Part type selection recommendation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a part type selection recommendation method and device, electronic equipment and a storage medium, and belongs to the technical field of intelligent type selection. The method comprises the following steps: receiving first demand data of a part category input by a user; calculating the first demand data to obtain a key parameter corresponding to the part category; matching the key parameters with component parameters in a preset industrial resource library to obtain a candidate component list; and evaluating the candidate part list to obtain an evaluation result, and generating a part recommendation list based on the evaluation result. According to the embodiment of the invention, the overall efficiency and accuracy of part type selection recommendation can be improved.

Description

Technical Field

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

[0002] The selection of standard mechanical parts has a vital impact on the performance and reliability of the mechanical system. By scientifically and rationally selecting standard parts, the overall performance of the mechanical system can be improved, the failure rate can be reduced, and the reliability and stability of the mechanical system can be improved.

[0003] The traditional method of mechanical parts selection is highly dependent on the personal experience of mechanical engineers and tedious manual calculation steps. Therefore, this method is not only inefficient, but also often accompanied by a large risk of error, and may introduce deviations due to information asymmetry. At present, there is still a lack of automated calculation tools to respond to engineers' input needs and optimize the selection process through intelligent recommendations, which in turn affects the accuracy of parts selection recommendations. Therefore, how to improve the overall efficiency and accuracy of parts selection recommendations 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 component selection recommendation method and device, an electronic device and a storage medium, aiming to improve the overall efficiency and accuracy of component selection recommendation.

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

[0006] receiving first demand data of a component category input by a user;

[0007] Calculating the first demand data to obtain key parameters corresponding to the component category;

[0008] Matching the key parameters with the component parameters in a preset industrial resource library to obtain a candidate component list;

[0009] The candidate parts list is evaluated to obtain evaluation results, and a parts recommendation list is generated based on the evaluation results.

[0010] In some embodiments, the first demand data includes: functional requirements, performance parameters and working environment.

[0011] In some embodiments, the receiving first demand data of a component category input by a user includes:

[0012] receiving a component category selected by a user, and receiving second demand data input by the user for the component category;

[0013] The second demand data is standardized to obtain demand data corresponding to the component category.

[0014] In some embodiments, calculating the first demand data to obtain key parameters corresponding to the component category includes:

[0015] A third-party calculation engine or algorithm library is called to calculate the first demand data to obtain key parameters corresponding to the component category.

[0016] In some embodiments, the method for constructing the industrial resource library includes:

[0017] Collecting first component information corresponding to a plurality of components, wherein the first component information includes basic information, performance parameters, material characteristics, and supplier information;

[0018] An industrial resource library is constructed based on the first component information corresponding to a plurality of components.

[0019] In some embodiments, after the industrial resource library is constructed based on the first component information corresponding to the plurality of components, the method further includes:

[0020] Second component information corresponding to a plurality of components is collected, and the industrial resource library is updated based on the second component information.

[0021] In some embodiments, after generating a parts recommendation list based on the evaluation results, the method further includes:

[0022] Obtain user feedback information on the component recommendation list, and optimize the component selection recommendation method based on the feedback information.

[0023] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a component selection recommendation device, the device comprising: a receiving module, a calculation module, a matching module and a recommendation module;

[0024] The receiving module is used to receive first demand data of a component category input by a user;

[0025] The calculation module is used to calculate the first demand data to obtain key parameters corresponding to the component category;

[0026] The matching module is used to match the key parameters with the component parameters in the preset industrial resource library to obtain a candidate component list;

[0027] The recommendation module is used to evaluate the candidate parts list, obtain evaluation results, and generate a parts recommendation list based on the evaluation results.

[0028] 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 component selection recommendation method described in the first aspect when executing the computer program.

[0029] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the component selection recommendation method described in the first aspect is implemented.

[0030] The present application proposes a component selection recommendation method and device, electronic device and storage medium, which receive first demand data of a component category input by a user, and calculate the first demand data to obtain key parameters corresponding to the component category. The key parameters of the component category can be automatically calculated, which is convenient for subsequent use of data in an industrial resource library to generate a candidate component list; the candidate component list is obtained by matching the key parameters with component parameters in a preset industrial resource library, and a candidate component list that meets the requirements can be quickly obtained from the industrial resource library; the candidate component list is evaluated to obtain an evaluation result, and a component recommendation list is generated based on the evaluation result. The candidate component list can be evaluated to optimize the generation of the component recommendation list, thereby improving the overall efficiency and accuracy of component selection recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of a component selection recommendation method provided in an embodiment of the present application;

[0032] Figure 2 yes Figure 1 Flow chart of step S101 in FIG.

[0033] Figure 3 It is a structural schematic diagram of a component selection recommendation device provided in an embodiment of the present application;

[0034] Figure 4 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0035] 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.

[0036] 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.

[0037] 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.

[0038] First, some nouns involved in this application are analyzed:

[0039] 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.

[0040] Based on this, the embodiments of the present application provide a component selection recommendation method and device, an electronic device and a storage medium, aiming to improve the overall efficiency and accuracy of component selection recommendation.

[0041] The component selection recommendation method and device, electronic device and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the component selection recommendation method in the embodiments of the present application is described.

[0042] 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.

[0043] 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.

[0044] A component selection recommendation method provided in an embodiment of the present application relates to the field of artificial intelligence technology. A component selection recommendation method provided in an embodiment of the present application can be applied to a terminal, can be applied to a server side, or can 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 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 be an application that implements a component selection recommendation method, etc., but is not limited to the above forms.

[0045] 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.

[0046] 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.

[0047] Figure 1 is an optional flowchart of a component selection recommendation method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0048] Step S101, receiving first demand data of a component category input by a user;

[0049] Step S102, calculating the first demand data to obtain key parameters corresponding to the component category;

[0050] Step S103, matching the key parameters with the component parameters in the preset industrial resource library to obtain a candidate component list;

[0051] Step S104: Evaluate the candidate parts list to obtain evaluation results, and generate a parts recommendation list based on the evaluation results.

[0052] Steps S101 to S104 shown in the embodiment of the present application calculate the first demand data input by the user to obtain the key parameters corresponding to the component category, then match the key parameters with the component parameters in the preset industrial resource library, and evaluate and optimize the matched candidate component list to generate a component recommendation list, which can improve the overall efficiency and accuracy of component selection recommendations.

[0053] See also Figure 2 In some embodiments, a component selection recommendation method in step S101 may include but is not limited to steps S201 to S202:

[0054] Step S201, receiving a component category selected by a user, and receiving second demand data input by the user for the component category;

[0055] Step S202: normalize the second demand data to obtain demand data corresponding to the component category.

[0056] In step S201 of some embodiments, the first requirement data includes but is not limited to functional requirements, performance parameters and working environment, etc. The user needs to first select a part category from a large number of part categories on the user side. After the user selects the part category, it needs to be confirmed. At this time, the system can receive the part category determined by the user and pop up or jump to the corresponding page to facilitate the user to enter the second requirement data on the corresponding page. The corresponding page includes but is not limited to modules such as input boxes, forms and drop-down menus. Specifically, the user can freely enter the requirement data in the input box, fill in the requirement data in the form, or select the requirement data from the drop-down menu to enter the second requirement data.

[0057] In step S202 of some embodiments, after the background receives the second demand data input by the user, the second demand data will be standardized to convert the second demand data into the format required by the third-party computing engine or algorithm library, that is, to ensure that the demand data meets the format required for the third-party computing engine or algorithm library to calculate, wherein the data standardization includes but is not limited to data cleaning, data type conversion, data format conversion and data unit conversion, etc.: the data cleaning includes but is not limited to removing duplicate values, filling missing values ​​and filtering outliers, etc. It should be noted that data standardization is not the focus of this embodiment, so it will not be expanded here.

[0058] Step S101 shown in the embodiment of the present application receives the demand data of the component category input by the user, and converts the demand data into a format that satisfies the subsequent calculation by a third-party calculation engine or algorithm library. This can facilitate the subsequent direct and rapid calling of the corresponding third-party calculation engine or algorithm library to directly calculate the demand data, facilitate the subsequent matching with the component parameters in the industrial resource library, and improve efficiency.

[0059] In step S102 of some embodiments, the first requirement data is calculated by calling a third-party calculation engine or algorithm library to obtain the key parameters corresponding to the component category. The third-party calculation engine may, but is not limited to, calculate the bearing capacity, temperature resistance, corrosion resistance, etc. of the first requirement data, and the third-party calculation engine may, but is not limited to, physical models, engineering standards, and empirical data, etc.; and the algorithm library contains algorithms and formulas for calculating part performance parameters, including but not limited to calculation formulas such as stress calculation, thermal expansion calculation, and material strength calculation. Step S102 may also calculate the first requirement data in other ways, and this embodiment is not limited thereto.

[0060] Step S102 shown in the embodiment of the present application can obtain key parameters corresponding to the component category by directly and quickly calling the corresponding third-party calculation engine or algorithm library to directly calculate the demand data, which can facilitate subsequent matching with the component parameters in the industrial resource library and improve efficiency.

[0061] Before step S103 in some embodiments, the component selection recommendation method further includes pre-building an industrial resource library, which includes but is not limited to basic information, performance parameters, material characteristics and supplier information of various components. Specifically, the method for building an industrial resource library includes: collecting first component information corresponding to a number of components from component standards issued by the state, product information corresponding to manufacturers, etc., wherein the first component information includes but is not limited to basic information, performance parameters, material characteristics and supplier information, etc.; and building an industrial resource library based on the first component information corresponding to a number of components. Among them, the basic information includes but is not limited to model and specification, the performance parameters include but are not limited to load capacity and high temperature resistance, the material characteristics include but are not limited to strength and hardness, and the supplier information includes but is not limited to price and delivery time. After the industrial resource library is built, it is also necessary to continuously collect second component information corresponding to a number of components in the new component standards issued by the state and the product information updated by the manufacturer, and update the industrial resource library based on the second component information. The industrial resource library can also be built and updated in other ways, and this embodiment is not limited to this.

[0062] In this way, by continuously collecting information on various existing parts and components to update the industrial resource library, it can be ensured that the data in the industrial resource library can be updated in real time to reflect the latest parts technology and market information.

[0063] In step S103 of some embodiments, by matching the calculated key parameters with the component parameters in the preset industrial resource library, specifically, it is necessary to use the key parameters as search conditions in the industrial resource library to search, and the search can be, but not limited to, by writing query statements or using the search tools in the industrial resource library to find matching parts, and compare whether each component parameter is consistent with the key parameter or close enough, and when the component parameter is consistent with the key parameter or close enough, then output a list of candidate components that match the key parameter requirements; wherein the list of candidate components includes but is not limited to the name, picture, detailed specifications, performance parameters, supplier information and price of the component. It should be noted that during the search process, it is necessary to adjust the search conditions to expand or narrow the search scope, and to pre-set a certain degree of parameter deviation to match the list of candidate components that meet the preset parameter deviation.

[0064] In step S103 shown in the embodiment of the present application, by matching the key parameters with the component parameters in the preset industrial resource library, a list of candidate components matching the key parameter requirements can be quickly screened out, thereby improving the overall efficiency of component selection recommendation.

[0065] In step S104 of some embodiments, after comprehensively evaluating the candidate parts list from factors such as performance, cost-effectiveness and supplier evaluation, a parts recommendation list can be generated by sorting the comprehensive scores in descending order according to the evaluation results including the comprehensive scores. After the parts recommendation list is obtained, the parts recommendation list will be displayed in the form of video or animated images, wherein the parts recommendation list includes but is not limited to the name, picture, detailed specifications, performance parameters, supplier information and price of the parts.

[0066] In step S104 shown in the embodiment of the present application, by comprehensively evaluating the candidate component list, the candidate component list can be optimized to improve the accuracy of the component selection recommendation.

[0067] After step S104 in some embodiments, feedback information from users on the component recommendation list is obtained, wherein the feedback information includes but is not limited to satisfaction ratings and specific comments, and based on the feedback information, the component selection recommendation method is optimized. Specifically, a rating system and a comment box may be set, but are not limited to, to receive user feedback information.

[0068] In this way, optimizing the component selection recommendation method based on user feedback information can improve the accuracy of component selection recommendations.

[0069] See also Figure 3 , the embodiment of the present application also provides a component selection recommendation device, which can implement the above-mentioned component selection recommendation method, and the device includes: a receiving module 100, a calculation module 200, a matching module 300 and a recommendation module 400;

[0070] The receiving module 100 is used to receive first demand data of a component category input by a user;

[0071] The calculation module 200 is used to calculate the first demand data to obtain key parameters corresponding to the component category;

[0072] The matching module 300 is used to match the key parameters with the component parameters in the preset industrial resource library to obtain a candidate component list;

[0073] The recommendation module 400 is used to evaluate the candidate parts list, obtain evaluation results, and generate a parts recommendation list based on the evaluation results.

[0074] The specific implementation of the component selection recommendation device is basically the same as the specific implementation of the above-mentioned component selection recommendation method, and will not be repeated here.

[0075] 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 component selection recommendation method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0076] See also Figure 4 , Figure 4 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0077] The processor 401 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;

[0078] The memory 402 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 402 can store an operating system and other applications. 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 402, and the processor 401 calls and executes a component selection recommendation method of the embodiment of this application;

[0079] Input / output interface 403, used to implement information input and output;

[0080] Communication interface 404, 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.);

[0081] Bus 405 , which transmits information between various components of the device (e.g., processor 401 , memory 402 , input / output interface 403 , and communication interface 404 );

[0082] The processor 401 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via the bus 405 .

[0083] 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, it implements the above-mentioned component selection recommendation method.

[0084] 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.

[0085] The embodiments of the present application provide a component selection recommendation method, a component selection recommendation device, an electronic device, and a storage medium. The method receives first demand data of a component category input by a user, and calculates the first demand data to obtain key parameters corresponding to the component category. The key parameters of the component category can be automatically calculated to facilitate the subsequent use of data in an industrial resource library to generate a candidate component list; a candidate component list is obtained by matching the key parameters with component parameters in a preset industrial resource library, and a candidate component list that meets the requirements can be quickly obtained from the industrial resource library; an evaluation result is obtained by evaluating the candidate component list, and a component recommendation list is generated based on the evaluation result. The candidate component list can be evaluated to optimize the generation of the component recommendation list, thereby improving the overall efficiency and accuracy of component selection recommendation.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 component selection recommendation method, characterized in that: The method comprises: receiving first demand data of a component category input by a user; Calculating the first demand data to obtain key parameters corresponding to the component category; Matching the key parameters with the component parameters in a preset industrial resource library to obtain a candidate component list; The candidate parts list is evaluated to obtain evaluation results, and a parts recommendation list is generated based on the evaluation results.

2. The method according to claim 1, characterized in that The first demand data includes: functional requirements, performance parameters and working environment.

3. The method according to claim 1, characterized in that The receiving of first demand data of a component category input by a user comprises: receiving a component category selected by a user, and receiving second demand data input by the user for the component category; The second demand data is standardized to obtain demand data corresponding to the component category.

4. The method according to claim 1, characterized in that: The calculating of the first demand data to obtain key parameters corresponding to the component category includes: A third-party calculation engine or algorithm library is called to calculate the first demand data to obtain key parameters corresponding to the component category.

5. The method according to claim 1, characterized in that The method for constructing the industrial resource library comprises: Collecting first component information corresponding to a plurality of components, wherein the first component information includes basic information, performance parameters, material characteristics, and supplier information; An industrial resource library is constructed based on the first component information corresponding to a plurality of components.

6. The method according to claim 5, characterized in that After the industrial resource library is constructed based on the first component information corresponding to the plurality of components, the method further includes: Second component information corresponding to a plurality of components is collected, and the industrial resource library is updated based on the second component information.

7. The method according to any one of claims 1 to 6, characterized in that: After generating a parts recommendation list based on the evaluation results, the method further includes: Obtain user feedback information on the component recommendation list, and optimize the component selection recommendation method based on the feedback information.

8. A component selection recommendation device, characterized in that: The device comprises: a receiving module, a calculating module, a matching module and a recommending module; The receiving module is used to receive first demand data of a component category input by a user; The calculation module is used to calculate the first demand data to obtain key parameters corresponding to the component category; The matching module is used to match the key parameters with the component parameters in the preset industrial resource library to obtain a candidate component list; The recommendation module is used to evaluate the candidate parts list, obtain evaluation results, and generate a parts recommendation list based on the evaluation results.

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 a component selection recommendation method as described in 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, a component selection recommendation method according to any one of claims 1 to 7 is implemented.

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