Intelligent part searching method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a part intelligent search method and device, electronic equipment and a storage medium, and belongs to the technical field of intelligent search. The method comprises the steps of obtaining a product function demand input by a user, and performing semantic analysis on the product function demand to obtain a first part feature; matching the first part features with second part features in a preset part database to obtain a plurality of part matching results; and inputting the plurality of part matching results into a pre-trained analysis model to obtain a target part corresponding to the product function demand. According to the embodiment of the invention, the industrial parts meeting the requirements can be quickly and accurately searched from the database.

Description

Technical Field

[0001] The present application relates to the field of intelligent search technology, and in particular to a component intelligent search method and device, an electronic device, and a storage medium. Background Art

[0002] In view of the current situation that industrial parts are of various types and huge in number, in order to more efficiently manage and retrieve the required parts, existing technologies establish industrial databases to enable users to quickly and accurately locate and obtain the required parts information, thereby improving the efficiency of data management.

[0003] In the prior art, there are industrial parts databases such as Big Engineer, Today's Manufacturing and Cadenas. These industrial parts databases can be searched in the database according to the part name or picture input by the user. These databases require users to have at least a basic understanding of the parts. However, for novices or users who only know the functional requirements but are not clear about the specific implementation methods and the required part names, the search process may become relatively cumbersome and time-consuming, and it is difficult to accurately locate the appropriate parts in a short time. In addition, the existing database can only output the corresponding target parts according to the user's needs, and cannot output the associated parts that have a functional or assembly relationship with the target parts. After outputting the target parts, engineers are required to search for the associated parts one by one, which cannot meet the user's needs and is inefficient. Therefore, how to quickly and accurately search for parts that meet the needs from the database has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device for intelligent search of parts, an electronic device and a storage medium, aiming to quickly and accurately search for industrial parts that meet the needs from a database.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a component intelligent search method, the method comprising:

[0006] Acquire a product function requirement input by a user, and perform semantic analysis on the product function requirement to obtain a first component feature;

[0007] Matching the first component feature with a second component feature in a preset component database to obtain a plurality of component matching results;

[0008] Several component matching results are input into a pre-trained analysis model to obtain target components corresponding to the product functional requirements.

[0009] In some embodiments, the method for constructing a parts database includes:

[0010] Collect component information corresponding to a number of components, wherein the component information includes the name, function description, picture, price information and supplier information of the component;

[0011] Removing duplicate items from the component information to obtain a first processing result;

[0012] Correcting errors in the first processing result to obtain a second processing result;

[0013] The second processing result is standardized, and the parts database is constructed based on the standardized processing result.

[0014] In some embodiments, matching the first component feature with a second component feature in a preset component database to obtain a component matching result includes:

[0015] Extracting a second component feature from the component database;

[0016] Calculating the similarity between the first component feature and the second component feature;

[0017] Based on the similarity, a component matching result is obtained.

[0018] In some embodiments, the step of inputting the plurality of component matching results into a pre-trained analysis model to obtain a target component corresponding to the product functional requirement includes:

[0019] Inputting a plurality of the component matching results into a pre-trained analysis model;

[0020] Evaluate the matching results of several components by using the analysis model to obtain an evaluation result;

[0021] Based on the evaluation results, target components corresponding to the functional requirements of the product are obtained.

[0022] In some embodiments, after obtaining the target component corresponding to the product functional requirement, the method further includes:

[0023] Obtaining user feedback results on the target component;

[0024] The analysis model is optimized based on the feedback result.

[0025] In some embodiments, after obtaining the target component corresponding to the product functional requirement, the method further includes:

[0026] Acquire the target component selected by the user, and acquire a third component feature corresponding to the target component from the component database;

[0027] Search for several application cases containing the third component feature from a preset application case library, and present the descriptions corresponding to the application cases in the form of animated images or videos.

[0028] In some embodiments, after presenting the description corresponding to the application case in the form of an animated picture or video, the method further includes:

[0029] Acquire the application case selected by the user, and analyze the components used in the application case to obtain analysis results;

[0030] Based on the analysis result, an associated component corresponding to the target component is obtained.

[0031] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a component intelligent search device, the device comprising: an acquisition module, a matching module and an analysis module;

[0032] The acquisition module is used to acquire the product function requirement input by the user, and perform semantic analysis on the product function requirement to obtain the first component feature;

[0033] The matching module is used to match the first component feature with the second component feature in a preset component database to obtain a plurality of component matching results;

[0034] The analysis module is used to input the plurality of component matching results into a pre-trained analysis model to obtain target components corresponding to the product functional requirements.

[0035] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the intelligent parts search method described in the first aspect when executing the computer program.

[0036] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the intelligent parts search method described in the first aspect.

[0037] The intelligent parts search method and device, electronic device and storage medium proposed in the present application can quickly grasp the semantics corresponding to the product function requirements input by the user by performing semantic analysis on the product function requirements input by the user, so as to facilitate the subsequent matching with the second part features in the parts database; by matching the first part features with the second part features in the preset parts database, it is possible to quickly search for several related second part features from the database, so as to facilitate the subsequent evaluation of the second part features and then determine the most matching parts; by inputting several of the part matching results into the pre-trained analysis model, it is possible to quickly and accurately search for industrial parts that meet the requirements from the database. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a component intelligent search method provided in an embodiment of the present application;

[0039] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0040] Figure 3 yes Figure 1 Flow chart of step S103 in FIG.

[0041] Figure 4 It is a schematic diagram of the data structure relationship of the application case provided in the embodiment of the present application;

[0042] Figure 5 It is a structural schematic diagram of a component intelligent search device provided in an embodiment of the present application;

[0043] Figure 6 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0047] First, some nouns involved in this application are analyzed:

[0048] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0049] Natural language processing (NLP): NLP uses computers to process, understand and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is often used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0050] Machine Learning is a key technology in the field of artificial intelligence. It enables computers to learn and understand data like humans, extract the laws and patterns of data through training samples, and then apply these laws and patterns to new data to achieve automatic classification, prediction, decision-making and other operations.

[0051] Based on this, the embodiments of the present application provide a method and device for intelligent search of parts, an electronic device and a storage medium, which aim to quickly and accurately search for industrial parts that meet the needs from a database.

[0052] The intelligent parts search method and device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the intelligent parts search method in the embodiments of the present application is described.

[0053] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0054] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0055] The intelligent search method for parts and components provided in the embodiment of the present application relates to the field of artificial intelligence technology. The intelligent search method for parts and components provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can realize the application of the intelligent search method for parts and components, etc., but is not limited to the above forms.

[0056] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0057] It should be noted that in each specific implementation of the present application, when it is necessary to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0058] Figure 1 is an optional flowchart of the intelligent parts search method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S103.

[0059] Step S101, obtaining a product function requirement input by a user, and performing semantic analysis on the product function requirement to obtain a first component feature;

[0060] Step S102, matching the first component feature with a second component feature in a preset component database to obtain a plurality of component matching results;

[0061] Step S103, inputting a plurality of component matching results into a pre-trained analysis model to obtain target components corresponding to the product functional requirements.

[0062] In steps S101 to S103 shown in the embodiment of the present application, a first component feature is obtained by performing semantic analysis on the product function requirements input by the user, and then the first component feature is matched with a second component feature in a preset component database. Finally, several component matching results are input into a pre-trained analysis model to obtain a target component corresponding to the product function requirements, so that industrial components that meet the requirements can be searched quickly and accurately from the database.

[0063] In step S101 of some embodiments, the user is required to first input the product function requirements on the user side, wherein the product function requirements are freely input by the user. In order to ensure the accuracy of the subsequent semantic analysis, corresponding input rules can be set, which can be but not limited to limiting the number of words input by the user, etc. After the user has entered the product function requirements, it needs to be confirmed. At this time, the system can receive the product function requirements input by the user, and perform semantic analysis on the product function requirements through NLP technology to understand the semantics of the product function requirements input by the user, and convert the user's input into a more structured form, that is, extract the first component feature from the product function requirements. Among them, the NLP technology can be but not limited to BERT or GPT, etc., and the first component feature can be but not limited to keywords, phrases, semantic roles and other features.

[0064] It should be noted that the user interface can be, but is not limited to, created via HTML / CSS / JavaScript (possibly using React or Vue).

[0065] In step S101 of the embodiment of the present application, by performing semantic analysis on the product function requirements input by the user, the semantics corresponding to the product function requirements input by the user can be quickly grasped, so as to facilitate the subsequent matching with the second component feature in the component database. Step S101 can also perform semantic analysis in other ways, and the present embodiment is not limited thereto.

[0066] Before step S102 in some embodiments, the intelligent search method for parts also includes pre-building a parts database, which can be but not limited to being stored in SQLite, PostgreSQL or MongoDB, etc. The parts database includes but is not limited to basic information, functional descriptions and supplier information of various parts. Specifically, the construction method of the parts database includes: first, collecting parts information corresponding to a number of parts from parts manufacturers, suppliers or online parts markets, etc., wherein the parts information includes but is not limited to the name, functional description, picture, price information and supplier information of the parts, etc., and the functional description can include but is not limited to mechanical motion power function, transmission function, noise reduction, shock absorption, physical change functions such as heating and cooling, information transmission feedback function and assembly connection function, etc.; secondly, removing duplicate items in the parts information to obtain a first processing result; thirdly, correcting errors in the first processing result to obtain a second processing result; finally, standardizing the second processing result, and constructing the parts database based on the standardized processing result. It should be noted that removing duplicate items, correcting errors and standardizing can all be achieved through existing technologies, which are not the focus of this embodiment, so they will not be expanded.

[0067] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S203:

[0068] Step S201, extracting a second component feature from the component database;

[0069] Step S202, calculating the similarity between the first component feature and the second component feature;

[0070] Step S203: obtaining component matching results based on the similarity.

[0071] In step S201 of some embodiments, the second component feature may be extracted from the component database by, but not limited to, writing a query statement, etc., wherein the second component feature may be, but not limited to, features such as keywords, phrases, and semantic roles.

[0072] In step S202 of some embodiments, after the second component feature is extracted from the component database, the similarity between the first component feature and the second component feature can be directly calculated by using methods such as cosine similarity, Jaccard similarity, etc., but not limited to, or a similarity calculation method can be directly designed. It should be noted that the similarity calculation is not the focus of this embodiment, so it will not be expanded here.

[0073] In step S203 of some embodiments, after calculating the similarity between the features, it is necessary to obtain the component matching result based on the similarity. Specifically, a similarity threshold may be pre-set, but is not limited to, and when the similarity between the features meets the similarity threshold, the corresponding component may be directly output.

[0074] It should be noted that the similarity threshold can be set freely, and this embodiment is not limited thereto.

[0075] In step S102 shown in the embodiment of the present application, by calculating the similarity between the first component feature and the second component feature in a preset component database, several related second component features can be quickly matched from the database, which facilitates the subsequent evaluation of the second component features and further determines the best matching component.

[0076] See also Figure 3 In some embodiments, step S103 may include but is not limited to steps S301 to S303:

[0077] Step S301, inputting a plurality of component matching results into a pre-trained analysis model;

[0078] Step S302, evaluating a plurality of component matching results by using the analysis model to obtain an evaluation result;

[0079] Step S303: based on the evaluation result, obtaining target components corresponding to the product functional requirements.

[0080] In step S301 of some embodiments, several of the component matching results can be directly input into the pre-trained analysis model; wherein, the training process of the analysis model is specifically as follows: first, it is necessary to clarify that the goal of the analysis model is to evaluate the accuracy of the analysis model, and determine the accuracy as the evaluation criterion. Secondly, collect the data set related to the matching results, and perform data cleaning on the data set, wherein the data cleaning includes but is not limited to processing missing values, removing noise data, data standardization and other means, and divide the cleaned data set into a training set and a validation set, wherein the training set is used for model training, the validation set is used to adjust the model parameters, and the test set is used to finally evaluate the model performance. Thirdly, it is necessary to select a suitable analysis model according to the evaluation goal and data type, and the analysis model includes but is not limited to support vector machine SVM, random forest and neural network models, and use the training set to train the analysis model, and continuously adjust the parameters and structure of the model to optimize the model performance. Of course, in the training process, in order to prevent overfitting, it can also be alleviated by regularization, cross-validation, data enhancement and other methods. Finally, use the validation set to evaluate the trained analysis model to monitor the changes in model performance to improve the accuracy of the analysis model.

[0081] In step S302 of some embodiments, the analysis model is used to evaluate several of the component matching results to obtain an evaluation result; specifically, the analysis model can be used to evaluate several of the component matching results one by one to obtain an evaluation result, that is, to obtain a score corresponding to each component matching result.

[0082] In step S303 of some embodiments, based on the evaluation result, a target component corresponding to the functional requirement of the product is obtained. Specifically, a threshold value can be freely set first, and a computer program or other method can be used to determine whether the evaluation result meets the threshold requirement. When the evaluation result meets the threshold requirement, the target component corresponding to the evaluation result that meets the threshold requirement is output, and the target component is displayed on the user-side interface. At this time, the target component can also correspond to the functional requirement of the product, wherein the name, function description, picture, price information, supplier information and application case of the target component are displayed during the display; when the evaluation result does not meet the threshold requirement, it means that the component corresponding to the evaluation result is not the target component, and it should be eliminated and not displayed on the user side.

[0083] It should be noted that there is at least one target component to be output. When multiple target components are output, the user can slide or click on the user side to browse the detailed information of the target component.

[0084] In step S103 shown in the embodiment of the present application, the component matching result is evaluated using an analysis model. When the evaluation result meets the preset conditions, the most matching target component can be obtained, and then the industrial component that meets the requirements can be quickly and accurately searched from the database.

[0085] After step S103 in some embodiments, the method further includes: obtaining user feedback results on the target component, wherein the feedback results include but are not limited to satisfaction scores and specific comments; and optimizing the analysis model based on the feedback results. Specifically, a scoring system and a comment box may be set up, but are not limited to, to receive user feedback results.

[0086] By optimizing the analysis model based on user feedback, the accuracy and relevance of the search can be continuously improved.

[0087] After step S103 in some embodiments, it may also include providing the user with associated parts. Specifically, the user is required to first select a target part from a large number of target parts on the user side. After the user selects the target part, the user needs to confirm the selection. At this time, the system can receive the target part selected by the user, and obtain the third part feature corresponding to the target part from the part database by writing a query statement or using a search tool in the part database; and based on the third part feature, a computer program (which can be implemented by Flask or Django in Python) is used to search for several application cases containing the third part feature from a preset application case library, and a corresponding page is popped up or jumped in the user side page to display the description corresponding to the application case in the form of a moving picture or video. After the application case is displayed, the application case selected by the user is obtained, and the parts used in the application case are analyzed to obtain the analysis results. Based on the analysis results, the associated parts corresponding to the target part in the application case are obtained, and all the associated parts are displayed on the user side interface to facilitate the user to select. After the user selects the associated parts, the classification column selection of other associated parts can be quickly entered based on the associated parts.

[0088] It should be noted that it is necessary to pre-build an application case library of component products that is not limited to a specific model or parameter. It can be stored in, but not limited to, SQLite, PostgreSQL, or MongoDB. The data structure relationship diagram of the application case is as follows: Figure 4 As shown, the application case library includes but is not limited to the parts used, assembly methods, functions of the final product, working methods of parts, combinations of parts, and cooperative working principles with other parts. Specifically, the method for constructing the application case library includes: first, collecting different application cases from channels such as parts manufacturers, suppliers, or online parts markets, which show how parts are combined and assembled for specific products or equipment. Secondly, a detailed description is provided for each application case through artificial intelligence, including the required parts, assembly steps, uses of the final product, etc. Finally, multimedia content such as animated images and videos can be produced for each application case through tools such as FFmpeg, but is not limited to, to intuitively display the combination and assembly methods of parts. Constructing an application case library is not the focus of this embodiment, so it will not be expanded.

[0089] In this way, by recommending the components used in the application case selected by the user as related components to the user, the user can find supporting components more quickly when selecting components for the entire equipment and machine, thereby improving overall efficiency. On the one hand, it provides users in the R&D stage with different ideas for designing the entire machine, which is more conducive to the rapid iteration and improvement of design products. On the other hand, it also makes it easier for non-professional design engineers to quickly find all components of the design plan, improve the design plan, and reduce the risk of primary design defects.

[0090] See also Figure 5 , the embodiment of the present application also provides a component intelligent search device, which can implement the above-mentioned component intelligent search method, and the device includes: an acquisition module 100, a matching module 200 and an analysis module 300;

[0091] The acquisition module 100 is used to acquire the product function requirement input by the user, and perform semantic analysis on the product function requirement to obtain the first component feature;

[0092] The matching module 200 is used to match the first component feature with the second component feature in a preset component database to obtain a plurality of component matching results;

[0093] The analysis module 300 is used to input a plurality of component matching results into a pre-trained analysis model to obtain target components corresponding to the product functional requirements.

[0094] The specific implementation of the intelligent parts search device is basically the same as the specific implementation of the above-mentioned intelligent parts search method, and will not be repeated here.

[0095] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned intelligent parts search method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0096] See also Figure 6 , Figure 6 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0097] The processor 601 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0098] The memory 602 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 602, and the processor 601 calls and executes the intelligent search method for parts in the embodiment of this application;

[0099] Input / output interface 603, used to implement information input and output;

[0100] Communication interface 604, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0101] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0102] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0103] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned intelligent search method for parts is implemented.

[0104] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0105] The intelligent parts search method, intelligent parts search device, electronic device and storage medium provided in the embodiments of the present application perform semantic analysis on the product function requirements input by the user to obtain a first part feature, then match the first part feature with a second part feature in a preset part database, and finally input several of the part matching results into a pre-trained analysis model to obtain a target part corresponding to the product function requirements, so as to quickly and accurately search for industrial parts that meet the requirements from the database.

[0106] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0107] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0108] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0110] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0111] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0113] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0116] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A method for intelligent search of parts, characterized in that: The method comprises: Acquire a product function requirement input by a user, and perform semantic analysis on the product function requirement to obtain a first component feature; Matching the first component feature with a second component feature in a preset component database to obtain a plurality of component matching results; Several component matching results are input into a pre-trained analysis model to obtain target components corresponding to the product functional requirements.

2. The method according to claim 1, characterized in that The parts database construction method comprises: Collect component information corresponding to a number of components, wherein the component information includes the name, function description, picture, price information and supplier information of the component; Removing duplicate items from the component information to obtain a first processing result; Correcting errors in the first processing result to obtain a second processing result; The second processing result is standardized, and the parts database is constructed based on the standardized processing result.

3. The method according to claim 1, characterized in that The matching of the first component feature with the second component feature in a preset component database to obtain a component matching result includes: Extracting a second component feature from the component database; Calculating the similarity between the first component feature and the second component feature; Based on the similarity, a component matching result is obtained.

4. The method according to claim 1, characterized in that: The step of inputting the plurality of component matching results into a pre-trained analysis model to obtain target components corresponding to the product functional requirements includes: Inputting a plurality of the component matching results into a pre-trained analysis model; Evaluate the matching results of several components by using the analysis model to obtain an evaluation result; Based on the evaluation results, target components corresponding to the functional requirements of the product are obtained.

5. The method according to claim 1, characterized in that After obtaining the target components corresponding to the product functional requirements, the method further includes: Obtaining user feedback results on the target component; The analysis model is optimized based on the feedback result.

6. The method according to claim 1, characterized in that After obtaining the target components corresponding to the product functional requirements, the method further includes: Acquire the target component selected by the user, and acquire a third component feature corresponding to the target component from the component database; Search for several application cases containing the third component feature from a preset application case library, and present the descriptions corresponding to the application cases in the form of animated images or videos.

7. The method according to claim 6, characterized in that After presenting the description of the application case in the form of animated pictures or videos, it also includes: Acquire the application case selected by the user, and analyze the components used in the application case to obtain analysis results; Based on the analysis result, an associated component corresponding to the target component is obtained.

8. A component intelligent search device, characterized in that: The device comprises: an acquisition module, a matching module and an analysis module; The acquisition module is used to acquire the product function requirement input by the user, and perform semantic analysis on the product function requirement to obtain the first component feature; The matching module is used to match the first component feature with the second component feature in a preset component database to obtain a plurality of component matching results; The analysis module is used to input the plurality of component matching results into a pre-trained analysis model to obtain target components corresponding to the product functional requirements.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the intelligent parts search method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the intelligent parts search method according to any one of claims 1 to 7 is implemented.

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