Processing method and system for food performance evaluation data analysis and display

Abstract

The invention discloses a processing method and system for analyzing and displaying food performance evaluation data. The method comprises the following steps: obtaining to-be-processed food evaluation information; preprocessing the to-be-processed food evaluation information to obtain target evaluation feature information; the target evaluation feature information comprises first evaluation feature information and second evaluation feature information; and performing analysis conversion processing on the target evaluation feature information to obtain target food analysis display information.

Description

Technical Field

[0001] The present invention relates to the field of evaluation processing technology, and in particular to a processing method and system for analyzing and displaying food performance evaluation data. Background Art

[0002] Traditional food efficacy evaluation methods often rely on laboratory tests and empirical judgments. These methods are not only time-consuming and labor-intensive, but also difficult to achieve large-scale real-time monitoring and evaluation. With the rapid development of information technology, especially the application of big data, cloud computing and Internet of Things technology, the field of food efficacy evaluation has ushered in new development opportunities. However, food efficacy evaluation data analysis faces challenges such as large data volume, diverse data types, and complex data sources. In order to effectively process and analyze these data, it is necessary to develop efficient and accurate data processing methods and systems. Therefore, a processing method and system for food efficacy evaluation data analysis and display is provided to achieve rapid processing and analysis of data such as food reviews, provide accurate food efficacy evaluation results, and thus provide a basis for efficient food security. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a processing method and system for food efficacy evaluation data analysis and display, which is conducive to the rapid processing and analysis of food review data, and provides accurate food efficacy evaluation results, thereby providing a basis for efficient food security.

[0004] In order to solve the above technical problem, a first aspect of an embodiment of the present invention discloses a data processing method, the method comprising:

[0005] Obtain evaluation information of food to be processed;

[0006] Pre-processing the food evaluation information to be processed to obtain target evaluation feature information; the target evaluation feature information includes first evaluation feature information and second evaluation feature information;

[0007] The target evaluation feature information is analyzed and converted to obtain target food analysis display information.

[0008] A second aspect of an embodiment of the present invention discloses a data processing system, the system comprising:

[0009] An acquisition module is used to obtain evaluation information of food to be processed;

[0010] A first processing module is used to pre-process the evaluation information of the food to be processed to obtain target evaluation feature information; the target evaluation feature information includes first evaluation feature information and second evaluation feature information;

[0011] The second processing module is used to analyze and convert the target evaluation feature information to obtain target food analysis display information.

[0012] A third aspect of the present invention discloses another data processing system, 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 data processing method disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the data processing method 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 drawings required for use in the description of the embodiments will be briefly introduced below. 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 food efficacy evaluation data analysis and display provided by an embodiment of the present invention;

[0019] Figure 2 It is a flowchart of a processing method for food efficacy evaluation data analysis and display disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural schematic diagram of a processing system for food efficacy evaluation data analysis and display disclosed in an embodiment of the present invention;

[0021] Figure 4 It is a structural schematic diagram of another processing system for food performance evaluation data analysis and display disclosed in an embodiment of the present invention;

[0022] Figure 5 It is a structural schematic diagram of a target feature analysis 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 can be a 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 model, which is 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 food performance evaluation data analysis and display, which are described in detail below.

[0034] See also Figure 1 , Figure 1 Schematic diagram of a processing system for analyzing and displaying food performance evaluation data provided by an embodiment of the present application. The processing system for analyzing and displaying food performance evaluation data may include a computer device 100, in which the processing system for analyzing and displaying food performance evaluation data is integrated, such as Figure 1 Computer equipment in.

[0035] In the embodiment of the present application, the computer device 100 is mainly used to obtain evaluation information of the food to be processed;

[0036] Pre-processing the food evaluation information to be processed to obtain target evaluation feature information; the target evaluation feature information includes first evaluation feature information and second evaluation feature information;

[0037] The target evaluation feature information is analyzed and converted to obtain target food analysis display information.

[0038] It can realize the rapid processing and analysis of data such as food reviews, provide accurate food performance evaluation results, and thus provide a basis for efficient food assurance.

[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 food performance evaluation data analysis and display may 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 food performance evaluation data analysis and display may also include a memory 200 for storing data, such as image data, location information, etc.

[0043] It should be noted that Figure 1 The scenario diagram of the processing system for food efficiency evaluation data analysis and display shown is merely an example. The processing system and scenario for food efficiency evaluation data analysis and display 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 food efficiency evaluation data analysis and display 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 food performance evaluation data analysis and display, which is conducive to realizing rapid processing and analysis of food review data, providing accurate food performance evaluation results, and thus providing a basis for efficient food security. The following are detailed descriptions.

[0045] Embodiment 1

[0046] See also Figure 2 , Figure 2 1 is a flow chart of a method for analyzing and displaying food performance evaluation data disclosed in an embodiment of the present invention. Figure 2 The processing method for analyzing and displaying food performance evaluation data 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 food performance evaluation data analysis and display may include the following operations:

[0047] 101. Obtain evaluation information of food to be processed.

[0048] 102. Pre-process the food evaluation information to be processed to obtain target evaluation feature information.

[0049] In the embodiment of the present invention, the target evaluation feature information includes first evaluation feature information and second evaluation feature information.

[0050] 103. Analyze, transform and process the target evaluation feature information to obtain target food analysis display information.

[0051] It should be noted that the above-mentioned food evaluation information to be processed represents the user's evaluation of different menu foods, which may be comments made on the user interaction platform and then the data is collected, which is not limited in the embodiment of the present invention.

[0052] It should be noted that the above target food analysis display information represents the visual information of the user's evaluation of the menu food, so as to clearly and efficiently understand the user's preference for different menu foods, thereby providing a basis for optimizing food security, and the embodiment of the present invention does not limit this.

[0053] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0054] In an optional embodiment, the food evaluation information to be processed is pre-processed in the above step 102 to obtain target evaluation feature information, including:

[0055] Performing data segmentation and cleaning processing on the food evaluation information to be processed to obtain first processed evaluation information and second processed evaluation information;

[0056] Encoding the first processing evaluation information to obtain first evaluation feature information;

[0057] The second processing evaluation information is encoded to obtain second evaluation feature information.

[0058] It should be noted that the above-mentioned encoding processing of the first processing evaluation information is to convert the text into a vector matrix, which can be implemented based on the LLM large model or based on a pre-trained artificial intelligence model such as Ernie, and the embodiment of the present invention is not limited thereto.

[0059] In this optional embodiment, as an optional implementation manner, the above-mentioned data segmentation and cleaning processing is performed on the to-be-processed food evaluation information to obtain the first processed evaluation information and the second processed evaluation information, including:

[0060] Perform word segmentation processing on the food evaluation information to be processed to obtain first food word segmentation information;

[0061] The word segmentation food evaluation information is processed by removing stop words to obtain the second food word segmentation information;

[0062] Eliminate the emoticons in the second food segmentation information to obtain first processed evaluation information;

[0063] The first emoticon in the second food segmentation information is retained, and other emoticons are removed to obtain second processed evaluation information.

[0064] It should be noted that the above-mentioned word segmentation processing of the food evaluation information to be processed can be implemented based on a word segmentation tool or based on an LLM large model, which is not limited in the embodiment of the present invention.

[0065] It should be noted that the above-mentioned processing of removing stop words from the segmented food evaluation information may be performed by using a stop word list set by a user, such as using a stop word list calibrated by Harbin Institute of Technology, etc., which is not limited in the embodiment of the present invention.

[0066] It should be noted that the above-mentioned removal of emoticons in the second food segmentation information is to retain all the text information of the menu food evaluation information, and remove the emoticons in order to realize the feature analysis of the pure text and realize the text-based sentiment feature analysis. Furthermore, the first emoticon in the second food segmentation information is retained at the same time, and the other emoticons are removed because when the user is commenting, the first emoticon is usually sufficient to express his emotion, while other emoticons may be repeated and the value of analyzing them is limited. Therefore, the first emoticon is retained in order to perform sentiment feature analysis through emoticons according to context information, thereby realizing the retention of multiple feature information, which is more conducive to the subsequent target feature analysis model to deeply analyze multiple features, thereby improving the accuracy and reliability of the sentiment classification of the menu food evaluation, which is not limited in the embodiment of the present invention.

[0067] It should be noted that the first processing evaluation information and the second processing evaluation information respectively represent the first model input and the second model input of the target feature analysis model, which is not limited in the embodiment of the present invention.

[0068] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0069] In another optional embodiment, the second processing evaluation information is encoded to obtain second evaluation feature information, including:

[0070] Encoding the evaluation text information and the evaluation symbol information in the second processed evaluation information respectively to obtain first encoding feature information and second encoding feature information; the second encoding feature information includes first sub-encoding feature information and second sub-encoding feature information;

[0071] The first coding feature information and the second coding feature information are converted to obtain second evaluation feature information.

[0072] It should be noted that the above-mentioned encoding processing of the evaluation text information and the evaluation symbol information in the second processed evaluation information respectively to obtain the first encoded feature information and the second encoded feature information is to use the emoji2Vec model to vectorize the evaluation text information and the evaluation symbol information respectively to obtain a static feature representation, which is not limited to the embodiments of the present invention.

[0073] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0074] In yet another optional embodiment, the first coding feature information and the second coding feature information are converted to obtain the second evaluation feature information, including:

[0075] Acquire coding weight information; the coding weight information includes first coding weight information and second coding weight information;

[0076] Using the first coding weight information and the second coding weight information in the coding weight information respectively to perform product calculation processing on the first sub-coding feature information and the second sub-coding feature information in the second coding feature information, first calculation feature information and second calculation feature information are obtained;

[0077] Concatenate the first calculated feature information and the second calculated feature information to obtain third encoded feature information;

[0078] The first coding feature information and the third coding feature information are concatenated to obtain second evaluation feature information.

[0079] It should be noted that the above-mentioned first encoding weight information and the second encoding weight information respectively represent the weights of the emoticons representing positive emotions and negative emotions in the context, which can be obtained by statistically analyzing different historical emoticons in historical menu food reviews, and the embodiment of the present invention is not limited thereto.

[0080] It should be noted that the weighted calculation and concatenation of the second coded feature information using the coded weight information is a dynamic feature embedding of comments with symbolic expressions to improve the information richness of the comment features, which is not limited in the embodiments of the present invention.

[0081] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0082] In another optional embodiment, the target evaluation feature information is analyzed and converted to obtain target food analysis display information, including:

[0083] Analyze and process the target evaluation feature information using the target feature analysis model to obtain the target food analysis result information;

[0084] The target food analysis result information is converted and processed to obtain the target food analysis display information.

[0085] It should be noted that the above-mentioned conversion and processing of the target food analysis result information to obtain the target food analysis display information is to classify and count the target food analysis result information according to the identified emotion category, and then convert it into a chart (such as a bar chart or a line chart, etc.) to obtain visual comment chart information, that is, to obtain the target food analysis display information, which is not limited to the embodiments of the present invention.

[0086] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0087] In an optional embodiment, if Figure 5 As shown, the target feature analysis model includes a first convolution module, a second convolution module, a third convolution module, a first pooling module, a second pooling module, a third pooling module, a first fusion module, a second fusion module, a first feature extraction network, a second feature extraction network, an attention module and a first connection module; wherein,

[0088] The input end of the first convolution module, the input end of the second convolution module, the input end of the third convolution module and the input end of the first feature extraction network are all configured to receive the first model input information of the target feature analysis model; the output end of the first convolution module is connected to the input end of the first pooling module; the output end of the first pooling module, the output end of the second pooling module and the output end of the third pooling module are all connected to the input end of the second fusion module; the output end of the first feature extraction network, the output end of the second fusion module and the output end of the second feature extraction network are all connected to the input end of the first fusion module; the output end of the second fusion module is also connected to the input end of the second feature extraction network; the input end of the first fusion module is also configured to receive the second model input information of the target feature analysis model; the output end of the first fusion module is connected to the input end of the attention module; the output end of the attention module is connected to the input end of the first connection module; the output end of the first connection module is configured to output the model output information of the target feature analysis model.

[0089] It should be noted that the above-mentioned first feature extraction network and second feature extraction network are used to extract text features, and they can be constructed based on BiLSTM or BERT, which is not limited in the embodiment of the present invention.

[0090] It should be noted that the above-mentioned attention module is constructed based on the self-attention mechanism, which is not limited in the embodiments of the present invention. Furthermore, the data output by the output end of the above-mentioned first feature extraction network, the output end of the second fusion module, and the output end of the second feature extraction network are spliced ​​and processed by the first fusion module, and the self-attention module performs interactive fusion, screening, and weighted processing between features, thereby capturing the internal correlation of features on a global scale and improving the extraction depth of feature information, which is not limited in the embodiments of the present invention.

[0091] It should be noted that the above-mentioned first fusion module and second fusion module are both constructed based on the splicing operation, which is not limited in the embodiment of the present invention.

[0092] It should be noted that the first connection module is composed of a fully connected layer and an activation function layer. Furthermore, the activation function can be a Relu activation function or a sigmoid activation function, which is not limited in the embodiment of the present invention. Furthermore, the data analysis result is mapped to a value between 0 and 1 through the first connection module, and then the emotional category of the recipe review is distinguished for subsequent further visualization analysis, which is not limited in the embodiment of the present invention.

[0093] It should be noted that the above-mentioned first pooling module, second pooling module, and third pooling module are all constructed based on the maximum pooling layer to perform dimensionality reduction processing on the feature data after convolution, reduce the amount of data processed by the model, and improve the processing efficiency of the model, which is not limited in the embodiments of the present invention.

[0094] It should be noted that the convolution kernel sizes of the first convolution module, the second convolution module, and the third convolution module are all 3×3, and the step size is 2, which is not limited in the embodiment of the present invention.

[0095] It should be noted that the above-mentioned target feature analysis model can perform feature analysis on the text and emoticons in the menu reviews at the same time, so as to combine the context and use emoticons to strengthen the sentiment classification of the menu reviews, thereby improving the accuracy and reliability of the food performance evaluation of the food products, which is not limited in the embodiments of the present invention.

[0096] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0097] In another optional embodiment, the target feature analysis model is trained based on the following method:

[0098] Get the first food review information set;

[0099] Performing annotation transformation processing on the first food review information set to obtain a first training sample set; the first training sample set includes a plurality of first training samples;

[0100] Based on the first training sample set, determining a target training sample set;

[0101] Using the target training sample set to train the basic feature analysis model, to obtain a training feature analysis model;

[0102] Determine whether the model parameter information corresponding to the training feature analysis model meets the model training conditions, and obtain the training judgment result;

[0103] When the training judgment result is no, the basic feature analysis model is updated using the training feature analysis model, and the execution is triggered to determine the target training sample set based on the first training sample set;

[0104] When the training judgment result is yes, the training feature analysis model is determined to be the target feature analysis model.

[0105] It should be noted that the above-mentioned model training conditions include that the number of training times corresponding to the model parameter information reaches the training times threshold (a positive integer not less than 500), and / or that the loss function value converges (obtained by calculating the model parameters using the cross entropy loss function), which is not limited in the embodiments of the present invention.

[0106] It should be noted that the first food review information set is a review of the menu food on the user interaction platform after the menu food is consumed, and may include text or symbol expressions, which is not limited in the embodiment of the present invention.

[0107] It should be noted that the above-mentioned annotation conversion processing of the first food review information set can be implemented based on a text annotation tool, or can be annotated using a large model, which is not limited in the embodiment of the present invention.

[0108] It should be noted that, based on the first training sample set, determining the target training sample set is to randomly select N first training samples from the first training sample set as the target training samples in the target training sample set, which is not limited in the embodiment of the present invention. Further, the above N is a positive integer not less than 10 and not greater than 20, which is not limited in the embodiment of the present invention.

[0109] It should be noted that the model architectures of the above-mentioned basic feature analysis model and the target feature analysis model are consistent, and the embodiments of the present invention do not limit this.

[0110] It can be seen that the processing method for food efficacy evaluation data analysis and display described in the embodiment of the present invention is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0111] Embodiment 2

[0112] See also Figure 3 , Figure 3 1 is a schematic diagram of a processing system for food performance evaluation data analysis and display 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:

[0113] An acquisition module 201 is used to acquire evaluation information of food to be processed;

[0114] The first processing module 202 is used to pre-process the food evaluation information to be processed to obtain target evaluation feature information; the target evaluation feature information includes first evaluation feature information and second evaluation feature information;

[0115] The second processing module 203 is used to analyze and convert the target evaluation feature information to obtain target food analysis display information.

[0116] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0117] In yet another optional embodiment, Figure 3 As shown, the food evaluation information to be processed is pre-processed to obtain target evaluation feature information, including:

[0118] Performing data segmentation and cleaning processing on the food evaluation information to be processed to obtain first processed evaluation information and second processed evaluation information;

[0119] Encoding the first processing evaluation information to obtain first evaluation feature information;

[0120] The second processing evaluation information is encoded to obtain second evaluation feature information.

[0121] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0122] In yet another optional embodiment, Figure 3 As shown, the second processing evaluation information is encoded to obtain second evaluation feature information, including:

[0123] Encoding the evaluation text information and the evaluation symbol information in the second processed evaluation information respectively to obtain first encoding feature information and second encoding feature information; the second encoding feature information includes first sub-encoding feature information and second sub-encoding feature information;

[0124] The first coding feature information and the second coding feature information are converted to obtain second evaluation feature information.

[0125] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0126] In yet another optional embodiment, Figure 3 As shown, the first coding feature information and the second coding feature information are converted to obtain the second evaluation feature information, including:

[0127] Acquire coding weight information; the coding weight information includes first coding weight information and second coding weight information;

[0128] Using the first coding weight information and the second coding weight information in the coding weight information respectively to perform product calculation processing on the first sub-coding feature information and the second sub-coding feature information in the second coding feature information, first calculation feature information and second calculation feature information are obtained;

[0129] Concatenate the first calculated feature information and the second calculated feature information to obtain third encoded feature information;

[0130] The first coding feature information and the third coding feature information are concatenated to obtain second evaluation feature information.

[0131] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0132] In yet another optional embodiment, Figure 3 As shown, the target evaluation feature information is analyzed and converted to obtain the target food analysis display information, including:

[0133] Analyze and process the target evaluation feature information using the target feature analysis model to obtain the target food analysis result information;

[0134] The target food analysis result information is converted and processed to obtain the target food analysis display information.

[0135] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

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

[0137] The input end of the first convolution module, the input end of the second convolution module, the input end of the third convolution module and the input end of the first feature extraction network are all configured to receive the first model input information of the target feature analysis model; the output end of the first convolution module is connected to the input end of the first pooling module; the output end of the first pooling module, the output end of the second pooling module and the output end of the third pooling module are all connected to the input end of the second fusion module; the output end of the first feature extraction network, the output end of the second fusion module and the output end of the second feature extraction network are all connected to the input end of the first fusion module; the output end of the second fusion module is also connected to the input end of the second feature extraction network; the input end of the first fusion module is also configured to receive the second model input information of the target feature analysis model; the output end of the first fusion module is connected to the input end of the attention module; the output end of the attention module is connected to the input end of the first connection module; the output end of the first connection module is configured to output the model output information of the target feature analysis model.

[0138] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0139] In another optional embodiment, Figure 3 As shown in the figure, the target feature analysis model is trained based on the following method:

[0140] Get the first food review information set;

[0141] Performing annotation transformation processing on the first food review information set to obtain a first training sample set; the first training sample set includes a plurality of first training samples;

[0142] Based on the first training sample set, determining a target training sample set;

[0143] Using the target training sample set to train the basic feature analysis model, to obtain a training feature analysis model;

[0144] Determine whether the model parameter information corresponding to the training feature analysis model meets the model training conditions, and obtain the training judgment result;

[0145] When the training judgment result is no, the basic feature analysis model is updated using the training feature analysis model, and the execution is triggered to determine the target training sample set based on the first training sample set;

[0146] When the training judgment result is yes, the training feature analysis model is determined to be the target feature analysis model.

[0147] It can be seen that implementation Figure 3 The described processing system for food efficacy evaluation data analysis and display is conducive to the rapid processing and analysis of food review data, providing accurate food efficacy evaluation results, and thus providing a basis for efficient food security.

[0148] Embodiment 3

[0149] See also Figure 4 , Figure 4 is a structural diagram of another processing system for food performance evaluation data analysis and display 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:

[0150] A memory 301 storing executable program codes;

[0151] a processor 302 coupled to the memory 301;

[0152] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the processing method for food efficacy evaluation data analysis and display described in the first embodiment.

[0153] Embodiment 4

[0154] 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 food performance evaluation data analysis and display described in the first embodiment.

[0155] Embodiment 5

[0156] 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 in the processing method for analyzing and displaying food performance evaluation data described in Example 1.

[0157] 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 based on 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 effort.

[0158] 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.

[0159] Finally, it should be noted that the processing method and system for food efficacy evaluation data analysis and display disclosed in the embodiment of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. 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 schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the various embodiments of the present invention.

Claims

1. A data processing method, characterized in that: The method comprises: Obtain evaluation information of food to be processed; Pre-processing the food evaluation information to be processed to obtain target evaluation feature information; the target evaluation feature information includes first evaluation feature information and second evaluation feature information; The target evaluation feature information is analyzed and converted to obtain target food analysis display information.

2. The data processing method according to claim 1, characterized in that: The pre-processing of the food evaluation information to be processed to obtain target evaluation feature information includes: Performing data segmentation and cleaning processing on the to-be-processed food evaluation information to obtain first processed evaluation information and second processed evaluation information; Encoding the first processing evaluation information to obtain first evaluation feature information; The second processing evaluation information is encoded to obtain second evaluation feature information.

3. The data processing method according to claim 2, characterized in that: The encoding process of the second processing evaluation information to obtain the second evaluation feature information includes: Encoding the evaluation text information and the evaluation symbol information in the second processed evaluation information respectively to obtain first encoding feature information and second encoding feature information; the second encoding feature information includes first sub-encoding feature information and second sub-encoding feature information; The first coding feature information and the second coding feature information are converted to obtain second evaluation feature information.

4. The data processing method according to claim 3, characterized in that: The converting the first coding feature information and the second coding feature information to obtain second evaluation feature information includes: Acquire coding weight information; the coding weight information includes first coding weight information and second coding weight information; Using the first coding weight information and the second coding weight information in the coding weight information respectively, the first sub-coding feature information and the second sub-coding feature information in the second coding feature information are multiplied to obtain first calculation feature information and second calculation feature information; Concatenating the first calculated feature information and the second calculated feature information to obtain third encoded feature information; The first coding feature information and the third coding feature information are concatenated to obtain second evaluation feature information.

5. The data processing method according to claim 1, characterized in that: The target evaluation feature information is analyzed and converted to obtain target food analysis display information, including: Analyzing and processing the target evaluation feature information using a target feature analysis model to obtain target food analysis result information; The target food analysis result information is converted to obtain target food analysis display information.

6. The data processing method according to claim 5, characterized in that: The target feature analysis model includes a first convolution module, a second convolution module, a third convolution module, a first pooling module, a second pooling module, a third pooling module, a first fusion module, a second fusion module, a first feature extraction network, a second feature extraction network, an attention module and a first connection module; wherein, The input end of the first convolution module, the input end of the second convolution module, the input end of the third convolution module and the input end of the first feature extraction network are all configured to receive the first model input information of the target feature analysis model; the output end of the first convolution module is connected to the input end of the first pooling module; the output end of the first pooling module, the output end of the second pooling module and the output end of the third pooling module are all connected to the input end of the second fusion module; the output end of the first feature extraction network, the output end of the second fusion module and the output end of the second feature extraction network are all connected to the input end of the first fusion module; the output end of the second fusion module is also connected to the input end of the second feature extraction network; the input end of the first fusion module is also configured to receive the second model input information of the target feature analysis model; the output end of the first fusion module is connected to the input end of the attention module; the output end of the attention module is connected to the input end of the first connection module; the output end of the first connection module is configured to output the model output information of the target feature analysis model.

7. The data processing method according to claim 5, characterized in that: The target feature analysis model is trained based on the following method: Get the first food review information set; Performing a labeling transformation process on the first food review information set to obtain a first training sample set; the first training sample set includes a plurality of first training samples; Based on the first training sample set, determining a target training sample set; Using the target training sample set to train a basic feature analysis model to obtain a training feature analysis model; Determine whether the model parameter information corresponding to the training feature analysis model meets the model training conditions, and obtain a training judgment result; When the training judgment result is no, the basic feature analysis model is updated by using the training feature analysis model, and the determination of the target training sample set based on the first training sample set is triggered; When the training judgment result is yes, the training feature analysis model is determined to be the target feature analysis model.

8. A data processing system, characterized in that: The system comprises: An acquisition module is used to obtain evaluation information of food to be processed; A first processing module is used to pre-process the evaluation information of the food to be processed to obtain target evaluation feature information; the target evaluation feature information includes first evaluation feature information and second evaluation feature information; The second processing module is used to analyze and convert the target evaluation feature information to obtain target food analysis display information.

9. A data processing system, characterized in that: The system 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 data processing method according to 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 data processing method according to any one of claims 1 to 7.

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