an embodiment of the present application, including the following steps:

and step S1, acquiring target model data corresponding to the current part model to be classified.

The target model data corresponding to at least one part model to be classified currently determined from a plurality of part models of an enterprise can be classified specifically.

For example, various parameters of the part model parameters, such as numbers, codes, names of names, specific dimension information of ceramic products, iron products, aluminum products, part models, functional information pertaining to parts such as connectors or cylinders, brand names, brand numbers, model numbers, and other model parameter data related to the models, may be included in the model data. Optionally, various parameter data in the model data may also be acquired and displayed as barcode information, for example, a two-dimensional code, a barcode, and a combination of the two-dimensional code and the barcode, and the server may acquire the model data in a scanning manner.

Optionally, before step S1, the method may further include steps Sa1-Sa 3:

and step Sa1, obtaining the part model to be classified currently from the part database.

The part database may include part models of all materials to be processed in an enterprise, for example, various different kinds of part models such as an internal part model designed inside the enterprise and a part model designed by external cooperation.

And step Sa2, judging whether the current part model to be classified meets the machining requirement.

After the design of the part model is completed, the worker can judge whether the part model meets the machining requirement.

For example, when judging whether the current part model to be classified meets the processing requirement, the judgment can be performed according to various parameters of the part model, such as a part number, a material number, a virtual value parameter, and the like. For example, a part model with a part number beginning with a capital letter B, an internal part model with a material number beginning with 2, and a part model with a virtual parameter NO are judged to meet the processing requirements, and are stored to be automatically written into the server, so that a data source is provided for subsequent classification work.

Optionally, the judgment criteria as to whether the machining requirements are met or not may be adaptively adjusted according to the specific conditions of the part models in the part database and the classification requirements of the part models.

And Sa3, storing the part model to be classified when the part model to be classified meets the machining requirement.

It is worth mentioning that whether the part model to be classified currently needs to be stored can be judged by judging the processing requirement of the part model to be classified currently, so that the subsequent classification processing is carried out on the part model to be classified currently, and the part classification is automatically carried out on the stored part model after the part model to be classified is stored. The influence of other irrelevant part models in the part database on the part classification is effectively reduced, and the pertinence of the part classification is improved.

After the execution of step S1, the process proceeds to step S2.

And step S2, recognizing the target model data to determine the machining type corresponding to the target model data.

The processing type may include processing type data and a processing code, the processing type data may be processing contents in a text form, such as ceramic products that need to be manufactured at a high temperature, and the processing code may be code data corresponding to the processing type data, and each processing content such as material, temperature, period, and the like has a corresponding code.

It should be noted that the identification may be performed according to a preset identification rule, and the preset identification rule may include: keyword recognition, for example, recognizing a keyword of a part name; part grade identification, such as a part importance grade, wherein the importance grade of the part can be a numerical grade, such as a first grade and a second grade, or a text grade, such as importance, generality and the like; process classification identification, such as high temperature, low temperature, milling, drilling, etc.; identifying the processing material, such as ceramic products, plastic products, iron products and the like; material identification, for example; the material of the part model is ceramic, plastic, wood and the like; identifying a machining range, for example, the interface part of the part model needs to be machined, including the specific range size of machining and the like; and identifying the supply period of the part, such as the use period of the part model, the specific supply period and the like. Alternatively, the recognition rules may be adjusted and modified according to the details of the part model and the actual requirements of the recognition process.

After the execution of step S2, the process proceeds to step S3.

And step S3, updating the bill of material where the current part model to be classified is located according to the machining type to obtain an updated bill of material.

The bill of materials comprises at least one part model to be classified and corresponding model data.

Optionally, the part database of the enterprise may further include a plurality of material lists, and when the machining types of the part models are classified, all the part models to be classified in the part database may be collected in one material list, where the material list includes at least one part model to be classified and corresponding model data. And automatically writing the identified machining type, adding the machining type to a corresponding position in a bill of materials where the current part model to be classified is located to update the bill of materials, and updating the model related information of the current part model to be classified to obtain an updated bill of materials.

After the execution of step S3, the process proceeds to step S4.

And step S4, verifying the updated bill of materials.

The updated bill of materials is verified, whether the machining types of the part models in the part classification method are successfully classified or not can be verified, and the classification accuracy and efficiency of each part model to be classified in the updated bill of materials are improved through verification.

Alternatively, step S4 may include steps S41-S43:

step S41 is performed to verify whether each of the part models to be classified in the updated bill of materials matches the corresponding machining type.

The updated bill of materials can contain a plurality of part models to be classified and corresponding model data, so that the updated bill of materials needs to be verified to verify whether each part model to be classified is matched with a corresponding machining type, and the influence of part models to be classified which fail in classification on other part models to be classified is reduced.

And step S42, when the part model to be classified is matched with the corresponding machining type, the updated bill of material is verified to be passed.

When the part model to be classified is verified to be matched with the corresponding machining type, the part model to be classified is successfully classified, and when each part model to be classified of the updated bill of materials is matched with the corresponding machining type, the verification of the updated bill of materials is passed, and all the part models to be classified in the updated bill of materials are successfully classified.

And step S43, when the part model to be classified is not matched with the corresponding machining type, the verification of the updated bill of material is represented to be failed.

When the part model to be classified is verified to be not matched with the corresponding machining type, the part model to be classified fails to be classified, the condition that the part model to be classified fails to be classified in the updated bill of materials is shown, and the verification of the updated bill of materials fails.

It is worth mentioning that the updated bill of materials is verified by verifying whether each part model to be classified in the updated bill of materials is matched with the corresponding machining type, so that the matching condition of each part model to be classified in the updated bill of materials can be verified. And the verification result of the updated bill of materials is judged according to the matching condition, so that the verification accuracy and pertinence are effectively improved.

After the execution of step S4, the process proceeds to step S5.

And step S5, if the updated bill of materials passes the verification, taking the updated bill of materials as a classification result.

And when the part model passes the verification, taking the machining type corresponding to each part model to be classified contained in the updated bill of materials as a classification result for classifying the part model.

Alternatively, the classification result may be displayed in the form of a machining type, a machining code, or a machining type + machining code. In the classification result, the processing type or the processing code of each part model in each part model to be classified in the updated bill of materials can be displayed, the part models belonging to the same processing type or the same processing code can be displayed in a centralized manner, and the classification result can be output in a chart or table form, so that a worker can check the classification result conveniently and perform subsequent operation and processing according to the classification result.

In the embodiment shown in fig. 1, the classification result of the machining type of the part can be obtained by obtaining and verifying the machining type. The time and the cost required when the machining types of the parts are classified are reduced, the efficiency and the accuracy of classifying the machining types of the parts are effectively improved, the rapid and accurate classification of the machining types of the material parts by enterprises is facilitated, and the working efficiency of the enterprises is improved.

Referring to fig. 2, fig. 2 is a detailed flowchart of a step S2 according to an embodiment of the present disclosure, which includes the following steps:

and step S21, classifying the target model data based on a preset classification rule to obtain classified data.

It should be noted that, because the target model data includes a plurality of model parameters with different meanings, when the plurality of model parameters are randomly identified one by one, the identified data type is complex or the identified data accuracy is low due to the correlation among the plurality of model parameters, and the identified data needs to be sorted and collected, which increases the workload and may have adverse effects. Therefore, before recognition, various model parameters in the target model data can be classified to improve the accuracy and efficiency of recognition.

Optionally, when performing classification, the preset classification rule may be to classify according to the relevant association between the model parameters, for example, to classify data such as numbers, codes, named names, etc. as name data; classifying data of ceramic products, iron products, aluminum products and the like into material parameters; classifying data such as specific dimension information of the part model and functional information belonging to parts such as a connecting piece or a cylinder into dimension functional data; data such as a brand name, a brand number, a model number, and the like are classified into brand model data. When the model parameters are acquired, the server can read data in a bar code scanning mode, and can adjust and modify the preset classification rules according to the actual conditions of the part models and the specific requirements of classification so as to be suitable for more classification conditions.

After the execution of step S21, the process proceeds to step S22.

And step S22, performing multi-stage identification on the classified data to obtain a processing type.

The multi-stage identification at least comprises two-stage identification, the classified data is subjected to multi-stage identification, the accuracy of the acquired processing type can be improved, the method is suitable for identifying the classified data of various different types, the identification range is expanded, and the identification precision is increased.

Optionally, the secondary identification includes a first-level identification and a second-level identification, and step S22 may further include steps S221 to S222;

and step S221, performing the first-stage identification on the classified data according to a preset priority identification rule, and matching in a machining type library according to a first-stage identification result to obtain a corresponding machining type when the identification is successful.

The multi-stage identification can be provided with a first-stage identification and a second-stage identification, and the identification rule of the first-stage identification is priority identification and can be used for preferentially identifying the classified data.

It should be noted that, when performing the first-level recognition according to the preset priority recognition rule, the priority recognition rule may include at least one of the following recognition contents: the method comprises the steps of keyword identification, part grade identification, process classification identification, processing material identification, processing range identification, part supply period identification and the like. The priority identification rule can be used for carrying out priority identification on parameters in the identification content, matching the identification content with the processing type library, and identifying some contents with higher matching degree with the processing type library, so that the first-level identification is successful. For example, obtaining the part name of the part a, such as the "air inflation axis or the air inflation axis code", with the indicative description, the keyword recognition in the preset recognition rule, the part name including the air inflation axis or the air inflation axis code, with the indicative description, and completely matching with the machining type about the air inflation axis in the machining type library, so that the first-stage recognition is successful. Optionally, the priority identification rule includes at least one identification content, and the identification content can be adjusted and modified according to the specific situation of the part model and the actual requirement of the identification process.

After step S221 is executed, step S222 is continuously executed.

And step S222, when the first-stage identification fails, performing second-stage identification on the classified data according to a preset detailed identification rule, and when the identification succeeds, matching in the machining type library according to a second-stage identification result to obtain a corresponding machining type.

After the first-stage identification fails, the classification data can be subjected to second-stage identification, so that the identification range of identification is increased, and the method is suitable for identifying multiple part models.

It should be noted that, when performing the second-level recognition according to the preset detailed recognition rule, the detailed recognition rule may include at least one of the following recognition contents: the method comprises the steps of keyword identification, part grade identification, process classification identification, processing material identification, processing range identification, part supply period identification and the like. When the first-stage identification fails, the parameters in the classified data cannot be completely matched with the data in the processing type library, the matching degree is low, in the detailed identification rule, semantic analysis can be performed on various parameters in the classified data, so that after the first-stage identification fails, the parameters in the classified data are subjected to semantic analysis, the data subjected to semantic analysis are matched with the processing type library, and when the matching degree is high, the second-stage identification succeeds. For example, descriptions such as "processing at 850 degrees centigrade" and the like in the part processing technology of the part B are obtained, and according to semantic analysis, the 850 degrees centigrade is analyzed to be high-temperature heat treatment, so that detailed contents in the contents are identified according to the process classification, and the detailed contents are matched in the range of the high-temperature heat treatment in the processing type library, so that the first-stage identification is successful. Optionally, the detailed identification rule includes at least one identification content, and the identification content can be adjusted and modified according to the specific situation of the part model and the actual requirement of the identification process.

Optionally, after the second-level recognition is performed, the multi-level recognition may further include an nth (N >2) level recognition, the nth level recognition may be performed on the classified data after the second-level recognition fails, and the nth level recognition may also include at least one of recognition contents such as keyword recognition, part grade recognition, process classification recognition, machining material recognition, machining range recognition, part supply cycle recognition, and the like. The recognition rule of the nth level recognition is more detailed than the recognition rule of the nth-1 level, for example, the contents of the material, the grade, the process classification and the like of the part model are judged for recognition according to the description which is more complex in parameters and has no definite directivity, and the recognition contents included in the recognition rule of the nth level can be adjusted and modified according to the specific situation of the part model and the actual requirements of the recognition process.

In the embodiment shown in fig. 2, the classified data is firstly subjected to the first-level identification through the priority identification rule, and then is subjected to the second-level identification through the detailed identification rule after the first-level identification fails, so that two levels of identification modes of priority identification or detailed identification can be provided for the classified data. And the second-stage identification is carried out after the first-stage identification fails, so that the identification efficiency and accuracy are effectively improved, the identification range is expanded, the method is suitable for the identification requirements of various different types of classified data, and various different conditions are met.

Referring to fig. 3, fig. 3 is a detailed flowchart illustrating a method for sorting parts according to an embodiment of the present application, and based on fig. 1, after step S4, the method further includes steps S6-S8:

and step S6, if the verification of the updated material list is not passed, screening out unmatched part models which are not matched with the corresponding processing types in the updated material list.

And screening unmatched part models which are unmatched to the processing types and fail in classification in the updated bill of materials, and performing subsequent operation aiming at the unmatched part models which fail in classification so as to perfect the whole classification process.

After the execution of step S6, the process proceeds to step S7.

And step S7, generating corresponding prompt information aiming at the unmatched part model.

The method and the device can generate corresponding prompt information aiming at the unmatched part model so as to prompt the unmatched part model matching failure, data missing and other related states. The unmatched part models are efficiently processed, the whole process of part classification in enterprises is perfected, and the method is suitable for various different part models.

Illustratively, the prompt message may include material codes, status information and details of missing information of the part model. For example, part C, numbered 101213, failed classification, lacked process and material information, and the like.

Optionally, the generated prompt information may also be sent to a work terminal used by the worker in the form of a short message or the like, so as to notify and remind the worker.

After the execution of step S7, the process proceeds to step S8.

And step S8, updating the model data of the unmatched part model according to the prompt information to obtain updated model data.

The state of the unmatched part model matching failure can be determined and notified through the prompt information, and model data of the unmatched part model can be supplemented and updated according to the prompt information. Under the condition that the model data of the part model in the part database is incomplete, the subsequent updating process of the unmatched model is perfected, the feedback and processing efficiency of the unmatched part model is effectively improved, and the limitation of the incomplete model data on part classification is reduced.

After step S5, steps S9-S10 may also be included;

and step S9, generating corresponding purchasing demand data according to the classification result.

The server can automatically read parameters according to the processing types or the processing codes in the classification results, and can generate corresponding purchasing demand data according to each read special technical requirement. The purchasing demand data can be displayed in the form of a table or a chart, the demands of updating each part model to be classified in the bill of materials can be arranged and listed, and a plurality of part models to be classified with the same purchasing demands can be displayed in a gathering mode.

Illustratively, the procurement requirements data may include requirements for a type of process or a code of process, e.g., temperature needs to be above 1000 degrees celsius, low temperature processing needs to be performed, drilling accuracy thresholds, etc.

After the execution of step S9, the process proceeds to step S10.

And step S10, matching the procurement demand data in a supply database to obtain a corresponding supply source.

The supply database contains specific processing data of multiple suppliers or supply sources in an enterprise, including various contents such as processing capacity, processing yield, processing period and the like. And matching in a supply database according to the purchasing demand data to a supply source meeting the purchasing demand data so as to provide a basis for purchasing at least one part model in the classification result. The follow-up purchasing process of the part model is perfected, the time for manually matching supply sources required by purchasing in an enterprise is saved, and the working efficiency of a purchasing end of the enterprise is improved.

Optionally, after determining the supply source of each part model to be classified in the updated material list, a purchasing list in a form of a table or a chart can be generated according to the supply source, so that a purchasing person can perform purchasing according to the purchasing list.

In the embodiment shown in fig. 3, the subsequent contents of passing verification and failing verification are specifically described, so that the overall process of the part classification method is perfected, and the working efficiency of an enterprise is improved.

Referring to fig. 4, fig. 4 is a schematic structural diagram of a part sorting apparatus according to an embodiment of the present application, where the part sorting apparatus 100 includes: an acquisition module 110, an identification module 120, an update module 130, and a verification module 140.

The obtaining module 110 is configured to obtain target model data corresponding to a current part model to be classified;

the identification module 120 is configured to identify the target model data to determine a processing type corresponding to the target model data;

the updating module 130 is configured to update a bill of materials where the current part model to be classified is located according to the processing type, so as to obtain an updated bill of materials, where the bill of materials includes at least one part model to be classified and corresponding model data;

a verification module 140, configured to verify the updated bill of materials; and if the verification of the updated bill of materials is passed, taking the updated bill of materials as a classification result.

The identification module 120 further includes: a classification submodule and a multi-stage identification submodule;

the classification submodule is used for classifying the target model data based on a preset classification rule to obtain classification data;

and the multi-stage identification submodule is used for carrying out multi-stage identification on the classified data to obtain a processing type, wherein the multi-stage identification at least comprises two-stage identification.

The secondary recognition comprises a first-stage recognition, a second-stage recognition and a multi-stage recognition submodule, and is also used for carrying out the first-stage recognition on the classified data according to a preset priority recognition rule, and when the recognition is successful, matching is carried out in a processing type library according to a first-stage recognition result to obtain a corresponding processing type;

and when the first-stage identification fails, performing second-stage identification on the classified data according to a preset detailed identification rule, and when the identification succeeds, matching in the machining type library according to a second-stage identification result to obtain a corresponding machining type.

The verification module 140 is further configured to verify whether each of the part models to be classified in the updated bill of materials matches a corresponding machining type;

when the part model to be classified is matched with the corresponding machining type, the updated bill of materials is verified to be passed;

and when the part model to be classified is not matched with the corresponding machining type, representing that the verification of the updated bill of material is failed.

The part sorting apparatus 100 further includes: the system comprises a prompt module, a supplement module, a purchase module and a storage module;

the prompting module is used for screening unmatched part models which are not matched with the corresponding machining types from the updated bill of materials if the updated bill of materials is not verified;

and generating corresponding prompt information aiming at the unmatched part model.

And the supplement module is used for updating the model data of the unmatched part model according to the prompt information to obtain updated model data.

The purchasing module is used for generating corresponding purchasing demand data according to the classification result;

and matching in a supply database according to the purchase demand data to obtain a corresponding supply source.

The storage module is used for acquiring a current part model to be classified from a part database;

judging whether the current part model to be classified meets the processing requirements;

and when the part model to be classified currently meets the machining requirement, storing the part model to be classified currently.

Since the principle of solving the problem by the apparatus in the embodiment of the present application is similar to that of the embodiment of the part classification method, the system in the embodiment of the present application may be implemented by referring to the description in the embodiment of the method, and repeated descriptions are omitted.

In the embodiment shown in fig. 4, through the operation of each module in the device, the time and cost required for classifying the machining types of the parts can be reduced, the efficiency and accuracy for classifying the machining types of the parts are effectively improved, an enterprise can rapidly and accurately classify the machining types of the material parts, and the working efficiency of the enterprise is improved.

The embodiment of the application further provides electronic equipment, which comprises a memory and a processor, wherein program instructions are stored in the memory, and when the processor reads and runs the program instructions, the steps in any one of the part classification methods provided by the embodiment are executed.

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 to execute the steps in the part classification method.

In summary, the embodiment of the application provides a part classification method, a device, an electronic device and a storage medium, by acquiring the processing types of various material part models and verifying the processing types of various part models to be classified in a bill of materials, the time and cost required for classifying the processing types of parts are reduced, the efficiency and accuracy for classifying the processing types of the parts can be effectively improved, and the working efficiency of enterprises is improved.

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, and for example, the block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices according to various embodiments of the present application. In this regard, each block in the block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions 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.