Food quality intelligent evaluation and grading system and method

Abstract

The embodiment of the invention relates to the technical field of food safety, and particularly discloses an intelligent evaluation and grading system and method for food quality. According to the embodiment of the invention, a food image uploaded by a user is received, positioning analysis and image recognition are carried out on the food image, and a food merchant and a food target are determined; obtaining food evaluation data; performing basic evaluation to obtain a basic evaluation score; performing image correlation matching on the food evaluation data according to the food image, and extracting a plurality of feedback evaluation scores from the food evaluation data; and performing comprehensive grading analysis on the basic evaluation score and the plurality of feedback evaluation scores, and determining the food quality level of the food target. According to the method, food merchants and food targets can be identified and determined, food evaluation data can be obtained, comprehensive grading analysis of basic evaluation and image correlation matching can be carried out, the food quality levels of the food targets can be determined, rich experience and professional equipment are not needed, and evaluation and grading of food quality can be efficiently, conveniently, rapidly and accurately completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of food safety, and in particular relates to a food quality intelligent assessment and grading system and method. Background Art

[0002] Food quality assessment is the process of comprehensively inspecting and evaluating the sensory characteristics, physical and chemical properties, microbiological status, packaging, labeling, storage and transportation of food through a series of scientific methods and standards. It aims to ensure that food complies with relevant laws, standards and consumer expectations, thereby protecting public health and safety.

[0003] In the existing technology, food quality assessment is a complex process involving many aspects of consideration, covering a series of professional methods such as sensory inspection, physical and chemical inspection, and hygiene inspection. It often requires rich practical experience and professional knowledge, or relies on professional equipment for testing and analysis. The process of food quality assessment is complicated and highly specialized, and it is impossible to complete the assessment and grading of food quality efficiently, conveniently, quickly and accurately. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a food quality intelligent assessment and grading system and method, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for intelligently evaluating and grading food quality, the method specifically comprising the following steps: Receive food images uploaded by users, perform location analysis and image recognition on the food images, and determine food merchants and food targets; Acquiring food evaluation data according to the food merchant and the food target; Performing a basic evaluation on the food image to obtain a basic evaluation score; According to the food image, performing image correlation matching on the food evaluation data, and extracting a plurality of feedback evaluation scores from the food evaluation data; A comprehensive grading analysis is performed on the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target.

[0006] As a further limitation of the technical solution of the embodiment of the present invention, the receiving of the food image uploaded by the user, performing location analysis and image recognition on the food image, and determining the food merchant and the food target specifically include the following steps: Receiving food images uploaded by users; Extracting shooting location data from the food image; Performing type recognition on the food image to determine the food type; According to the photographed positioning data and the food type, a merchant positioning analysis is performed in a preset electronic map to determine the food merchant; Obtaining food details of the food merchant; According to the food type, a food target is matched from the food details.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the obtaining of food evaluation data according to the food merchant and the food target specifically includes the following steps: Obtaining feedback and evaluation data of the food merchant; According to the food target, food evaluation data is filtered from the feedback evaluation data.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, performing basic evaluation on the food image and obtaining a basic evaluation score specifically includes the following steps: acquiring a plurality of standard evaluation images according to the food type; Performing feature recognition on the food image to obtain multiple food features; Based on the plurality of standard evaluation images, matching and identifying the plurality of food features, and selecting a matching evaluation image from the plurality of standard evaluation images; A basic evaluation score is determined based on the matching evaluation image.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, performing image correlation matching on the food evaluation data according to the food image and extracting multiple feedback evaluation scores from the food evaluation data specifically comprises the following steps: Filtering evaluation image data from the food evaluation data; Based on the evaluation image data, performing image correlation matching on the food image, and selecting a plurality of relevant matching images; From the evaluation image data, feedback evaluation scores of the plurality of relevant matching images are extracted.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the comprehensive grading analysis of the basic evaluation score and the multiple feedback evaluation scores to determine the food quality level of the food target specifically includes the following steps: Performing abnormality identification on the plurality of feedback evaluation scores to determine a plurality of abnormal evaluation scores; Among the multiple feedback evaluation scores, multiple abnormal evaluation scores are eliminated and optimized to obtain multiple valid evaluation scores; Performing a comprehensive evaluation calculation on the basic evaluation score and the multiple effective evaluation scores to generate a comprehensive evaluation score; Based on the preset score grading information, the comprehensive evaluation scores are graded and matched to determine the food quality level of the food target.

[0011] A food quality intelligent evaluation and grading system, the system comprises a food image processing unit, an evaluation data acquisition unit, a food image basic evaluation unit, an image correlation matching unit and a comprehensive grading analysis unit, wherein: A food image processing unit, used to receive food images uploaded by users, perform location analysis and image recognition on the food images, and determine food merchants and food targets; An evaluation data acquisition unit, used for acquiring food evaluation data according to the food merchant and the food target; A food image basic evaluation unit, used to perform basic evaluation on the food image and obtain a basic evaluation score; An image correlation matching unit, configured to perform image correlation matching on the food evaluation data according to the food image, and extract a plurality of feedback evaluation scores from the food evaluation data; The comprehensive grading analysis unit is used to perform a comprehensive grading analysis on the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the food image processing unit specifically includes: An image receiving module, used to receive food images uploaded by users; A positioning extraction module, used to extract shooting positioning data from the food image; A type recognition module, used to perform type recognition on the food image to determine the food type; A merchant location analysis module, used to perform merchant location analysis in a preset electronic map according to the shooting location data and the food type, and determine the food merchant; A details acquisition module, used to acquire the food details of the food merchant; A food target matching module is used to match food targets from the food details according to the food type.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the food image basic evaluation unit specifically includes: An image acquisition module, used for acquiring a plurality of standard evaluation images according to the food type; A feature recognition module, used to perform feature recognition on the food image to obtain multiple food features; A matching and identifying module, used for matching and identifying a plurality of food features based on a plurality of the standard evaluation images, and selecting a matching evaluation image from the plurality of the standard evaluation images; The basic score determination module is used to determine a basic evaluation score according to the matching evaluation image.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the comprehensive grading analysis unit specifically includes: An anomaly identification module, used to identify anomalies of the plurality of feedback evaluation scores and determine a plurality of anomaly evaluation scores; A elimination optimization module, used for eliminating and optimizing a plurality of abnormal evaluation scores among a plurality of feedback evaluation scores to obtain a plurality of valid evaluation scores; A comprehensive evaluation calculation module, used for performing comprehensive evaluation calculation on the basic evaluation score and the multiple effective evaluation scores to generate a comprehensive evaluation score; The grading matching module is used to grade and match the comprehensive evaluation scores based on preset score grading information to determine the food quality level of the food target.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention receives food images uploaded by users, performs positioning analysis and image recognition on the food images, determines food merchants and food targets; obtains food evaluation data; performs basic evaluation and obtains basic evaluation scores; performs image correlation matching on the food evaluation data according to the food images, extracts multiple feedback evaluation scores from the food evaluation data; performs comprehensive grading analysis on the basic evaluation scores and multiple feedback evaluation scores to determine the food quality level of the food target. The food merchants and food targets can be identified and determined, food evaluation data can be obtained, comprehensive grading analysis of basic evaluation and image correlation matching can be performed, and the food quality level of the food target can be determined. No rich experience and professional equipment are required, and the evaluation and grading of food quality can be completed efficiently, conveniently, quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0017] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0018] Figure 2 A flow chart of determining food merchants and food targets in the method provided by an embodiment of the present invention is shown.

[0019] Figure 3 A flow chart of obtaining food evaluation data in the method provided in an embodiment of the present invention is shown.

[0020] Figure 4 A flow chart of obtaining a basic evaluation score in the method provided in an embodiment of the present invention is shown.

[0021] Figure 5 A flow chart of image correlation matching in the method provided by an embodiment of the present invention is shown.

[0022] Figure 6 A flow chart of comprehensive hierarchical analysis in the method provided by an embodiment of the present invention is shown.

[0023] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0024] Figure 8 The structure block diagram of the food image processing unit in the system provided by the embodiment of the present invention is shown.

[0025] Fig. 9 The structure block diagram of the basic food image evaluation unit in the system provided by the embodiment of the present invention is shown.

[0026] Fig.10 The structure block diagram of the comprehensive hierarchical analysis unit in the system provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention.

[0028] It is understandable that in the existing technology, food quality assessment is a complex process involving many aspects of consideration, covering a series of professional methods such as sensory inspection, physical and chemical inspection, and hygiene inspection. It often requires rich practical experience and professional knowledge, or relies on professional equipment for testing and analysis. The process of food quality assessment is complicated and highly specialized, and it is impossible to complete the assessment and grading of food quality efficiently, conveniently, quickly and accurately.

[0029] To solve the above problems, the embodiment of the present invention receives food images uploaded by users, performs positioning analysis and image recognition on the food images, and determines the food merchants and food targets; obtains food evaluation data based on the food merchants and food targets; performs basic evaluation on the food images to obtain basic evaluation scores; performs image correlation matching on the food evaluation data based on the food images, and extracts multiple feedback evaluation scores from the food evaluation data; performs comprehensive grading analysis on the basic evaluation scores and multiple feedback evaluation scores to determine the food quality level of the food target. It is possible to identify and determine food merchants and food targets, obtain food evaluation data, perform comprehensive grading analysis of basic evaluation and image correlation matching, and determine the food quality level of the food target. It does not require extensive experience and professional equipment, and can efficiently, conveniently, quickly and accurately complete the evaluation and grading of food quality.

[0030] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0031] Specifically, a method for intelligently evaluating and grading food quality comprises the following steps: Step S101, receiving a food image uploaded by a user, performing location analysis and image recognition on the food image, and determining a food merchant and a food target.

[0032] In an embodiment of the present invention, in daily life, when a user has a need to assess and grade food quality, the user can open relevant software on a smart device, upload the captured food image on the software interface, and perform attribute recognition on the food image by receiving the food image uploaded by the user. From the image attribute data, the shooting location data is extracted, and the type of the food image is recognized to determine the food type. Then, according to the shooting location data and the food type, a merchant location analysis is performed in a preset electronic map to determine the food merchant corresponding to the photographed food. Then, through big data technology, the food merchant's food details can be obtained from platform channels such as group buying platforms and food platforms, and then the relevant food targets can be matched from the food details according to the food type.

[0033] Specifically, Figure 2 A flow chart of determining food merchants and food targets in the method provided by an embodiment of the present invention is shown.

[0034] Among them, in the preferred embodiment provided by the present invention, the receiving of the food image uploaded by the user, performing location analysis and image recognition on the food image, and determining the food merchant and the food target specifically include the following steps: Step S1011, receiving food images uploaded by users; Step S1012, extracting shooting location data from the food image; Step S1013, performing type recognition on the food image to determine the food type; Step S1014, performing merchant location analysis in a preset electronic map according to the shooting location data and the food type, and determining the food merchant; Step S1015, obtaining food details of the food merchant; Step S1016: Match food targets from the food details according to the food type.

[0035] Furthermore, the food quality intelligent assessment and grading method further comprises the following steps: Step S102, obtaining food evaluation data according to the food merchant and the food target.

[0036] In an embodiment of the present invention, feedback evaluation data of food merchants are obtained from platform channels such as group buying platforms and food platforms, and then the feedback evaluation data are matched and screened according to food targets, and food evaluation data only related to food targets are screened from the feedback evaluation data.

[0037] Specifically, Figure 3 A flow chart of obtaining food evaluation data in the method provided in an embodiment of the present invention is shown.

[0038] Among them, in the preferred embodiment provided by the present invention, the obtaining of food evaluation data according to the food merchant and the food target specifically includes the following steps: Step S1021, obtaining feedback evaluation data of the food merchant; Step S1022: Filter food evaluation data from the feedback evaluation data according to the food target.

[0039] Furthermore, the food quality intelligent assessment and grading method further comprises the following steps: Step S103: perform basic evaluation on the food image to obtain a basic evaluation score.

[0040] In an embodiment of the present invention, based on big data technology, multiple standard evaluation images related to food types are obtained, and food correction is performed on the food images to remove the filter effects that come with shooting, generate actual images, and then perform feature recognition on the actual images to obtain multiple food features. Then, based on the multiple standard evaluation images, the multiple food features are matched and identified, and from the multiple standard evaluation images, a matching evaluation image that can match the multiple food features is selected, and then the evaluation score corresponding to the matching evaluation image is obtained to obtain a basic evaluation score.

[0041] Specifically, Figure 4 A flow chart of obtaining a basic evaluation score in the method provided in an embodiment of the present invention is shown.

[0042] Among them, in the preferred embodiment provided by the present invention, the basic evaluation of the food image and obtaining the basic evaluation score specifically include the following steps: Step S1031, acquiring a plurality of standard evaluation images according to the food type; Step S1032, performing feature recognition on the food image to obtain multiple food features; Step S1033, matching and identifying the plurality of food features based on the plurality of standard evaluation images, and selecting a matching evaluation image from the plurality of standard evaluation images; Step S1034: determining a basic evaluation score according to the matching evaluation image.

[0043] Furthermore, the food quality intelligent assessment and grading method further comprises the following steps: Step S104: performing image correlation matching on the food evaluation data according to the food image, and extracting a plurality of feedback evaluation scores from the food evaluation data.

[0044] In an embodiment of the present invention, evaluation image data is screened from food evaluation data, and then image correlation matching is performed on food images based on the evaluation image data. Multiple related matching images with the same food features as the food images are matched from multiple evaluation feedback images of the evaluation image data. Then, related feedback information corresponding to the multiple related matching images is screened in the evaluation image data, and multiple corresponding feedback evaluation scores are extracted from the multiple related feedback information.

[0045] Specifically, Figure 5 A flow chart of image correlation matching in the method provided by an embodiment of the present invention is shown.

[0046] Among them, in the preferred embodiment provided by the present invention, performing image correlation matching on the food evaluation data according to the food image and extracting multiple feedback evaluation scores from the food evaluation data specifically comprises the following steps: Step S1041, screening evaluation image data from the food evaluation data; Step S1042, performing image correlation matching on the food image based on the evaluation image data, and selecting a plurality of relevant matching images; Step S1043: extracting feedback evaluation scores of the plurality of related matching images from the evaluation image data.

[0047] Furthermore, the food quality intelligent assessment and grading method further comprises the following steps: Step S105, performing a comprehensive grading analysis on the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target.

[0048] In an embodiment of the present invention, anomalies are identified for multiple feedback evaluation scores to determine the highest and lowest abnormal evaluation scores, and then among the multiple feedback evaluation scores, the highest and lowest abnormal evaluation scores are eliminated and optimized, retaining multiple valid evaluation scores, and then a comprehensive evaluation is performed on the basic evaluation score and the multiple valid evaluation scores by averaging to obtain a comprehensive evaluation score. Based on preset score grading information, the comprehensive evaluation scores are graded and matched to determine the food quality level of the food target and displayed on the smart device.

[0049] Specifically, Figure 6 A flow chart of comprehensive hierarchical analysis in the method provided by an embodiment of the present invention is shown.

[0050] Among them, in the preferred embodiment provided by the present invention, the comprehensive grading analysis of the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target specifically includes the following steps: Step S1051, performing abnormality identification on the plurality of feedback evaluation scores to determine a plurality of abnormal evaluation scores; Step S1052, among the multiple feedback evaluation scores, eliminate and optimize the multiple abnormal evaluation scores to obtain multiple valid evaluation scores; Step S1053, performing comprehensive evaluation calculation on the basic evaluation score and the multiple effective evaluation scores to generate a comprehensive evaluation score; Step S1054, based on the preset score grading information, the comprehensive evaluation scores are graded and matched to determine the food quality level of the food target.

[0051] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0052] Among them, in another preferred embodiment provided by the present invention, a food quality intelligent assessment and grading system includes: The food image processing unit 101 is used to receive food images uploaded by users, perform location analysis and image recognition on the food images, and determine food merchants and food targets.

[0053] In an embodiment of the present invention, in daily life, when a user has a need to assess and grade food quality, the user can open relevant software on the smart device and upload the captured food image on the software interface. The food image processing unit 101 receives the food image uploaded by the user, identifies the attribute of the food image, extracts the shooting location data from the image attribute data, and identifies the type of the food image to determine the food type. Then, according to the shooting location data and the food type, a merchant location analysis is performed in a preset electronic map to determine the food merchant corresponding to the photographed food. Then, through big data technology, the food merchant's food details can be obtained from platform channels such as group buying platforms and food platforms, and then the relevant food targets can be matched from the food details according to the food type.

[0054] Specifically, Figure 8 The structure block diagram of the food image processing unit 101 in the system provided by the embodiment of the present invention is shown.

[0055] Among them, in the preferred embodiment provided by the present invention, the food image processing unit 101 specifically includes: An image receiving module 1011 is used to receive food images uploaded by users; A positioning extraction module 1012, used to extract shooting positioning data from the food image; A type recognition module 1013 is used to perform type recognition on the food image to determine the food type; The merchant location analysis module 1014 is used to perform merchant location analysis in a preset electronic map according to the shooting location data and the food type, and determine the food merchant; A details acquisition module 1015 is used to acquire the food details of the food merchant; The food target matching module 1016 is used to match the food target from the food details according to the food type.

[0056] Furthermore, the food quality intelligent assessment and grading system also includes: The evaluation data acquisition unit 102 is used to acquire food evaluation data according to the food merchant and the food target.

[0057] In the embodiment of the present invention, the evaluation data acquisition unit 102 obtains feedback evaluation data of food merchants from platform channels such as group buying platforms and food platforms, and then matches and filters the feedback evaluation data according to food targets, and filters food evaluation data that is only related to the food targets from the feedback evaluation data.

[0058] The food image basic evaluation unit 103 is used to perform basic evaluation on the food image to obtain a basic evaluation score.

[0059] In an embodiment of the present invention, the food image basic evaluation unit 103 obtains multiple standard evaluation images related to food types based on big data technology, performs food correction on the food image, removes the filter effect that comes with the shooting, generates an actual image, and then performs feature recognition on the actual image to obtain multiple food features, and then matches and recognizes the multiple food features based on the multiple standard evaluation images, selects a matching evaluation image that can match multiple food features from the multiple standard evaluation images, and then obtains the evaluation score corresponding to the matching evaluation image to obtain a basic evaluation score.

[0060] Specifically, Fig. 9 The structure block diagram of the food image basic evaluation unit 103 in the system provided by the embodiment of the present invention is shown.

[0061] In a preferred embodiment of the present invention, the food image basic evaluation unit 103 specifically includes: An image acquisition module 1031 is used to acquire a plurality of standard evaluation images according to the food type; A feature recognition module 1032 is used to perform feature recognition on the food image to obtain multiple food features; A matching identification module 1033 is used to match and identify the plurality of food features based on the plurality of standard evaluation images, and select a matching evaluation image from the plurality of standard evaluation images; The basic score determination module 1034 is used to determine a basic evaluation score according to the matching evaluation image.

[0062] Furthermore, the food quality intelligent assessment and grading system also includes: The image correlation matching unit 104 is used to perform image correlation matching on the food evaluation data according to the food image, and extract multiple feedback evaluation scores from the food evaluation data.

[0063] In the embodiment of the present invention, the image correlation matching unit 104 screens the evaluation image data from the food evaluation data, and then performs image correlation matching on the food image based on the evaluation image data, matches multiple related matching images with the same food features as the food image from multiple evaluation feedback images of the evaluation image data, and then screens the related feedback information corresponding to the multiple related matching images in the evaluation image data, and extracts multiple corresponding feedback evaluation scores from the multiple related feedback information.

[0064] The comprehensive grading analysis unit 105 is used to perform a comprehensive grading analysis on the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target.

[0065] In an embodiment of the present invention, the comprehensive grading analysis unit 105 identifies abnormalities in multiple feedback evaluation scores to determine the highest and lowest abnormal evaluation scores, and then eliminates and optimizes the highest and lowest abnormal evaluation scores among the multiple feedback evaluation scores, retains multiple valid evaluation scores, and then performs an average calculation of the basic evaluation score and the multiple valid evaluation scores for a comprehensive evaluation to obtain a comprehensive evaluation score. Based on preset score grading information, the comprehensive evaluation scores are graded and matched to determine the food quality level of the food target, and displayed on the smart device.

[0066] Specifically, Fig.10 It shows a structural block diagram of the comprehensive hierarchical analysis unit 105 in the system provided by the embodiment of the present invention.

[0067] Among them, in the preferred embodiment provided by the present invention, the comprehensive classification analysis unit 105 specifically includes: An abnormality identification module 1051 is used to identify abnormalities of the plurality of feedback evaluation scores and determine a plurality of abnormality evaluation scores; The elimination optimization module 1052 is used to eliminate and optimize the multiple abnormal evaluation scores among the multiple feedback evaluation scores to obtain multiple valid evaluation scores; A comprehensive evaluation calculation module 1053 is used to perform comprehensive evaluation calculation on the basic evaluation score and the multiple effective evaluation scores to generate a comprehensive evaluation score; The grading matching module 1054 is used to perform grading matching on the comprehensive evaluation scores based on preset score grading information to determine the food quality level of the food target.

[0068] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0069] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0070] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent assessment and grading of food quality, characterized in that: The method specifically comprises the following steps: Receive food images uploaded by users, perform location analysis and image recognition on the food images, and determine food merchants and food targets; Acquiring food evaluation data according to the food merchant and the food target; Performing a basic evaluation on the food image to obtain a basic evaluation score; According to the food image, performing image correlation matching on the food evaluation data, and extracting a plurality of feedback evaluation scores from the food evaluation data; A comprehensive grading analysis is performed on the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target.

2. The method for intelligent food quality assessment and grading according to claim 1, characterized in that: The receiving of the food image uploaded by the user, performing location analysis and image recognition on the food image, and determining the food merchant and the food target specifically comprises the following steps: Receiving food images uploaded by users; Extracting shooting location data from the food image; Performing type recognition on the food image to determine the food type; According to the photographed positioning data and the food type, a merchant positioning analysis is performed in a preset electronic map to determine the food merchant; Obtaining food details of the food merchant; According to the food type, a food target is matched from the food details.

3. The method for intelligent food quality assessment and grading according to claim 1, characterized in that: The step of obtaining food evaluation data according to the food merchant and the food target specifically includes the following steps: Obtaining feedback and evaluation data of the food merchant; According to the food target, food evaluation data is filtered from the feedback evaluation data.

4. The method for intelligent food quality assessment and grading according to claim 2, characterized in that: The performing basic evaluation on the food image and obtaining the basic evaluation score specifically comprises the following steps: acquiring a plurality of standard evaluation images according to the food type; Performing feature recognition on the food image to obtain multiple food features; Based on the plurality of standard evaluation images, matching and identifying the plurality of food features, and selecting a matching evaluation image from the plurality of standard evaluation images; A basic evaluation score is determined based on the matching evaluation image.

5. The method for intelligent food quality assessment and grading according to claim 1, characterized in that: The step of performing image correlation matching on the food evaluation data according to the food image and extracting a plurality of feedback evaluation scores from the food evaluation data specifically comprises the following steps: Filtering evaluation image data from the food evaluation data; Based on the evaluation image data, performing image correlation matching on the food image, and selecting a plurality of relevant matching images; From the evaluation image data, feedback evaluation scores of the plurality of relevant matching images are extracted.

6. The method for intelligent food quality assessment and grading according to claim 1, characterized in that: The comprehensive grading analysis of the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target specifically comprises the following steps: Performing abnormality identification on the plurality of feedback evaluation scores to determine a plurality of abnormal evaluation scores; Among the multiple feedback evaluation scores, multiple abnormal evaluation scores are eliminated and optimized to obtain multiple valid evaluation scores; Performing a comprehensive evaluation calculation on the basic evaluation score and the multiple effective evaluation scores to generate a comprehensive evaluation score; Based on the preset score grading information, the comprehensive evaluation scores are graded and matched to determine the food quality level of the food target.

7. A food quality intelligent assessment and grading system, characterized in that: The system comprises a food image processing unit, an evaluation data acquisition unit, a food image basic evaluation unit, an image correlation matching unit and a comprehensive grading analysis unit, wherein: A food image processing unit, used to receive food images uploaded by users, perform location analysis and image recognition on the food images, and determine food merchants and food targets; An evaluation data acquisition unit, used for acquiring food evaluation data according to the food merchant and the food target; A food image basic evaluation unit, used to perform basic evaluation on the food image and obtain a basic evaluation score; An image correlation matching unit, configured to perform image correlation matching on the food evaluation data according to the food image, and extract a plurality of feedback evaluation scores from the food evaluation data; The comprehensive grading analysis unit is used to perform a comprehensive grading analysis on the basic evaluation score and the plurality of feedback evaluation scores to determine the food quality level of the food target.

8. The food quality intelligent assessment and grading system according to claim 7, characterized in that: The food image processing unit specifically comprises: An image receiving module, used to receive food images uploaded by users; A positioning extraction module, used to extract shooting positioning data from the food image; A type recognition module, used to perform type recognition on the food image to determine the food type; A merchant location analysis module, used to perform merchant location analysis in a preset electronic map according to the shooting location data and the food type, and determine the food merchant; A details acquisition module, used to acquire the food details of the food merchant; A food target matching module is used to match food targets from the food details according to the food type.

9. The food quality intelligent assessment and grading system according to claim 8, characterized in that: The food image basic evaluation unit specifically includes: An image acquisition module, used for acquiring a plurality of standard evaluation images according to the food type; A feature recognition module, used to perform feature recognition on the food image to obtain multiple food features; A matching and identifying module, used for matching and identifying a plurality of food features based on a plurality of the standard evaluation images, and selecting a matching evaluation image from the plurality of the standard evaluation images; The basic score determination module is used to determine a basic evaluation score according to the matching evaluation image.

10. The food quality intelligent assessment and grading system according to claim 7, characterized in that: The comprehensive grading analysis unit specifically includes: An anomaly identification module, used to identify anomalies of the plurality of feedback evaluation scores and determine a plurality of anomaly evaluation scores; A elimination optimization module, used for eliminating and optimizing a plurality of abnormal evaluation scores among a plurality of feedback evaluation scores to obtain a plurality of valid evaluation scores; A comprehensive evaluation calculation module, used for performing comprehensive evaluation calculation on the basic evaluation score and the multiple effective evaluation scores to generate a comprehensive evaluation score; The grading matching module is used to grade and match the comprehensive evaluation scores based on preset score grading information to determine the food quality level of the food target.

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