Customized weight-losing suggestion generation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a customized weight losing suggestion generation method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring obesity survey information and survey communication information corresponding to a target customer according to a preset obesity information survey item; determining a target universal suggestion from a plurality of preset candidate universal suggestions based on the obesity survey information; performing voice recognition on the basis of the investigation communication information to obtain communication content information corresponding to the target customer; determining a target cue word template from a plurality of preset candidate cue word templates based on the obesity survey information of the corresponding target customer; inputting the target cue word template and the communication content information into a pre-trained weight-losing service large model, and generating personalized suggestion information; and determining a target customized weight-losing suggestion according to the target general suggestion and the personalized suggestion information. According to the embodiment of the invention, customized weight-losing service information with stable quality can be provided for customers.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device, electronic device and storage medium for generating customized weight loss suggestions. Background Art

[0002] In health management services, especially offline customized weight loss services, employees collect and analyze customer information face to face, so as to provide customers with appropriate advice and services. However, the professional level of employees responsible for customized services varies, making it difficult to ensure the consistency of service quality. There are problems such as inefficient communication, incomplete information capture, and low service standardization. Therefore, how to provide customers with stable quality services and customized weight loss services has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a customized weight loss suggestion generation method, device, electronic device and storage medium, aiming to provide customers with customized weight loss service information with stable quality.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for generating customized weight loss suggestions, the method comprising:

[0005] According to the preset obesity information survey items, obtain obesity survey information and survey communication information corresponding to the target customers;

[0006] Determining a target general recommendation from a plurality of preset candidate general recommendations based on the obesity survey information;

[0007] Perform voice recognition based on the survey communication information to obtain communication content information corresponding to the target customer;

[0008] Based on the obesity survey information corresponding to the target customer, determining a target prompt word template from a plurality of preset candidate prompt word templates;

[0009] Inputting the target prompt word template and the communication content information into a pre-trained weight loss service model to generate personalized suggestion information through the weight loss service model;

[0010] A target customized weight loss suggestion is determined based on the target general suggestion and the personalized suggestion information.

[0011] In some embodiments, determining a target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information includes:

[0012] Based on the obesity survey information, an obesity characteristic analysis is performed on the obesity manifestations of the target customers to obtain obesity characteristic information;

[0013] Based on the obesity feature information, feature matching is performed with a plurality of preset typical feature information to obtain typical feature matching information; wherein each of the typical feature information corresponds to one of the candidate general suggestions;

[0014] Based on the typical feature matching information, the corresponding candidate general suggestion is determined as the target general suggestion.

[0015] In some embodiments, before determining the target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information, it also includes pre-configuring a plurality of the candidate general suggestions, specifically including:

[0016] Acquire a historical service data set, wherein the historical service data set includes a plurality of weight loss suggestion information, and each piece of weight loss suggestion information corresponds to a type of obesity characteristic information;

[0017] The common obesity feature information in the historical service data set is determined as the typical feature information, and the weight loss suggestion information corresponding to each of the typical feature information is integrated to obtain a plurality of candidate general suggestions.

[0018] In some embodiments, the obesity survey information includes customer status information and customer demand information, and determining a target prompt word template from a plurality of preset candidate prompt word templates based on the obesity survey information corresponding to the target customer includes:

[0019] Determining the obesity assessment standard of the target customer based on the customer status information; wherein the customer status information includes customer age information, customer gender information and customer diet information;

[0020] Determining the weight loss demand information of the target customer based on the customer demand information;

[0021] A target prompt word template is determined from a plurality of preset candidate prompt word templates based on the obesity assessment standard and the weight loss demand information.

[0022] In some embodiments, obtaining obesity survey information and survey communication information corresponding to target customers according to preset obesity information survey items includes:

[0023] According to the preset obesity information survey items, the target employee conducts survey communication with the target customer to obtain the obesity survey information and the survey communication information;

[0024] After inputting the target prompt word template and the communication content information into the pre-trained weight loss service model to generate personalized suggestion information through the weight loss service model, the method further includes:

[0025] Perform voice role recognition based on the communication content information to obtain first voice data corresponding to the target employee and second voice data corresponding to the target customer;

[0026] Performing semantic recognition based on the first voice data to obtain employee voice recognition information;

[0027] Performing service content analysis on the employee voice recognition information according to preset employee service evaluation standards to obtain corresponding employee evaluation information;

[0028] Performing voice sentiment analysis based on the second voice data to obtain satisfaction information of the target customer;

[0029] The service evaluation information of the target employee is obtained based on the employee evaluation information and the satisfaction information.

[0030] In some embodiments, after determining the target customized weight loss suggestion according to the target general suggestion and the personalized suggestion information, the method includes:

[0031] Generate a weight loss service knowledge graph based on the obesity survey information, the communication content information and the target customized weight loss suggestions of the target customer;

[0032] Continuously updating the weight loss service knowledge graph according to subsequent weight loss operations of the target customer to obtain a target updated knowledge graph;

[0033] Based on the weight loss service knowledge graph and the target update knowledge graph, a weight loss effect analysis is performed to generate target adjustment suggestions.

[0034] In some embodiments, before inputting the target prompt word template and the communication content information into a pre-trained weight loss service model to generate personalized recommendation information through the weight loss service model, it also includes pre-training with the weight loss service model, specifically including:

[0035] Obtaining weight loss knowledge data; wherein the weight loss knowledge data includes knowledge theory data adapted to different customer status information;

[0036] Acquire a model training set; wherein the model training set includes a plurality of weight loss service training samples corresponding to different customer status information, and each of the weight loss service training samples is configured with corresponding recommended sample information;

[0037] Generate recommended training information based on the weight loss service training samples and the weight loss knowledge data corresponding to the same customer status information input into the original weight loss service macro model;

[0038] Comparing the recommended training information corresponding to the weight loss service training sample with the recommended sample information to obtain comparison deviation data;

[0039] The model parameters of the weight loss service large model are updated based on the comparison deviation data to obtain the pre-trained weight loss service large model.

[0040] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a customized weight loss suggestion generating device, the device comprising:

[0041] An obesity information survey module, used to obtain obesity survey information and survey communication information corresponding to target customers according to preset obesity information survey items;

[0042] A general suggestion providing module, used for determining a target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information;

[0043] A speech recognition module, used to perform speech recognition information based on the survey communication information to obtain communication content information corresponding to the target customer;

[0044] A prompt word template determination module, used to determine a target prompt word template from a plurality of preset candidate prompt word templates based on the obesity survey information corresponding to the target customer;

[0045] A large model analysis module, used for inputting the target prompt word template and the communication content information into a pre-trained weight loss service large model, so as to generate personalized suggestion information through the weight loss service large model;

[0046] The weight loss suggestion providing module is used to determine the target customized weight loss suggestion according to the target general suggestion and the personalized suggestion information.

[0047] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the customized weight loss suggestion generation method described in the first aspect above.

[0048] To achieve the above objectives, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the customized weight loss suggestion generation method described in the first aspect above.

[0049] The customized weight loss suggestion generation method, device, electronic device and storage medium proposed in the present application obtain the target customer's obesity survey information and survey communication information according to the preset obesity information survey items, and determine the target general suggestion from a preset plurality of candidate general suggestions based on the obesity survey information, and then perform voice recognition information based on the survey communication information to obtain the communication content information of the corresponding target customer, and determine the target prompt word template from a preset plurality of candidate prompt word templates based on the obesity survey information of the corresponding target customer, and input the target prompt word template and the communication content information into the pre-trained weight loss service big model to generate personalized suggestion information through the weight loss service big model, and determine the target customized weight loss suggestion according to the target general suggestion and the personalized suggestion information. It can be seen from this that the present application obtains the target customers' obesity survey information and survey communication information, and obtains target general suggestions adapted to the target customers through the obesity survey information, thereby obtaining basic suggestion information for solving the target customers' obesity problems, and then uses the communication content information with the target customers and the adapted target prompt word template to input into the pre-trained weight loss service large model to generate personalized suggestion information for the target customers. By combining the target general suggestions and personalized suggestion information, the target customized weight loss suggestions are determined, which can solve the target customers' weight loss needs in a targeted manner and provide customized weight loss services with stable quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a method for generating customized weight loss suggestions provided in an embodiment of the present application;

[0051] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0052] Figure 3 yes Figure 1 The flowchart before step S102 in FIG.

[0053] Figure 4 yes Figure 1 Flow chart of step S104 in FIG.

[0054] Figure 5 yes Figure 1 The flowchart before step S105 in FIG.

[0055] Figure 6 yes Figure 1 The flowchart after step S106 in FIG.

[0056] Figure 7 yes Figure 1 The flowchart after step S105 in FIG.

[0057] Figure 8It is a structural schematic diagram of a customized weight loss suggestion generating device provided in an embodiment of the present application;

[0058] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0063] A large AI model refers to a machine learning model with ultra-large-scale parameters (usually more than one billion) and complex computing structures. It is usually able to process massive amounts of data and complete various complex tasks, such as natural language processing and image recognition.

[0064] Natural language processing (NLP): NLP uses computers to process, understand and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is often used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0065] The embodiments of the present application provide a method, device, electronic device and storage medium for generating customized weight loss suggestions, aiming to provide customers with customized weight loss service information with stable quality.

[0066] The customized weight loss suggestion generating method, device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the customized weight loss suggestion generating method in the embodiments of the present application is described.

[0067] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0068] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0069] The customized weight loss suggestion generation method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The customized weight loss suggestion generation method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a customized weight loss suggestion generation method, etc., but is not limited to the above forms.

[0070] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0071] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0072] Figure 1 is an optional flowchart of the method for generating customized weight loss suggestions provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0073] Step S101, obtaining obesity survey information and survey communication information corresponding to target customers according to preset obesity information survey items;

[0074] Step S102, determining a target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information;

[0075] Step S103, performing voice recognition information based on the survey communication information to obtain communication content information corresponding to the target customer;

[0076] Step S104, determining a target prompt word template from a plurality of preset candidate prompt word templates based on the obesity survey information of the corresponding target customers;

[0077] Step S105, inputting the target prompt word template and the communication content information into the pre-trained weight loss service model to generate personalized suggestion information through the weight loss service model;

[0078] Step S106, determining target customized weight loss suggestions based on the target general suggestions and the personalized suggestion information.

[0079] Steps S101 to S106 shown in the embodiment of the present application are to obtain the target customer's obesity survey information and survey communication information according to the preset obesity information survey items, and determine the target general suggestion from a preset plurality of candidate general suggestions based on the obesity survey information, and then perform voice recognition information based on the survey communication information to obtain the communication content information of the corresponding target customer, and determine the target prompt word template from a preset plurality of candidate prompt word templates based on the obesity survey information of the corresponding target customer, and input the target prompt word template and the communication content information into the pre-trained weight loss service big model to generate personalized suggestion information through the weight loss service big model, and determine the target customized weight loss suggestion according to the target general suggestion and the personalized suggestion information. It can be seen from this that the present application obtains the target customers' obesity survey information and survey communication information, and obtains target general suggestions adapted to the target customers through the obesity survey information, thereby obtaining basic suggestion information for solving the target customers' obesity problems, and then uses the communication content information with the target customers and the adapted target prompt word template to input into the pre-trained weight loss service large model to generate personalized suggestion information for the target customers. By combining the target general suggestions and personalized suggestion information, the target customized weight loss suggestions are determined, which can solve the target customers' weight loss needs in a targeted manner and provide customized weight loss services with stable quality.

[0080] In step S101 of some embodiments, obesity survey information and survey communication information corresponding to the target customer are obtained according to a preset obesity information survey project. The obesity information survey project is used to obtain information related to weight loss from the target customer, and is used to capture the target customer's basic information, living habits, eating habits, exercise habits, weight loss experience, and expectations for weight loss services.

[0081] Obtain survey communication information based on the obesity information survey project with target customers to supplement the obesity survey information. Through the obesity information survey project, the obesity survey information of target customers is collected in a standardized manner. At the same time, the survey communication information is obtained by communicating with target customers. More information can be collected, and non-quantitative information such as the target customers' expected motivation for weight loss and attitude towards customized weight loss services can be captured, as well as supplementary information for the obesity survey information.

[0082] Both obesity survey information and survey communication information are obtained based on the obesity information survey project. The difference is that obesity survey information is data directly obtained through the obesity information survey project, while survey communication information is voice communication data obtained during the communication process with target customers.

[0083] In some embodiments, the obesity survey information and survey communication information corresponding to the target customer are obtained in the form of a questionnaire. If the target customer is communicated with by an employee, the employee communicates with the target customer based on the survey questions on the questionnaire and fills out the questionnaire. The questionnaire answer data obtained by filling out the questionnaire is the obesity survey information. The voice communication data obtained by the employee communicating with the target customer is the survey communication information. The questionnaire can be paper or electronic. If it is paper, it needs to be converted into electronic data later to facilitate processing in subsequent steps.

[0084] In some embodiments, a special artificial intelligence can be set up to communicate with the target customer. The artificial intelligence communicates with the target customer through the pre-set obesity information survey items, thereby obtaining obesity survey information and survey communication information. By configuring the voice package through artificial intelligence, information collection can be performed in a more standardized manner to obtain obesity survey information and survey communication information.

[0085] In step S102 of some embodiments, the target general suggestion suitable for the target customer is determined from a series of preset candidate general suggestions through the collected obesity survey information. Obesity survey information obtained in a standardized manner can quickly respond to filter out target general suggestions suitable for the target customer from multiple preset candidate general suggestions. Target general suggestions refer to suggestions suitable for target customers with specific obesity characteristics obtained based on the analysis of obesity survey information, which can provide the most basic weight loss guidance.

[0086] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S203:

[0087] Step S201, performing obesity characteristic analysis on the obesity manifestations of target customers based on obesity survey information to obtain obesity characteristic information;

[0088] Step S202, feature matching is performed based on the obesity feature information and a plurality of preset typical feature information to obtain typical feature matching information; wherein each typical feature information corresponds to a candidate general suggestion;

[0089] Step S203: Based on the typical feature matching information, the corresponding candidate general suggestion is determined as the target general suggestion.

[0090] In step S201 of some embodiments, the target customer's obesity manifestations are analyzed in depth based on the collected obesity survey information to identify and extract obesity characteristic information. The obesity characteristic information is the obesity characteristics of the target customer, involving indicators such as the target customer's weight, body fat percentage, and body shape.

[0091] In step S202 of some embodiments, since there is a certain correlation between the causes of obesity and the corresponding obesity characteristics, there are several common causes of obesity, and these common causes can obtain corresponding typical characteristic information. These typical characteristic information are also common obesity characteristic information of customers. For these common typical characteristic information, corresponding candidate general suggestions are configured. Candidate general suggestions are basic suggestions applicable to the corresponding typical characteristic information.

[0092] Feature matching is performed based on the obesity feature information obtained from the target customer and a plurality of existing typical feature information to determine whether there is matching typical feature information, and obtain typical feature matching information.

[0093] In step S203 of some embodiments, matching typical feature information is determined based on the typical feature matching information, and the corresponding candidate general suggestion is determined as the target general suggestion.

[0094] Through steps S201 to S203, obesity characteristic information can be obtained in response to the obesity survey information of the target customer, and then matched with multiple existing common typical characteristic information to obtain corresponding target general suggestions, thereby providing the target customer with adaptive basic weight loss guidance information.

[0095] See also Figure 3 In some embodiments, before step S102, the following steps may be included but not limited to steps S301 to S302:

[0096] Step S301, obtaining a historical service data set, wherein the historical service data set includes a plurality of weight loss suggestion information, each piece of weight loss suggestion information corresponds to a type of obesity characteristic information;

[0097] Step S302: Common obesity feature information in the historical service data set is determined as typical feature information, and weight loss suggestion information corresponding to each typical feature information is integrated to obtain a plurality of candidate general suggestions.

[0098] In step S301 of some embodiments, a historical service data set is obtained, which includes multiple weight loss suggestion information, each weight loss suggestion information corresponds to a type of obesity characteristic information. These data are accumulated through previous customer service experience and include weight loss suggestion information under various obesity conditions.

[0099] In step S302 of some embodiments, common obesity feature information is identified from the historical service data set and determined as typical feature information, which represents the obesity types and features commonly found in a wide range of customer groups. The weight loss advice information corresponding to the typical feature information is integrated to form a plurality of candidate general advices, which are weight loss strategies and advices that have been repeatedly and proven to be effective based on historical data, and can provide a standardized starting point for customized weight loss services for target customers, thereby ensuring the consistency and quality of the services.

[0100] Through step S301 to step S302, a series of candidate general suggestions can be formed based on historical experience and data analysis, and these suggestions will be used in step S102 to match with the obesity characteristic information of the target customer to determine the final target general suggestion.

[0101] In step S103 of some embodiments, the survey communication information is identified by voice recognition technology to obtain the communication content information with the target customer. In some embodiments, if the person communicating with the target customer is an employee, the employee's voice data will be collected in advance to obtain the voice information of the corresponding employee, so as to better distinguish the voice information of the employee and the target customer when performing voice recognition on the survey communication information, and obtain accurate communication content information.

[0102] In step S104 of some embodiments, the obesity survey information provides information of the target customers including but not limited to weight, eating habits, lifestyle, weight loss goals, etc. The target prompt word template can be determined from a plurality of preset candidate prompt word templates.

[0103] See also Figure 4 In some embodiments, the obesity survey information includes customer status information and customer demand information. Step S104 may include but is not limited to steps S401 to S403:

[0104] Step S401, determining the obesity assessment standard of the target customer based on the customer status information; wherein the customer status information includes customer age information, customer gender information and customer diet information;

[0105] Step S402, determining the weight loss demand information of the target customer based on the customer demand information;

[0106] Step S403: determining a target prompt word template from a plurality of preset candidate prompt word templates based on the obesity assessment standard and the weight loss demand information.

[0107] In step S401 of some embodiments, the obesity survey information includes customer status information and customer demand information. The customer status information covers the basic information of the customer, including customer age information, customer gender information and customer diet information. Customers of different age groups and genders may face different health risks and weight loss challenges. Eating habits directly affect obesity status and the feasibility of weight loss. Eating habits can be obtained indirectly by analyzing the region where the target customers are located, or they can be obtained through direct eating data.

[0108] The obesity assessment standard for target customers needs to be adjusted according to different customer age information, customer gender information and customer diet information. The obesity assessment standard is the standard information for assessing the obesity status of target customers.

[0109] In step S402 of some embodiments, the target customer's weight loss demand information is determined based on the customer demand information, including the target customer's weight loss goal, expected results, and personal preferences, so as to better meet the target customer's needs.

[0110] In step S403 of some embodiments, a target prompt word template that best suits the target customer is determined from a plurality of preset candidate prompt word templates in combination with the obesity assessment standard and the weight loss demand information. Each preset candidate prompt word template is configured with an assessment weight corresponding to the obesity assessment standard and weight loss plan information that matches the weight loss demand information.

[0111] In some embodiments, the target customer's dietary information can be indirectly indicated based on the target customer's region, and then the specific obesity assessment criteria can be clarified through the customer's age information and customer gender information. The target customer's expected weight loss goal and the length of time to achieve the expected goal can be analyzed through the customer demand information to determine the target customer's weight loss demand information, and determine the target prompt word template from multiple preset candidate prompt word templates.

[0112] Through steps S401 to S403, the target prompt word template matching the target customer is accurately determined, which can better fit the objective situation and customer needs of the target customer, enhance the pertinence and effectiveness of the weight loss service, and improve the personalization of the weight loss service.

[0113] In step S105 of some embodiments, the adapted target prompt word template and the communication content information obtained by speech recognition are input into a pre-trained weight loss service model to obtain personalized suggestion information corresponding to the target customer.

[0114] The weight loss service big model is an artificial intelligence big model. The artificial intelligence big model can provide more accurate and personalized services, predict the specific obstacles that target customers may encounter in the weight loss process, and provide corresponding solutions.

[0115] See also Figure 5 In some embodiments, before step S105, the following steps may be included but not limited to steps S501 to S505:

[0116] Step S501, obtaining weight loss knowledge data; wherein the weight loss knowledge data includes knowledge theory data adapted to different customer status information;

[0117] Step S502, obtaining a model training set; wherein the model training set includes a plurality of weight loss service training samples corresponding to different customer status information, and each weight loss service training sample is configured with corresponding recommended sample information;

[0118] Step S503, generating recommended training information based on the weight loss service training samples and weight loss knowledge data corresponding to the same customer status information input into the original weight loss service macro model;

[0119] Step S504, comparing the recommended training information and the recommended sample information corresponding to the weight loss service training sample to obtain comparison deviation data;

[0120] Step S505, updating the model parameters of the weight loss service large model based on the comparison deviation data to obtain a pre-trained weight loss service large model.

[0121] In step S501 of some embodiments, weight loss knowledge data is obtained, and the weight loss knowledge data includes knowledge theories adapted to different customer status information, such as theoretical knowledge in the fields of nutrition, kinesiology, and health psychology.

[0122] In step S502 of some embodiments, a model training set is obtained, and the model training set includes multiple weight loss service training samples corresponding to different customer status information. Each weight loss service training sample is configured with corresponding recommended sample information. The weight loss service training sample is based on experience data obtained from previous weight loss services for customers, which can help the artificial intelligence large model learn and understand the relationship between different customer status information and recommended sample information.

[0123] In step S503 of some embodiments, based on the weight loss service training samples and weight loss knowledge data corresponding to the same customer status information, the original weight loss service big model is input to generate recommendation training information, so that the original weight loss service big model learns how to generate recommendation information according to the customer status.

[0124] In some embodiments, different training data sets can be constructed according to different ages, genders, and regions, so that when the weight loss service big model is used subsequently, the target prompt word template can be used to enable the weight loss service big model to provide accurate personalized suggestion information.

[0125] In step S504 of some embodiments, the suggested training information and suggested sample information corresponding to the weight loss service training sample are compared to obtain comparison deviation data, which is data for evaluating whether the suggested information generated by the weight loss service large model is accurate.

[0126] In step S505 of some embodiments, the model parameters of the weight loss service big model are updated based on the comparison deviation data to obtain a pre-trained weight loss service big model. The model parameters of the weight loss service big model are further fine-tuned by comparing the accuracy of the suggestion information generated by the weight loss service big model represented by the deviation data to improve the accuracy and reliability of the weight loss service big model until the suggestion training information generated by the weight loss service big model is similar to the suggestion sample information.

[0127] Through steps S501 to S505, it is ensured that the weight loss service model has been fully preprocessed and trained before generating personalized weight loss suggestions, and can give personalized suggestion information based on customer status information.

[0128] In step S106 of some embodiments, the target general suggestions and personalized suggestion information are combined to determine the target customized weight loss suggestions, and generate a specific customized weight loss plan. The customized weight loss plan not only considers the target customer's health status, living habits and weight loss goals, but also considers the target customer's preference information and possible weight loss obstacles, thereby realizing customized weight loss services for target customers.

[0129] See also Figure 6In some embodiments, after step S106, the following steps may include but are not limited to steps S601 to S603:

[0130] Step S601, generating a weight loss service knowledge graph using the target customer's obesity survey information, communication content information, and target customized weight loss suggestions;

[0131] Step S602, continuously updating the weight loss service knowledge graph according to the subsequent weight loss operation of the target customer to obtain a target updated knowledge graph;

[0132] Step S603: Perform weight loss effect analysis based on the weight loss service knowledge graph and the target update knowledge graph to generate target adjustment suggestions.

[0133] In step S601 of some embodiments, the target customer's obesity survey information, communication content information, and target customized weight loss advice are integrated to generate a weight loss service knowledge graph. The weight loss service knowledge graph is a structured data representation that can link the target customer's personal data, communication data, and weight loss advice to form a comprehensive information view, thereby helping the target customer understand the current status and providing a reference framework for the subsequent continuous provision of weight loss services.

[0134] In step S602 of some embodiments, subsequent information of the target customer's weight loss operation based on the target customized weight loss suggestion is obtained, and the weight loss service knowledge graph is continuously updated to obtain the target update knowledge graph, thereby reflecting the target customer's weight loss situation in real time, such as weight change and energy intake change.

[0135] In step S603 of some embodiments, a weight loss effect analysis is performed based on the weight loss service knowledge graph and the target update knowledge graph, and a corresponding target adjustment suggestion is generated. The target adjustment suggestion refers to a weight loss suggestion updated according to the actual weight loss situation of the target customer.

[0136] In some embodiments, the weight loss service knowledge graph and the goal update knowledge graph may be input into the big model to update or supplement the suggestion information and obtain goal adjustment suggestions.

[0137] Through steps S601 to S603, dynamic adjustments can be made based on the target customer's feedback and weight loss progress, and suggestions can be optimized based on actual conditions to obtain target adjustment suggestions, thereby ensuring that the target customer is always on the best weight loss path, enhancing the flexibility of the weight loss method, and providing the target customer with more accurate and feasible suggestion information, thereby improving the quality of weight loss services.

[0138] See also Figure 7In some embodiments, step S101 is to obtain obesity survey information and survey communication information by conducting survey communication between target employees and target customers, and after step S105, steps S701 to S705 may be included but are not limited to:

[0139] Step S701, performing voice role recognition based on the communication content information to obtain first voice data corresponding to the target employee and second voice data corresponding to the target customer;

[0140] Step S702, performing semantic recognition based on the first voice data to obtain employee voice recognition information;

[0141] Step S703, performing service content analysis on the employee voice recognition information according to a preset employee service evaluation standard to obtain corresponding employee evaluation information;

[0142] Step S704, performing voice emotion analysis based on the second voice data to obtain satisfaction information of the target customer;

[0143] Step S705: obtaining the service evaluation information of the target employee based on the employee evaluation information and satisfaction information.

[0144] In step S701 of some embodiments, voice role recognition is performed based on the communication content information to distinguish the voice data of the target employee and the target customer, and obtain the first voice data corresponding to the target employee and the second voice data corresponding to the target customer.

[0145] In step S702 of some embodiments, semantic recognition is performed on the first voice data to obtain employee voice recognition information. The first voice data may be semantically recognized using a neural network model trained using natural language processing technology.

[0146] In step S703 of some embodiments, the employee speech recognition information is analyzed for service content according to the preset employee service evaluation standard to obtain employee evaluation information. The speech content of the target employee is compared with the preset employee service evaluation standard to evaluate whether the target employee follows the established service process and whether the appropriate service terms are used, which helps to identify potential problems and improvement points in the weight loss service process and obtain employee evaluation information.

[0147] The employee service evaluation standards include service integrity, affinity, expression ability, judgment ability, guidance ability and other service indicators. Target employees can be scored based on various service indicators to obtain employee evaluation information.

[0148] In step S704 of some embodiments, voice sentiment analysis is performed based on the second voice data of the target customer to obtain customer satisfaction information. By analyzing the intonation factors and sentiment factors in the voice of the target customer, the target customer's satisfaction with the customized weight loss service provided by the target employee can be evaluated.

[0149] In step S705 of some embodiments, the service evaluation information of the target employee is obtained by combining the employee evaluation information and the target customer satisfaction information. The service evaluation information not only helps to improve the quality of customized weight loss services, but also provides a basis for employee training and performance management.

[0150] Through step S701 to step S705, a service evaluation process for target employees is provided, which can provide feedback information to target employees by providing service evaluation information, which is conducive to further improving the quality of weight loss services.

[0151] In a complete embodiment, a tablet device can be used as an information collection device, and communication can be conducted between target employees and target customers. An electronic questionnaire is stored in the tablet device as an obesity information survey project, and the communication content between the target employees and target customers is recorded to obtain survey communication information. The obesity survey information is obtained through the results of filling out the electronic questionnaire. The tablet device then transmits the obtained obesity survey information and survey communication information to a dedicated server for analysis. Some steps that do not require high computing power can also be analyzed directly by the tablet device, such as analyzing the obesity survey information of the target customers to obtain the corresponding obesity feature information, and matching the features with several existing typical feature information, determining the matching typical feature information and obtaining the corresponding candidate general suggestions as the target general suggestions.

[0152] The server extracts customer status information related to the target customer through the obesity survey information, determines the obesity assessment standard of the target customer, and extracts the target customer's demand information to obtain weight loss demand information. According to the obesity assessment standard and weight loss demand information, the target prompt word template is determined from multiple pre-configured candidate prompt word templates to obtain personalized suggestion information that is more suitable for the target customer. Then, the survey communication information is voice recognized to obtain the communication content information, and the communication content information and the target prompt word template are input into the pre-trained weight loss service model to generate personalized suggestion information.

[0153] Feedback the target general suggestions and personalized suggestions information through the tablet device. According to the target general suggestions and personalized suggestions information, the target customized weight loss suggestions can be determined. The target employees can assist in generating specific customized weight loss plans based on the target customized weight loss suggestions, or they can be directly generated by the tablet device or server and communicated to the target customers.

[0154] The embodiment of the present application obtains the target customer's obesity survey information and survey communication information according to the preset obesity information survey project, and determines the target general suggestion from the preset multiple candidate general suggestions based on the obesity survey information, and then performs voice recognition information based on the survey communication information to obtain the communication content information corresponding to the target customer, and determines the target prompt word template from the preset multiple candidate prompt word templates based on the obesity survey information corresponding to the target customer, and inputs the target prompt word template and the communication content information into the pre-trained weight loss service big model to generate personalized suggestion information through the weight loss service big model, and determines the target customized weight loss suggestion according to the target general suggestion and personalized suggestion information. It can be seen that the present application obtains the target customer's obesity survey information and survey communication information, and obtains the target general suggestion adapted to the target customer through the obesity survey information, so as to obtain the basic suggestion information for solving the target customer's obesity problem, and then uses the communication content information with the target customer and the adapted target prompt word template to input into the pre-trained weight loss service big model to generate the target customer's personalized suggestion information, and determines the target customized weight loss suggestion by combining the target general suggestion and personalized suggestion information, so as to solve the target customer's weight loss needs in a targeted manner and provide a customized weight loss service with stable quality.

[0155] See also Figure 8 The embodiment of the present application also provides a customized weight loss suggestion generating device, which can implement the above-mentioned customized weight loss suggestion generating method, and the device includes:

[0156] An obesity information survey module, used to obtain obesity survey information and survey communication information corresponding to target customers according to preset obesity information survey items;

[0157] A general advice providing module, used for determining a target general advice from a plurality of preset candidate general advices based on obesity survey information;

[0158] A speech recognition module is used to perform speech recognition information based on the survey communication information to obtain the communication content information of the corresponding target customers;

[0159] A prompt word template determination module, used to determine a target prompt word template from a plurality of preset candidate prompt word templates based on obesity survey information of corresponding target customers;

[0160] A big model analysis module, used for inputting the target prompt word template and the communication content information into the pre-trained weight loss service big model, so as to generate personalized suggestion information through the weight loss service big model;

[0161] The weight loss suggestion module is used to determine target customized weight loss suggestions based on target general suggestions and personalized suggestion information.

[0162] The specific implementation of the customized weight loss suggestion generating device is substantially the same as the specific implementation of the customized weight loss suggestion generating method described above, and will not be described in detail herein.

[0163] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned customized weight loss suggestion generation method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0164] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0165] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0166] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the customized weight loss suggestion generation method of the embodiment of this application;

[0167] Input / output interface 903, used to implement information input and output;

[0168] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0169] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0170] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0171] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned customized weight loss suggestion generation method is implemented.

[0172] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0173] The customized weight loss advice generation method, device, electronic device and storage medium provided in the embodiments of the present application obtain obesity survey information and survey communication information of target customers according to preset obesity information survey items, and determine target general advice from preset multiple candidate general advices based on the obesity survey information, and then perform voice recognition information based on the survey communication information to obtain communication content information corresponding to the target customer, and determine a target prompt word template from preset multiple candidate prompt word templates based on the obesity survey information corresponding to the target customer, and input the target prompt word template and communication content information into a pre-trained weight loss service big model to generate personalized advice information through the weight loss service big model, and determine the target customized weight loss advice based on the target general advice and the personalized advice information. It can be seen from this that the present application obtains the target customers' obesity survey information and survey communication information, and obtains target general suggestions adapted to the target customers through the obesity survey information, thereby obtaining basic suggestion information for solving the target customers' obesity problems, and then uses the communication content information with the target customers and the adapted target prompt word template to input into the pre-trained weight loss service large model to generate personalized suggestion information for the target customers. By combining the target general suggestions and personalized suggestion information, the target customized weight loss suggestions are determined, which can solve the target customers' weight loss needs in a targeted manner and provide customized weight loss services with stable quality.

[0174] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0175] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0176] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0178] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0179] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0180] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0181] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0183] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0184] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for generating customized weight loss suggestions, characterized in that: The method comprises: According to the preset obesity information survey items, obtain obesity survey information and survey communication information corresponding to the target customers; Determining a target general recommendation from a plurality of preset candidate general recommendations based on the obesity survey information; Perform voice recognition based on the survey communication information to obtain communication content information corresponding to the target customer; Based on the obesity survey information corresponding to the target customer, determining a target prompt word template from a plurality of preset candidate prompt word templates; Inputting the target prompt word template and the communication content information into a pre-trained weight loss service model to generate personalized suggestion information through the weight loss service model; A target customized weight loss suggestion is determined based on the target general suggestion and the personalized suggestion information.

2. The method according to claim 1, characterized in that The step of determining a target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information includes: Based on the obesity survey information, an obesity characteristic analysis is performed on the obesity manifestations of the target customers to obtain obesity characteristic information; Based on the obesity feature information, feature matching is performed with a plurality of preset typical feature information to obtain typical feature matching information; wherein each of the typical feature information corresponds to one of the candidate general suggestions; Based on the typical feature matching information, the corresponding candidate general suggestion is determined as the target general suggestion.

3. The method according to claim 2, characterized in that Before determining the target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information, the method further includes pre-configuring a plurality of the candidate general suggestions, specifically including: Acquire a historical service data set, wherein the historical service data set includes a plurality of weight loss suggestion information, and each piece of weight loss suggestion information corresponds to a type of obesity characteristic information; The common obesity feature information in the historical service data set is determined as the typical feature information, and the weight loss suggestion information corresponding to each of the typical feature information is integrated to obtain a plurality of candidate general suggestions.

4. The method according to claim 1, characterized in that: The obesity survey information includes customer status information and customer demand information. The step of determining a target prompt word template from a plurality of preset candidate prompt word templates based on the obesity survey information corresponding to the target customer includes: Determining the obesity assessment standard of the target customer based on the customer status information; wherein the customer status information includes customer age information, customer gender information and customer diet information; Determining the weight loss demand information of the target customer based on the customer demand information; A target prompt word template is determined from a plurality of preset candidate prompt word templates based on the obesity assessment standard and the weight loss demand information.

5. The method according to claim 1, characterized in that: The step of obtaining obesity survey information and survey communication information corresponding to target customers according to preset obesity information survey items includes: According to the preset obesity information survey items, the target employee conducts survey communication with the target customer to obtain the obesity survey information and the survey communication information; After inputting the target prompt word template and the communication content information into the pre-trained weight loss service model to generate personalized suggestion information through the weight loss service model, the method further includes: Perform voice role recognition based on the communication content information to obtain first voice data corresponding to the target employee and second voice data corresponding to the target customer; Performing semantic recognition based on the first voice data to obtain employee voice recognition information; Performing service content analysis on the employee voice recognition information according to preset employee service evaluation standards to obtain corresponding employee evaluation information; Performing voice sentiment analysis based on the second voice data to obtain satisfaction information of the target customer; The service evaluation information of the target employee is obtained based on the employee evaluation information and the satisfaction information.

6. The method according to claim 1, characterized in that After determining the target customized weight loss suggestion according to the target general suggestion and the personalized suggestion information, the method includes: Generate a weight loss service knowledge graph based on the obesity survey information, the communication content information and the target customized weight loss suggestions of the target customer; Continuously updating the weight loss service knowledge graph according to subsequent weight loss operations of the target customer to obtain a target updated knowledge graph; Based on the weight loss service knowledge graph and the target update knowledge graph, a weight loss effect analysis is performed to generate target adjustment suggestions.

7. The method according to claim 4, characterized in that Before inputting the target prompt word template and the communication content information into the pre-trained weight loss service model to generate personalized suggestion information through the weight loss service model, it also includes pre-training with the weight loss service model, specifically including: Obtaining weight loss knowledge data; wherein the weight loss knowledge data includes knowledge theory data adapted to different customer status information; Acquire a model training set; wherein the model training set includes a plurality of weight loss service training samples corresponding to different customer status information, and each of the weight loss service training samples is configured with corresponding recommended sample information; Generate recommended training information based on the weight loss service training samples and the weight loss knowledge data corresponding to the same customer status information input into the original weight loss service macro model; Comparing the recommended training information corresponding to the weight loss service training sample with the recommended sample information to obtain comparison deviation data; The model parameters of the weight loss service large model are updated based on the comparison deviation data to obtain the pre-trained weight loss service large model.

8. A customized weight loss suggestion generating device, characterized in that: The device comprises: An obesity information survey module, used to obtain obesity survey information and survey communication information corresponding to target customers according to preset obesity information survey items; A general suggestion providing module, used for determining a target general suggestion from a plurality of preset candidate general suggestions based on the obesity survey information; A speech recognition module, used to perform speech recognition information based on the survey communication information to obtain communication content information corresponding to the target customer; A prompt word template determination module, used to determine a target prompt word template from a plurality of preset candidate prompt word templates based on the obesity survey information corresponding to the target customer; A large model analysis module, used for inputting the target prompt word template and the communication content information into a pre-trained weight loss service large model, so as to generate personalized suggestion information through the weight loss service large model; The weight loss suggestion providing module is used to determine the target customized weight loss suggestion according to the target general suggestion and the personalized suggestion information.

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

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for generating customized weight loss advice according to any one of claims 1 to 7 is implemented.

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