Recommendation Method, Device, Equipment and Storage Medium for Product Update

Abstract

This application relates to the field of artificial intelligence technology, and discloses a method, device, equipment and storage medium for product update recommendation. The method includes: obtaining product information of a target product, where the product information includes at least one of UI information, product code, and database configuration; obtaining an extended range corresponding to the product information in a preset database, and selecting several extended items from the extended range; obtaining audience information corresponding to the target product, and calculating the adhesion between the audience information and the extended items; taking the extended items with the adhesion greater than a preset adhesion threshold as product update recommendation items for the target product, so as to reduce the waste of development resources in the process of product improvement.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and particularly to a method, device, equipment and storage medium for product update recommendation. Background Art

[0002] As the company operates and develops, the products within the company will also be updated and improved. In the actual update and improvement process, it is often necessary to modify the original design documents. The more complex the product, the more content needs to be modified. When there is a deviation in one of the modification links, it may lead to the function of the improved product not meeting the market expectations and causing waste of development resources. Summary of the Invention

[0003] The main purpose of this application is to provide a method, device, equipment and storage medium for product update recommendation, aiming to solve the problem of excessive waste of development resources in the improvement process of existing products.

[0004] To achieve the above-mentioned invention purpose, this application proposes a method for product update recommendation, and the method includes:

[0005] Obtain the product information of the target product, where the product information includes at least one of UI information, product code, and database configuration;

[0006] Obtain the extended range corresponding to the product information in a preset database, and select several extended items from the extended range;

[0007] Obtain the audience information corresponding to the target product, and calculate the adhesion degree between the audience information and the extended items;

[0008] Use the extended items with the adhesion degree greater than a preset adhesion threshold as the product update recommendation items for the target product.

[0009] Further, the obtaining the extended range corresponding to the product information in a preset database includes:

[0010] Identify the product category of the target product according to the product information;

[0011] In a preset database, obtain the classification feature information of the classified products under the product category, and calculate the similarity between the classification feature information and the product information;

[0012] Obtain the extended range corresponding to the product information according to the similarity.

[0013] Further, after selecting several extended items from the extended range, it further includes:

[0014] Obtain industry materials corresponding to the target product and push the industry materials to the user, where each item of the industry materials respectively corresponds to at least one of the expansion items;

[0015] Receive an expansion item selection instruction returned by the user according to the industry materials, and use the expansion item selected by the expansion item selection instruction as the product update recommendation item of the target product.

[0016] Further, the pushing the industry materials to the user includes:

[0017] Generate corresponding UI controls according to the industry materials and the expansion items, where the UI controls are used to send expansion item selection instructions according to the touch signals of the user;

[0018] Display the UI controls on the display device of the user.

[0019] Further, the calculating the adhesion degree between the audience information and the expansion item includes:

[0020] Extract transaction behavior information of the audience for the expansion item from the audience information;

[0021] Calculate the adhesion degree between the audience information and the expansion item according to the transaction behavior information.

[0022] Further, the obtaining the expansion range corresponding to the product information in the preset database includes:

[0023] Construct a function tree according to the database;

[0024] Query similar nodes corresponding to the product information in the function tree, and use the successor nodes of the similar nodes as the expansion range.

[0025] Further, the querying similar nodes corresponding to the product information in the function tree includes:

[0026] Traverse each child node in the function tree through a preset breadth-first search algorithm, and calculate the similarity between each child node and the product category until each child node in the function tree is accessed;

[0027] After the traversal of the child nodes is completed, use the child nodes whose similarity meets the preset threshold range as the similar nodes.

[0028] This application also proposes a product update recommendation device, including:

[0029] An information acquisition module for acquiring product information of a target product, where the product information includes at least one of UI information, product code, and database configuration;

[0030] A product extension module for obtaining the extension range corresponding to the product information in a preset database and selecting several extension items from the extension range;

[0031] An adhesion degree calculation module for obtaining the audience information corresponding to the target product and calculating the adhesion degree between the audience information and the extension items;

[0032] An update recommendation module for using the extension items with the adhesion degree greater than a preset adhesion threshold as the product update recommendation items for the target product.

[0033] This application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0034] This application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0035] The product update recommendation method, device, equipment, and storage medium of this application obtain product information such as UI information, product code, and database configuration, so as to obtain the existing functions of the target product, providing a data basis for product update recommendation; by obtaining the extension range corresponding to the product information in a preset database, it can be market-oriented to accurately obtain the updatable range, and at the same time select extension items from the extension range, realizing the simplification of the update direction, improving the effectiveness of product updates, and reducing resource waste; by obtaining the audience information corresponding to the target product, the interest degree of customers in each extension item is determined according to the adhesion degree between the audience information and the extension items, improving the pertinence of product updates; by using the extension items with the adhesion degree greater than a preset adhesion threshold as the product update recommendation items for the target product, the limited development resources can be invested in the extension items with higher adhesion degrees, that is, expanding and updating in the directions that users are more interested in, improving the product update efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic flowchart of the product update recommendation method according to an embodiment of this application;

[0037] Figure 2 It is a schematic block diagram of the structure of the product update recommendation device according to an embodiment of this application;

[0038] Figure 3Structural schematic block diagram of a computer device according to an embodiment of the present application.

[0039] The realization of the purpose of the present application, functional features and advantages will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0040] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to 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.

[0041] Referring to Figure 1 , in order to achieve the above-mentioned invention purpose, the present application proposes a method for recommending product updates, and the method includes:

[0042] S1: Obtain product information of a target product, where the product information includes at least one of UI information, product code, and database configuration;

[0043] S2: Obtain an extended range corresponding to the product information in a preset database, and select several extension items from the extended range;

[0044] S3: Obtain audience information corresponding to the target product, and calculate the adhesion degree between the audience information and the extension items;

[0045] S4: Use the extension items with the adhesion degree greater than a preset adhesion threshold as product update recommendation items for the target product.

[0046] In this embodiment, by obtaining product information such as UI information, product code, and database configuration, the existing functions of the target product can be obtained, providing a data basis for product update recommendations; by obtaining the extended range corresponding to the product information in a preset database, the updatable range can be accurately obtained in a market-oriented manner, and at the same time, extension items are selected from the extended range, realizing the simplification of the update direction, improving the effectiveness of product updates, and reducing resource waste; by obtaining the audience information corresponding to the target product, the degree of interest of customers in each extension item is determined according to the adhesion degree between the audience information and the extension items, improving the pertinence of product updates; by using the extension items with the adhesion degree greater than the preset adhesion threshold as product update recommendation items for the target product, limited development resources can be invested in the extension items with higher adhesion degrees, that is, expand and update in the directions that users are more interested in, improving the efficiency of product updates.

[0047] For step S1, this embodiment is applied to product development, especially in product research and development and updates. Data acquisition, selection of expansion items, and calculation of adhesion can be performed based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is 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. A product update recommendation method provided by this embodiment, in order to improve the effectiveness of update development and reduce waste of development resources, the present invention obtains product information such as UI (User Interface) information, product code, and database configuration, so as to obtain the existing functions of the target product, providing a data basis for product update recommendations.

[0048] For step S2, after obtaining product information such as the UI information, product code, and database configuration of the target product, a comprehensive scan of the existing mature products in the preset database is performed for UI, interaction, code implementation, database configuration, etc. Through data analysis, the available expansion ability range of the current functions is obtained, and the available expansion ability range is compared with the product information of the target product. The difference between the two is used as the expansion range. Since in the actual implementation scenario, there may be hundreds or thousands of expandable directions for the same target product, in order to reduce waste of development resources, an appropriate number of items can be randomly selected from the expansion range as the above-mentioned expansion items. The present invention can accurately obtain the updatable range oriented by the market by obtaining the expansion range corresponding to the product information in the preset database. At the same time, selecting expansion items from the expansion range realizes the simplification of the update direction, improves the effectiveness of product updates, and reduces resource waste.

[0049] For step S3, in order to perform product updates more pertinently, the audience information of the target product can be obtained, that is, the customer information of those who purchase the target product. The adhesion between each expansion item is calculated based on the above-mentioned audience information. Among them, the above-mentioned audience information includes customers' purchase behaviors, attention behaviors, etc. The interest degree of customers in each expansion item is determined according to the adhesion, improving the pertinence of product updates.

[0050] For step S4, after calculating the adhesion, the interest degree of the customer in the expansion item can be determined according to the adhesion. Therefore, the adhesion threshold can be set according to the R & D ability of the enterprise, and the limited development resources are invested in the expansion items with higher adhesion, that is, the expansion and update are carried out in the direction that the user is more interested in, thereby improving the product update efficiency.

[0051] In one embodiment, obtaining the expansion range corresponding to the product information in the preset database includes:

[0052] S21: Identify the product category of the target product according to the product information;

[0053] S22: In a preset database, obtain the classification feature information of the classified products under the product category, and calculate the similarity between the classification feature information and the product information;

[0054] S23: Obtain the expansion range corresponding to the product information according to the similarity.

[0055] In this embodiment, the similarity between other mature products and the target product is obtained through data analysis, so as to obtain the available expansion ability range of the current function, improving the rationality and accuracy of expansion.

[0056] For step S21, in the actual R & D process, products of the same category may correspond to different names or models in different enterprises. To improve the practicality of the expansion range, this embodiment obtains the corresponding product category according to the product information. Exemplarily, in enterprise A, the name of the sweeping robot to be expanded may be Sweeping Machine SDX1, and in enterprise B, the name of the sweeping robot to be expanded may be Cleaning Robot TA553. Obviously, the above-mentioned "Sweeping Machine SDX1" and "Cleaning Robot TA553" both belong to the product category of "sweeping robot".

[0057] For step S22, in the existing database, for existing mature products, a comprehensive scan of UI interaction, code implementation, database configuration, etc. is performed. Through the keyword hashing algorithm, the semantic feature information corresponding to the product name of the classified products is extracted as the above-mentioned classification feature information, and through the keyword hashing algorithm, the semantic feature information corresponding to the product information is extracted as the product feature information of the target product; the Hamming distance between the classification feature information and the product feature information is calculated to obtain the similarity between the two.

[0058] For step S23, after obtaining the similarity between the above-mentioned mature classified products and the target product through the data analysis method of semantic feature similarity calculation, the classified products are screened according to the preset similarity range, that is, the classified products whose similarity meets the above similarity range are used as expandable products, and the set composed of all expandable products is used as the above expansion range, so as to obtain the available expansion ability range of the target product, while improving the rationality and accuracy of the expansion range.

[0059] In one embodiment, after selecting several expansion items from the expansion range, it further includes:

[0060] S201: Obtain the industry materials corresponding to the target product and push the industry materials to the user, where each item of the industry materials respectively corresponds to at least one of the expansion items;

[0061] S202: Receive the expansion item selection instruction returned by the user according to the industry materials, and use the expansion item selected by the expansion item selection instruction as the product update recommendation item for the target product.

[0062] In this embodiment, through personalized recommendation of the service, product update recommendation items identical to the user's expectations are generated, improving the controllability of the product recommendation direction.

[0063] For step S201, for personalized service content, it is a relatively weak link for developers. However, through big data analysis and simulating the ecological network algorithm, knowledge inferences in the industry can be obtained, and relevant industry content materials can be input into the document database accordingly for personalized service recommendation.

[0064] For step S202, when the user receives the industry materials and the corresponding expansion items, the user can select the corresponding expansion items according to the industry materials, that is, adopt a foolproof operation plan, only need to click to select without any redundant operations.

[0065] In one embodiment, the pushing the industry materials to the user includes:

[0066] S211: Generate corresponding UI controls according to the industry materials and the expansion items, where the UI controls are used to send expansion item selection instructions according to the touch signals of the user;

[0067] S212: Display the UI controls on the display device of the user.

[0068] In this embodiment, corresponding UI controls are generated for the industry materials and the expansion items and pushed to the display device of the user, so as to facilitate the user to select expansion items, improving the selection efficiency and intuitiveness.

[0069] For step S211, for the industry materials and the corresponding expansion items to be pushed, personalized adjustment can be performed through a visual interface to obtain different UI controls. The UI controls are used to display the industry materials and the corresponding expansion item information, and are also used to respond to the click instructions of the user, select the corresponding expansion items, and send the selected expansion items to the control center.

[0070] In one embodiment, the calculating the adhesion degree between the audience information and the expansion items includes:

[0071] S31: Extract from the audience information the transaction behavior information of the audience for the expansion items;

[0072] S32: Calculate the adhesion degree between the audience information and the extension item according to the transaction behavior information.

[0073] In this embodiment, by extracting the transaction behavior information of the audience and calculating the adhesion degree between the audience information and the extension item, it is convenient to determine whether each extension item can meet the needs of the audience, and reduces the probability that the product update direction deviates from the market demand.

[0074] For step S31, the above-mentioned transaction behavior information includes the number of transactions, transaction amount, etc.

[0075] For step S32, in a specific implementation, obtain the number of transactions and transaction amount generated by the audience for the products under the above extension item within a preset time period. When the number of transactions generated by the audience for the products under the extension item is 0, the adhesion degree is considered 0. When the number of transactions generated by the audience for the products under the extension item is greater than 0, set corresponding adhesion weights for the number of transactions and the transaction amount respectively, and perform weighted calculation on the number of transactions and the transaction amount according to the above adhesion weights to obtain the above adhesion degree. Among them, the number of transactions, the transaction amount, and the adhesion degree are all positively correlated. That is, the more the number of transactions of the audience for the products under the extension item, the stronger the adhesion degree; the higher the transaction amount of the audience for the products under the extension item, the stronger the adhesion degree. This is to facilitate determining whether each extension item meets the needs of the audience according to the adhesion degree, and to avoid the deviation between the product update direction and the market demand.

[0076] In one embodiment, the obtaining the extension range corresponding to the product information in the preset database includes:

[0077] S24: Construct a function tree according to the database;

[0078] S25: Query the similar nodes corresponding to the product information in the function tree, and use the successor nodes of the similar nodes as the extension range.

[0079] In this embodiment, node query is performed through the function tree corresponding to the database, so as to obtain an accurate extension range.

[0080] For step S24, after obtaining a database containing several product categories, the above product categories are classified hierarchically to obtain several category branches. Exemplarily, for products in the cleaning category, the corresponding first-level classification can be cleaning equipment, and the second-level classification can be intelligent cleaning equipment and non-intelligent cleaning equipment. For intelligent cleaning equipment, the third-level classification can include intelligent dishwashers, intelligent floor sweepers, intelligent washing machines, etc. For intelligent floor sweepers, the fourth-level classification can include pure floor-sweeping intelligent robots, mopping and sweeping integrated intelligent robots, and so on. By analogy, several category branches corresponding to different product categories are obtained. Each level of classification is used as a node, and the next level of classification is the successor node of the previous level of classification, thereby obtaining a complete functional tree. The above hierarchical processing can be completed manually or by a pre-trained classification model.

[0081] In one embodiment, querying for similar nodes corresponding to the product information in the functional tree includes:

[0082] S251: Through a preset breadth-first search algorithm, traverse each child node in the functional tree and calculate the similarity between each child node and the product category until each child node in the functional tree has been visited;

[0083] S252: After completing the traversal of the child nodes, use the child nodes whose similarity meets the preset threshold range as the similar nodes.

[0084] In this embodiment, the breadth-first search algorithm is used to traverse and visit the child nodes in the functional tree, and the corresponding similarity is obtained, so as to identify mutually similar nodes through the similarity and improve the accuracy of the expansion range.

[0085] For step S251, the above breadth-first search algorithm is a traversal algorithm. Starting from the root node of the functional tree, traverse the product categories under each child node. When each child node in the functional tree has been visited, the visit is aborted, thereby obtaining the similarity between the product category of each child node and the product information. Among them, the above similarity can be semantic similarity.

[0086] Referring to Figure 2 , the present application also proposes a recommendation device for product update, including:

[0087] An information acquisition module 100, configured to acquire product information of a target product, where the product information includes at least one of UI information, product code, and database configuration;

[0088] A product expansion module 200, configured to obtain an expansion range corresponding to the product information in a preset database and select several expansion items from the expansion range;

[0089] The adhesion calculation module 300 is configured to obtain the audience information corresponding to the target product and calculate the adhesion between the audience information and the extension item;

[0090] The update recommendation module 400 is configured to use the extension item with the adhesion greater than a preset adhesion threshold as the product update recommendation item for the target product.

[0091] In this embodiment, by obtaining product information such as UI information, product code, and database configuration, the existing functions of the target product are obtained, providing a data basis for product update recommendations; by obtaining the extension range corresponding to the product information in a preset database, the updatable range can be accurately obtained in a market-oriented manner. At the same time, by selecting extension items from the extension range, the update direction is streamlined, the effectiveness of product updates is improved, and resource waste is reduced; by obtaining the audience information corresponding to the target product and determining the interest degree of customers in each extension item according to the adhesion between the audience information and the extension item, the pertinence of product updates is improved; by using the extension item with the adhesion greater than the preset adhesion threshold as the product update recommendation item for the target product, limited development resources are invested in extension items with higher adhesion, that is, extended updates are performed in the direction that users are more interested in, improving the product update efficiency.

[0092] In one embodiment, the product extension module 200 is further configured to:

[0093] Identify the product category of the target product according to the product information;

[0094] In a preset database, obtain the classification feature information of the classified products under the product category and calculate the similarity between the classification feature information and the product information;

[0095] Obtain the extension range corresponding to the product information according to the similarity.

[0096] In one embodiment, the product extension module 200 is further configured to:

[0097] Obtain the industry materials corresponding to the target product and push the industry materials to the user, where each item of the industry materials respectively corresponds to at least one of the extension items;

[0098] Receive the extension item selection instruction returned by the user according to the industry materials, and use the extension item selected by the extension item selection instruction as the product update recommendation item for the target product.

[0099] In one embodiment, the product extension module 200 is further configured to:

[0100] Generate corresponding UI controls according to the industry data and the extension items, where the UI controls are used to send extension item selection instructions according to the touch signals of the user;

[0101] Display the UI controls on the display device of the user.

[0102] In one embodiment, the adhesion calculation module 300 is further configured to:

[0103] Extract transaction behavior information of the audience for the extension item from the audience information;

[0104] Calculate the adhesion between the audience information and the extension item according to the transaction behavior information.

[0105] In one embodiment, the product extension module 200 is further configured to:

[0106] Construct a function tree according to the database;

[0107] Query for similar nodes corresponding to the product information in the function tree, and use the successor nodes of the similar nodes as the extension scope.

[0108] In one embodiment, the product extension module 200 is configured to:

[0109] Traverse each child node in the function tree through a preset breadth-first search algorithm, and calculate the similarity between each child node and the product category until each child node in the function tree is accessed;

[0110] After completing the traversal of the child nodes, use the child nodes whose similarity meets the preset threshold range as the similar nodes.

[0111] Refer to Figure 3 , An embodiment of the present application further provides a computer device, which may be a server, and its internal structure may be as Figure 3As shown in the figure. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the recommended method for product updates. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a recommended method for product updates. The recommended method for product updates includes: obtaining product information of a target product, where the product information includes at least one of UI information, product code, and database configuration; obtaining an extended range corresponding to the product information in a preset database, and selecting several extended items from the extended range;

[0112] obtaining the audience information corresponding to the target product, and calculating the adhesion degree between the audience information and the extended items; using the extended items with the adhesion degree greater than a preset adhesion threshold as the recommended items for product updates of the target product.

[0113] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a recommended method for product updates, including the steps of: obtaining product information of a target product, where the product information includes at least one of UI information, product code, and database configuration; obtaining an extended range corresponding to the product information in a preset database, and selecting several extended items from the extended range;

[0114] obtaining the audience information corresponding to the target product, and calculating the adhesion degree between the audience information and the extended items; using the extended items with the adhesion degree greater than a preset adhesion threshold as the recommended items for product updates of the target product.

[0115] The above-mentioned recommended method, device, equipment and storage medium for product update obtain product information such as UI information, product code, database configuration, etc., so as to obtain the existing functions of the target product, providing a data basis for product update recommendation; by obtaining the extended range corresponding to the product information in a preset database, it is possible to accurately obtain the updatable range oriented to the market. At the same time, by selecting extended items in the extended range, the update direction is streamlined, the effectiveness of product update is improved, and resource waste is reduced; by obtaining the audience information corresponding to the target product and determining the interest degree of customers in each extended item according to the adhesion degree between the audience information and the extended items, the pertinence of product update is improved; by using the extended items with an adhesion degree greater than the preset adhesion threshold as the product update recommendation items for the target product, limited development resources can be invested in the extended items with higher adhesion degree, that is, expand and update in the direction that users are more interested in, improving the product update efficiency.

[0116] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned method embodiments. Among them, any reference to memory, storage, database or other media provided in the present application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0117] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such a process, device, article or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article or method including such an element.

[0118] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for recommending product updates, characterized in that, The method includes: Obtaining product information of a target product, where the product information includes at least one of UI information, product code, and database configuration, so as to obtain the existing functions of the target product; Obtaining the extension range corresponding to the product information in a preset database, and selecting several extension items from the extension range; Obtaining the audience information corresponding to the target product, and calculating the adhesion degree between the audience information and the extension items; Taking the extension items with the adhesion degree greater than a preset adhesion threshold as the product update recommendation items of the target product for extension update; The obtaining the extension range corresponding to the product information in a preset database includes: Identifying the product category of the target product according to the product information; In a preset database, obtaining the classification feature information of the classified products under the product category, and calculating the similarity between the classification feature information and the product information; Obtaining the extension range corresponding to the product information according to the similarity; The calculating the adhesion degree between the audience information and the extension items includes: Extracting the transaction behavior information generated by the audience for the extension items from the audience information; Calculating the adhesion degree between the audience information and the extension items according to the transaction behavior information.

2. The recommended method for product update according to claim 1, characterized in that, After selecting several extension items from the extension range, it further includes: Obtaining the industry materials corresponding to the target product, and pushing the industry materials to the user, where each industry material corresponds to at least one of the extension items; Receiving the extension item selection instruction returned by the user according to the industry materials, and taking the extension items selected by the extension item selection instruction as the product update recommendation items of the target product.

3. The recommended method for product update according to claim 2, characterized in that The pushing the industry materials to the user includes: Generating corresponding UI controls according to the industry materials and the extension items, where the UI controls are used to send extension item selection instructions according to the touch signals of the user; Displaying the UI controls on the display device of the user.

4. The recommended method for product update according to claim 1, wherein The obtaining the extension range corresponding to the product information in a preset database includes: Constructing a function tree according to the database; Querying the similar nodes corresponding to the product information in the function tree, and taking the successor nodes of the similar nodes as the extension range.

5. The recommended method for product update according to claim 4, characterized in that, The querying the similar nodes corresponding to the product information in the function tree includes: Traversing each child node in the function tree through a preset breadth-first search algorithm, and calculating the similarity between each child node and the product category until each child node in the function tree is accessed; After the traversal of the child nodes is completed, taking the child nodes with the similarity meeting the preset threshold range as the similar nodes.

6. A product update recommendation device for performing the method according to any one of claims 1-5, characterized in that, It includes: An information acquisition module, configured to obtain product information of a target product, where the product information includes at least one of UI information, product code, and database configuration; A product extension module, configured to obtain the extension range corresponding to the product information in a preset database, and select several extension items from the extension range; The adhesion calculation module is used to obtain the audience information corresponding to the target product and calculate the adhesion between the audience information and the extension item; The update recommendation module is used to use the extension item with the adhesion greater than the preset adhesion threshold as the product update recommendation item for the target product.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method described in any one of claims 1 to 5 are implemented.

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