Processing method and system for efficiently updating menu

Abstract

The invention discloses a processing method and system for efficiently updating a menu. The method comprises the following steps: acquiring menu comment information; performing coding processing on the menu comment information to obtain target coding feature information; and performing feature extraction analysis processing on the target coding feature information to obtain target analysis result information.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a processing method and system for efficiently updating menus. Background Art

[0002] The large amount of comment data that users usually leave on catering platforms has become a valuable resource. Through big data analysis, users' needs and preferences for food taste, nutrition and health can be mined to provide a basis for updating menus. How to conduct more detailed and accurate needs and preferences analysis on user comments has become one of the important research directions for efficient updating of menus. Therefore, a processing method and system for efficient updating of menus is provided, which can accurately and efficiently extract comment features through fine-grained analysis of user comment information, and realize accurate analysis of user preferences for menus. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a processing method and system for efficiently updating menus, which is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby realizing accurate analysis of user preferences for menus.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a processing method for efficiently updating a menu, the method comprising:

[0005] Get recipe review information;

[0006] Encoding the recipe review information to obtain target encoding feature information;

[0007] The target coding feature information is subjected to feature extraction and analysis processing to obtain target analysis result information.

[0008] A second aspect of an embodiment of the present invention discloses a processing system for efficiently updating a menu, the system comprising:

[0009] The acquisition module is used to obtain recipe review information;

[0010] A first processing module is used to encode the recipe review information to obtain target encoding feature information;

[0011] The second processing module is used to perform feature extraction and analysis on the target coding feature information to obtain target analysis result information.

[0012] The third aspect of the present invention discloses another processing system for efficiently updating a menu, the system comprising:

[0013] A memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the processing method for efficient updating of menus disclosed in the first aspect of the embodiment of the present invention.

[0016] A fourth aspect of the present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the processing method for efficient updating of menus disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a schematic diagram of a scenario of a processing system for efficiently updating a menu provided by an embodiment of the present invention;

[0019] Figure 2 It is a flowchart of a processing method for efficiently updating a menu disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural schematic diagram of a processing system for efficiently updating a menu according to an embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of the structure of another processing system for efficiently updating a menu according to an embodiment of the present invention;

[0022] Figure 5 It is a structural schematic diagram of a target feature extraction model disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0025] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0027] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0028] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. 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. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0029] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0030] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further perform graphic processing so that computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0031] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0032] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are often used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In an embodiment of the present application, the large model may be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyi Tongwen model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0033] The embodiments of the present application provide a processing method, system, computer device, and computer-readable storage medium for efficiently updating a menu, which are described in detail below.

[0034] See also Figure 1 , Figure 1 Schematic diagram of a scenario of a processing system for efficiently updating a menu provided in an embodiment of the present application. The processing system for efficiently updating a menu may include a computer device 100, in which the processing system for efficiently updating a menu is integrated. Figure 1 Computer equipment in.

[0035] In the embodiment of the present application, the computer device 100 is mainly used to obtain recipe review information;

[0036] Encoding the recipe review information to obtain target encoding feature information;

[0037] The target coding feature information is subjected to feature extraction and analysis processing to obtain target analysis result information.

[0038] It can accurately and efficiently extract comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0039] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0040] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0041] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the processing system for efficiently updating menus can also include one or more other services, which are not specifically limited here.

[0042] In addition, if Figure 1 As shown, the processing system for efficiently updating menus may also include a memory 200 for storing data, such as image data, location information, and the like.

[0043] It should be noted that Figure 1 The scenario diagram of the processing system for efficiently updating menus shown is merely an example. The processing system and scenario for efficiently updating menus described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art will appreciate that with the evolution of the processing system for efficiently updating menus and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0044] The present invention discloses a processing method and system for efficiently updating menus, which is beneficial for accurately and efficiently extracting comment features through fine-grained analysis of user comment information, and realizing accurate analysis of user preferences for menus. The following are detailed descriptions.

[0045] Embodiment 1

[0046] See also Figure 2 , Figure 2 : is a flowchart of a method for efficiently updating a menu disclosed in an embodiment of the present invention. Figure 2The processing method for efficiently updating menus described above is applied to a management system, such as a local server or a cloud server for management, and is not limited in the embodiments of the present invention. Figure 2 As shown, the processing method for efficiently updating the menu may include the following operations:

[0047] 101. Get recipe review information.

[0048] 102. Encode the recipe review information to obtain target encoding feature information.

[0049] 103. Perform feature extraction and analysis on the target coding feature information to obtain target analysis result information.

[0050] It should be noted that the above recipe review information represents the user's evaluation of the food corresponding to different recipes, which may be collected on a catering evaluation platform, and the embodiment of the present invention does not limit this.

[0051] It should be noted that the above target analysis result information represents the classification result of the user's evaluation sentiment on the menu. Based on the target analysis result information, the user's preference for the food corresponding to the menu can be analyzed, so as to achieve optimization and update of the menu, which is not limited in the embodiment of the present invention.

[0052] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0053] In an optional embodiment, in the above step 102, the recipe review information is encoded to obtain target encoding feature information, including:

[0054] Perform word segmentation processing on the recipe review information to obtain first review word segmentation information;

[0055] Remove stop words from the first comment segmentation information to obtain the second comment segmentation information;

[0056] The second comment word segmentation information is encoded to obtain target encoding feature information.

[0057] It should be noted that the above-mentioned word segmentation processing of the recipe review information is to split the words in the review text into meaningful word units to facilitate subsequent encoding and feature extraction analysis processing. It can be achieved by rule-based word segmentation, statistics-based word segmentation and deep learning-based word segmentation processing methods, which is not limited in the embodiments of the present invention.

[0058] It should be noted that the above-mentioned processing of removing stop words from the first comment segmentation information is to remove the stopped words in the first comment segmentation information through a stop word table set by the user to improve the efficiency of subsequent encoding and feature extraction and analysis processing, which is not limited in the embodiment of the present invention.

[0059] It should be noted that the encoding process of the second comment word segmentation information is to encode the word segmentation information so as to convert the text into target encoding feature information in the form of a vector, which is not limited in the embodiment of the present invention.

[0060] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0061] In another optional embodiment, encoding processing is performed on the second comment segmentation information to obtain target encoding feature information, including:

[0062] Using the first encoding model to encode the second comment segmentation information to obtain first encoding feature information;

[0063] Using the second encoding model to encode the second comment segmentation information to obtain second encoding feature information;

[0064] Performing feature embedding processing on the second coded feature information to obtain third coded feature information;

[0065] The first coding feature information and the third coding feature information are fused to obtain fourth coding feature information;

[0066] The fourth coding feature information is subjected to connection activation processing to obtain target coding feature information.

[0067] It should be noted that, considering that the menu reviews are basically in Chinese, the BERT model is selected as the first encoding model to fully utilize its excellent understanding ability of Chinese text and realize deep text feature extraction, which is not limited in the embodiment of the present invention.

[0068] It should be noted that the above-mentioned second encoding model can be constructed based on a graph neural network to achieve more fine-grained feature extraction of comment information, better understand the structure and semantics of comment information, and achieve more fine-grained feature representation, which is not limited in the embodiments of the present invention.

[0069] It should be noted that the above-mentioned feature embedding processing of the second encoded feature information is to first reduce the dimension of the second encoded feature information, and then represent it as a dense vector to facilitate fusion processing with the first encoded feature information. It can be implemented based on Word2Vec, GloVe, FastText, etc., and the embodiment of the present invention is not limited.

[0070] It should be noted that the above-mentioned fusion processing of the first coding feature information and the third coding feature information is implemented based on an element-by-element addition operation, so as to orderly fuse the coding features extracted in different ways, thereby improving the effectiveness of feature extraction, and the embodiments of the present invention are not limited thereto.

[0071] It should be noted that the above-mentioned connection activation processing of the fourth encoded feature information is to first use a fully connected layer to perform a fully connected weighted summation processing, and then use the Relu activation function for processing, which is not limited in the embodiment of the present invention.

[0072] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0073] In yet another optional embodiment, feature extraction and analysis processing is performed on the target coding feature information to obtain target analysis result information, including:

[0074] Using the target feature extraction model to perform feature extraction processing on the target coding feature information to obtain target extraction feature information;

[0075] The target feature information is extracted and analyzed to obtain the target analysis result information.

[0076] It should be noted that the above-mentioned feature extraction processing of the target coding feature information using the target feature extraction model can perform hierarchical deep extraction of text information so as to perform more accurate classification and analysis of the comment information, which is not limited in the embodiment of the present invention.

[0077] In this optional embodiment, as an implementable manner, the target extraction feature information is analyzed and processed to obtain target analysis result information, including:

[0078] Analyzing and processing the target extracted feature information using the first feature analysis model to obtain first analysis result information;

[0079] Among them, the first feature analysis model is:

[0080]

[0081] Where DY represents the first analysis result information; TZ represents the target extraction feature information; ‖·‖ represents the modulus length;

[0082] Analyzing and processing the first analysis result information using the second feature analysis model to obtain second analysis result information;

[0083] Among them, the second characteristic analysis model is:

[0084]

[0085] Where DE i represents the i-th second analysis result value in the second analysis result information; x i Represents the i-th coefficient; tz i Representing the i-th first analysis result value in the first analysis result information;

[0086] The second analysis result information is activated to obtain target analysis result information.

[0087] It should be noted that the activation processing performed on the second analysis result information may be implemented based on a Sigmoid activation function, which is not limited in the embodiment of the present invention.

[0088] It should be noted that the above coefficient is a positive number greater than 0 and not greater than 1, and the embodiment of the present invention does not limit this.

[0089] It should be noted that the above-mentioned conversion of data using the first feature analysis model is conducive to improving the accuracy of sentiment classification of recipe reviews, which is not limited in the embodiment of the present invention.

[0090] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0091] In yet another optional embodiment, Figure 5 As shown, the target feature extraction model includes a feature extraction network, a first attention module, a first normalization module, a first pooling module, a first fusion module, a second fusion module and a feature extraction module; wherein,

[0092] The input end of the feature extraction network and the input end of the first fusion module are configured to receive the first model input information of the target feature extraction model; the output end of the feature extraction network is connected to the input end of the first attention module; the output end of the first attention module is respectively connected to the input end of the first normalization module and the input end of the first fusion module; the output end of the first normalization module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the second fusion module; the output end of the first fusion module is connected to the input end of the feature extraction module; the output end of the feature extraction module is connected to the input end of the second fusion module; the output end of the second fusion module is configured to output the first model output information of the target feature extraction model.

[0093] It should be noted that the feature extraction network can be constructed based on the BiLSTM model to obtain long-distance dependencies in the recipe review information and extract more in-depth semantic and grammatical global features of the recipe review information, which is not limited in the embodiment of the present invention.

[0094] It should be noted that the feature extraction module extracts local feature information of the recipe review information to obtain more accurate text information, which is not limited in the embodiment of the present invention.

[0095] It should be noted that the above-mentioned first normalization module is constructed based on layer normalization, which is not limited in the embodiment of the present invention.

[0096] It should be noted that the above-mentioned first pooling module is constructed based on the maximum pooling layer, which is not limited in the embodiment of the present invention.

[0097] It should be noted that the first attention module is constructed based on the self-attention mechanism to suppress irrelevant information and improve the effectiveness of word feature extraction according to the relationship between words in the menu review information, which is not limited in the embodiment of the present invention.

[0098] It should be noted that both the first fusion module and the second fusion module are constructed based on element-by-element addition, which is not limited in the embodiment of the present invention.

[0099] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0100] In an optional embodiment, the feature extraction module includes a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a first attention unit, a second attention unit, a third attention unit, a fourth attention unit, a first normalization unit, a second normalization unit, a third normalization unit, a fourth normalization unit, a first pooling unit, and a second pooling unit; wherein,

[0101] The input end of the first convolution unit and the input end of the second convolution unit are both connected to the output end of the first fusion module; the output end of the first convolution unit is connected to the input end of the first attention unit; the output end of the first attention unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the third convolution unit; the output end of the third convolution unit is connected to the input end of the third attention unit; the output end of the third attention unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the first pooling unit; the output end of the second convolution unit is connected to the input end of the second attention unit; the output end of the second attention unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the fourth attention unit; the output end of the fourth attention unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the second pooling unit; the output end of the first pooling unit and the output end of the second pooling unit are both connected to the input end of the second fusion module.

[0102] It should be noted that the above-mentioned first attention unit, second attention unit, third attention unit, and fourth attention unit are all constructed based on the self-attention mechanism, and the embodiments of the present invention do not limit this.

[0103] It should be noted that the above-mentioned first normalization unit, second normalization unit, third normalization unit, and fourth normalization unit are all constructed based on layer normalization, which is not limited in the embodiment of the present invention.

[0104] It should be noted that the first pooling unit and the second pooling unit are both constructed based on the maximum pooling layer to reduce the dimension of the data, which is not limited in the embodiment of the present invention.

[0105] It should be noted that the convolution kernels of the first convolution unit, the second convolution unit, the third convolution unit, and the fourth convolution unit are all 3×3, with a step size of 2, which is not limited in the embodiment of the present invention.

[0106] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0107] In another optional embodiment, the target feature extraction model is trained based on the following method:

[0108] Obtain a text training sample set; the text training sample set includes M text training samples;

[0109] The initial feature extraction model is trained using N text training samples in the text training sample set and a cross entropy loss function to obtain a target feature extraction model; N is not greater than M / 2.

[0110] It should be noted that the above text training samples can be annotated by users or can be sample data downloaded from Github, which is not limited in the embodiment of the present invention.

[0111] It should be noted that the above N is not greater than M / 2, and the target samples representing each training batch are N samples randomly selected from the text training samples as a training batch and input into an initial feature extraction model consistent with the model architecture of the target feature extraction model for training, thereby obtaining the final target feature extraction model, which is not limited in the embodiments of the present invention.

[0112] It should be noted that the above training times threshold for terminating model training is not less than 500, and this embodiment of the present invention does not limit this.

[0113] It can be seen that the processing method for efficient menu update described in the embodiment of the present invention is beneficial to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0114] Embodiment 2

[0115] See also Figure 3 , Figure 3 : is a schematic diagram of a processing system for efficiently updating menus disclosed in an embodiment of the present invention. Figure 3 The described system can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the system may include:

[0116] An acquisition module 201 is used to acquire recipe review information;

[0117] The first processing module 202 is used to encode the recipe review information to obtain target encoding feature information;

[0118] The second processing module 203 is used to perform feature extraction and analysis processing on the target coding feature information to obtain target analysis result information.

[0119] It can be seen that the implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0120] In another optional embodiment, Figure 3As shown, the recipe review information is encoded to obtain target encoding feature information, including:

[0121] Perform word segmentation processing on the recipe review information to obtain first review word segmentation information;

[0122] Remove stop words from the first comment segmentation information to obtain the second comment segmentation information;

[0123] The second comment word segmentation information is encoded to obtain target encoding feature information.

[0124] It can be seen that the implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0125] In yet another optional embodiment, Figure 3 As shown, the second comment segmentation information is encoded to obtain target encoding feature information, including:

[0126] Using the first encoding model to encode the second comment segmentation information to obtain first encoding feature information;

[0127] Using the second encoding model to encode the second comment segmentation information to obtain second encoding feature information;

[0128] Performing feature embedding processing on the second coded feature information to obtain third coded feature information;

[0129] The first coding feature information and the third coding feature information are fused to obtain fourth coding feature information;

[0130] The fourth coding feature information is subjected to connection activation processing to obtain target coding feature information.

[0131] It can be seen that the implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0132] In yet another optional embodiment, Figure 3 As shown, feature extraction and analysis are performed on the target coding feature information to obtain target analysis result information, including:

[0133] Using the target feature extraction model to perform feature extraction processing on the target coding feature information to obtain target extraction feature information;

[0134] The target feature information is extracted and analyzed to obtain the target analysis result information.

[0135] It can be seen that the implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0136] In yet another optional embodiment, Figure 3 As shown, the target feature extraction model includes a feature extraction network, a first attention module, a first normalization module, a first pooling module, a first fusion module, a second fusion module and a feature extraction module; wherein,

[0137] The input end of the feature extraction network and the input end of the first fusion module are configured to receive the first model input information of the target feature extraction model; the output end of the feature extraction network is connected to the input end of the first attention module; the output end of the first attention module is respectively connected to the input end of the first normalization module and the input end of the first fusion module; the output end of the first normalization module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the second fusion module; the output end of the first fusion module is connected to the input end of the feature extraction module; the output end of the feature extraction module is connected to the input end of the second fusion module; the output end of the second fusion module is configured to output the first model output information of the target feature extraction model.

[0138] It can be seen that the implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0139] In yet another optional embodiment, Figure 3 As shown, the feature extraction module includes a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a first attention unit, a second attention unit, a third attention unit, a fourth attention unit, a first normalization unit, a second normalization unit, a third normalization unit, a fourth normalization unit, a first pooling unit, and a second pooling unit; wherein,

[0140] The input end of the first convolution unit and the input end of the second convolution unit are both connected to the output end of the first fusion module; the output end of the first convolution unit is connected to the input end of the first attention unit; the output end of the first attention unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the third convolution unit; the output end of the third convolution unit is connected to the input end of the third attention unit; the output end of the third attention unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the first pooling unit; the output end of the second convolution unit is connected to the input end of the second attention unit; the output end of the second attention unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the fourth attention unit; the output end of the fourth attention unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the second pooling unit; the output end of the first pooling unit and the output end of the second pooling unit are both connected to the input end of the second fusion module.

[0141] It can be seen that implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0142] In yet another optional embodiment, Figure 3 As shown in the figure, the target feature extraction model is trained based on the following method:

[0143] Obtain a text training sample set; the text training sample set includes M text training samples;

[0144] The initial feature extraction model is trained using N text training samples in the text training sample set and a cross entropy loss function to obtain a target feature extraction model; N is not greater than M / 2.

[0145] It can be seen that implementation Figure 3 The described processing system for efficient updating of menus is conducive to accurately and efficiently extracting comment features through fine-grained analysis of user comment information, thereby achieving accurate analysis of user preferences for menus.

[0146] Embodiment 3

[0147] See also Figure 4 , Figure 4 is a structural diagram of another processing system for efficiently updating menus disclosed in an embodiment of the present invention. Figure 4 The described system can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the system may include:

[0148] A memory 301 storing executable program codes;

[0149] a processor 302 coupled to the memory 301;

[0150] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the processing method for efficiently updating the menu described in the first embodiment.

[0151] Embodiment 4

[0152] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the processing method for efficiently updating a menu as described in the first embodiment.

[0153] Embodiment 5

[0154] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the processing method for efficiently updating a menu described in the first embodiment.

[0155] The system embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative labor.

[0156] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0157] Finally, it should be noted that the processing method and system for efficient menu updating disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for efficiently updating menus, characterized in that: The method comprises: Get recipe review information; Encoding the recipe review information to obtain target encoding feature information; The target coding feature information is subjected to feature extraction and analysis processing to obtain target analysis result information.

2. The method for efficiently updating menus according to claim 1, characterized in that: The encoding process of the recipe review information to obtain target encoding feature information includes: Performing word segmentation processing on the recipe review information to obtain first review word segmentation information; Perform stop word processing on the first comment segmentation information to obtain second comment segmentation information; The second comment segmentation information is encoded to obtain target encoding feature information.

3. The method for efficiently updating menus according to claim 2, characterized in that: The encoding process of the second comment segmentation information to obtain target encoding feature information includes: Using a first encoding model to encode the second comment segmentation information to obtain first encoding feature information; Using a second encoding model to encode the second comment segmentation information to obtain second encoding feature information; Performing feature embedding processing on the second coded feature information to obtain third coded feature information; The first coding feature information and the third coding feature information are fused to obtain fourth coding feature information; The fourth coding feature information is subjected to connection activation processing to obtain target coding feature information.

4. The method for efficiently updating menus according to claim 1, characterized in that: The step of performing feature extraction and analysis on the target coding feature information to obtain target analysis result information includes: Using a target feature extraction model to perform feature extraction processing on the target encoding feature information to obtain target extraction feature information; The target extracted feature information is analyzed and processed to obtain target analysis result information.

5. The method for efficiently updating menus according to claim 4, characterized in that: The target feature extraction model includes a feature extraction network, a first attention module, a first normalization module, a first pooling module, a first fusion module, a second fusion module and a feature extraction module; wherein, The input end of the feature extraction network and the input end of the first fusion module are configured to receive the first model input information of the target feature extraction model; the output end of the feature extraction network is connected to the input end of the first attention module; the output end of the first attention module is respectively connected to the input end of the first normalization module and the input end of the first fusion module; the output end of the first normalization module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the second fusion module; the output end of the first fusion module is connected to the input end of the feature extraction module; the output end of the feature extraction module is connected to the input end of the second fusion module; the output end of the second fusion module is configured to output the first model output information of the target feature extraction model.

6. The method for efficiently updating menus according to claim 5, characterized in that: The feature extraction module includes a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a first attention unit, a second attention unit, a third attention unit, a fourth attention unit, a first normalization unit, a second normalization unit, a third normalization unit, a fourth normalization unit, a first pooling unit and a second pooling unit; wherein, The input end of the first convolution unit and the input end of the second convolution unit are both connected to the output end of the first fusion module; the output end of the first convolution unit is connected to the input end of the first attention unit; the output end of the first attention unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the third convolution unit; the output end of the third convolution unit is connected to the input end of the third attention unit; the output end of the third attention unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the first pooling unit; the output end of the second convolution unit is connected to the input end of the second attention unit; the output end of the second attention unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the fourth attention unit; the output end of the fourth attention unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the second pooling unit; the output end of the first pooling unit and the output end of the second pooling unit are both connected to the input end of the second fusion module.

7. The method for efficiently updating menus according to claim 4, characterized in that: The target feature extraction model is trained based on the following method: Acquire a text training sample set; the text training sample set includes M text training samples; Using the N text training samples in the text training sample set and a cross entropy loss function to train an initial feature extraction model to obtain the target feature extraction model; The N is not greater than M / 2.

8. A processing device for efficiently updating menus, characterized in that: The device comprises: The acquisition module is used to obtain recipe review information; A first processing module is used to encode the recipe review information to obtain target encoding feature information; The second processing module is used to perform feature extraction and analysis on the target coding feature information to obtain target analysis result information.

9. A processing device for efficiently updating menus, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the processing method for efficiently updating a menu as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the processing method for efficiently updating menus according to any one of claims 1 to 7.

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