A dish individualization recognition control system based on a big data environment

Abstract

The application discloses a kind of recognition control system based on big data environment realizes dish individualization, including information acquisition analysis module, distance feeling determination module, touch analysis module, switching identification module and intelligent error correction learning module.The application has the advantages of diversified diet information and the database can be updated in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of classification recognition, in particular to a recognition control system for realizing dish individualization based on a big data environment. BACKGROUND

[0002] With the deep integration of Internet technology and catering, more and more intelligent restaurant modes have emerged, and the intelligent generation, distribution and sales of the catering industry have been promoted. The existing intelligent restaurant dish selection is often single, which means that it cannot meet the taste of consumers to a large extent, or it can only randomly choose popular food that does not meet the health and dietary needs of the individual, thereby causing consumer distress. Whether it can develop dishes that include various regions and countries and truly serve consumers is a technical problem that needs to be solved in this field in recent years.

[0003] At present, the image information captured by image recognition cannot provide real-time necessary information feedback to managers, or cannot perform real-time updating of information when an error occurs. For example, a robot bionic hand grabbing method disclosed in Chinese patent documents, with publication number CN113942009A, only obtains the soft and hard attribute data of the target object based on the tactile information and visual information of the target object, and adjusts the tactile grabbing parameters of the robot bionic hand based on the soft and hard attribute data of the target object and continuously grabs, without an effective real-time correction system to realize real-time updating monitoring and alarm mechanism for abnormal behavior. SUMMARY

[0004] The present application aims to solve the problems of single restaurant food and database that cannot be updated in real time in the prior art, and provides a recognition control system and method for realizing dish individualization based on a big data environment, which has the advantages of diversified food information and real-time updating and correction of the database. Another purpose is to improve the efficiency of food recognition and quickly analyze the required force of the captured object.

[0005] The technical scheme adopted by the present application to solve the above technical problems is a recognition control system for realizing dish individualization based on a big data environment, comprising: an information collection and analysis module connected with a distance and touch judgment module, which analyzes and collects information required for dish cooking and selects and determines required dishes; the distance and touch judgment module is connected with the touch analysis module, which judges the distance between food and a mechanical hand; the touch analysis module is connected with a switching recognition module, which analyzes the softness and hardness of food and the required grabbing strength; the switching recognition module is connected with an intelligent error correction module, which determines different food types in different scenarios; and the intelligent error correction learning module performs intelligent error correction and alarm in the case of recognition error and updates the database in real time. The present application solves the problems of single restaurant food and the inability of the database to update and alarm in real time in the prior art, and has the advantages of diversified food information and the ability of the database to update and correct in real time.

[0006] As a preferred, the information collection and analysis module comprises: a collection unit, which collects information required for dish cooking through globalization and regionalization analysis in different countries and regions, and compiles a dish cooking database; a market evaluation unit, which collects basic information, preference degree and audience degree of all dishes, wherein the basic information includes specific dish raw material information and raw material quantity information, and the audience degree includes evaluation and determination of required dish raw material inventory quantity; an intelligent screening unit, which screens required menus of specific restaurants, and determines required dishes and quantity through the selected menu of the market evaluation unit; and a dish structure building unit, which obtains multi-level structure and grade determination system of required food through the dish cooking database. Information collection and analysis can further improve the diversification of dish information.

[0007] As a preferred, the distance and touch judgment module comprises: a measurement and judgment unit connected with the dish structure building unit, which measures the position of the food to be grabbed and judges whether the distance of the food to be grabbed can be measured for touch sensitivity; and a folding and stretching unit, which increases the grabbing distance when the food to be grabbed is too long. The object position is changed.

[0008] As a preferred, the touch analysis module comprises: a flexible body sensing unit, which judges the softness and hardness of the surface of the food to be measured, selects different schemes according to the measurement results, and switches scenes; and a conveying unit, which conveys the food to be conveyed after the flexible body sensing unit completes recognition. The touch analysis module can quickly analyze the required strength of the grabbed object.

[0009] As a preferred, the switching recognition module comprises: a PLC touch point coding recognition unit, which starts the conveying unit to convey the food to be measured to the PLC touch point coding area for texture grade and surface curve analysis after the flexible body sensing unit completes judgment; and a 3D visual recognition unit, which directly performs visual recognition after the flexible body sensing unit completes judgment. The switching recognition can improve the recognition efficiency.

[0010] As preferred, the intelligent error correction learning module comprises: a database error correction unit for determining whether the database is corrected or updated when the feature data and the original data deviate greatly; and a mechanical fault error correction unit for confirming mechanical fault when the database has no error but accurate information cannot be recognized. The intelligent error correction learning module can update and correct the database in real time according to different information.

[0011] The present application has the advantages that: the present application establishes a dish information database by collecting and comparing regional information, effectively solves the problem of single food in the existing intelligent restaurant, selects and recognizes food types according to the touch detection technology, improves the efficiency of recognizing the to-be-grasped object, solves the problem that the original mechanical hand cannot guarantee that the to-be-grasped object is not damaged, determines whether the database needs to be updated or corrected by comparing and analyzing the to-be-grasped object feature curve and the database, solves the problem that the original database information cannot be updated and corrected, has the advantages of diversified food information, fast food grasping and less food damage, and real-time database updating, the present application diversifies and recognizes food, solves the problems of single food and uncorrectable database in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0013] The present application will be further described below in conjunction with the drawings and embodiments.

[0014] Figure 1 The present application is a kind of based on big data environment realizes dish individualization's identification control system overall block diagram. DETAILED DESCRIPTION

[0015] The specific implementation of the technical solutions of the present application will be further described below by embodiments and in conjunction with the drawings.

[0016] The present application is a kind of based on big data environment realizes dish individualization's identification control system, reference Figure 1As shown, including information collection and analysis module, with the distance from the judgment module is connected, regional analysis of the information needed for cooking dishes, through the selected menu screening to determine the required dishes; distance from the judgment module, with the touch of the judgment module is connected, determine the distance between the food and the robot length and the need to stretch the distance; touch analysis module, with the switching recognition module is connected, analysis of the food soft and hard degree and the required food corresponding to the grabbing strength; switching recognition module, with the intelligent error correction module is connected, the robot can determine different food types in different scenarios; intelligent error correction learning module, in the case of identification error, intelligent error correction alarm, and real-time training updates the food information database. The information collection and analysis module comprises: S1: acquisition unit, with the market assessment unit is connected, different countries and regions through globalization and regional analysis of the information needed for cooking dishes, preparation of dishes cooking database; S2: market assessment unit, with the intelligent screening unit is connected, all the basic information of dishes, the degree of preference and the degree of audience, wherein the basic information includes specific dish raw material information and raw material quantity information, the degree of preference includes the condition that the dishes are popular with the public, the degree of audience includes the evaluation of the required dish raw material inventory; S3: intelligent screening unit, with the dish structure building unit is connected, including screening the specific menu required by the restaurant, and then determining the required dishes and the amount according to the selected menu through the market assessment unit; S4: dish structure building unit, with the distance from the judgment module is connected, through the dish cooking database to obtain the multi-level structure of the required food and the grade determination system. The distance from the judgment module comprises: S5: measurement determination unit, connected with the dish structure building unit, including determining the position of the food to be grabbed, and determining whether the distance of the food to be grabbed can be measured for touch sensitivity; S6: folding and stretching unit, connected with the measurement determination unit and the touch analysis module, including the folding and stretching unit increasing the grabbing distance when the food to be grabbed is too long. The touch analysis module comprises: S7: flexible body perception unit, connected with the folding and stretching unit, including determining the soft and hard degree of the surface of the food to be measured, selecting different schemes according to the measurement results, and switching scenes; S8: conveying unit, connected with the flexible body perception unit and the switching recognition module, including conveying the food to be conveyed after the flexible body perception unit completes the identification. The switching recognition module comprises: S9: PLC touch point coding recognition unit, connected with the touch analysis module and the intelligent error correction learning module, after the flexible body perception unit completes the determination, the conveying unit is started to convey the food to be measured to the PLC touch point coding area for texture grade and surface curve analysis; S10: 3D visual recognition unit, connected with the touch analysis module and the intelligent error correction learning module, after the flexible body perception unit completes the determination, directly performing visual recognition.The intelligent error correction learning module comprises: S11: a database error correction unit, connected with the switching identification module, comprising determining whether the database is corrected or updated when the characteristic data and the original data deviate greatly; S12: a mechanical fault error correction unit, connected with the database error correction unit, comprising mechanical fault confirmation when the database has no error but accurate information cannot be identified. The intelligent error correction learning module is connected with the cloud server through an SSH client, and after the connection, the data is updated and corrected in real time.

[0017] In addition to the above embodiments, within the scope disclosed in the claims and the specification of the present application, the technical features or technical data of the present application can be reselected and combined to form new embodiments, which can be realized by those skilled in the art without creative labor, and therefore these embodiments of the present application which are not described in detail should be regarded as specific embodiments of the present application and within the protection scope of the present application.

Claims

1. A recognition control system for realizing dish personalization based on a big data environment, characterized in that, Comprise: Information collection and analysis module, connected with the distance feeling judgment module, analyzes the information required for dish cooking, and screens and determines the required dishes; Distance feeling judgment module, connected with the touch feeling judgment module, judges the distance between food and robot; Touch feeling analysis module, connected with the switching recognition module, analyzes the softness of food and the required grabbing force, including flexible body sensing unit, including determining the softness of the surface of the food to be measured, selecting different schemes according to the measurement results, and switching scenes; Switching recognition module, connected with intelligent error correction module, determines different food types in different scenes, including: PLC touch point coding recognition unit, after the completion of the determination by the flexible body sensing unit, the food to be measured is transported to the PLC touch point coding area for analysis; 3D visual recognition unit, after the completion of the determination by the flexible body sensing unit, directly performs visual recognition; Intelligent error correction learning module, in the case of identification error, intelligent error correction alarm is given, and the database is updated in real time. 2.The dish individualization recognition control system based on a big data environment according to claim 1, wherein, The information collection and analysis module comprises: S1: collection unit, connected with market evaluation unit, different countries and regions are analyzed through globalization and regionalization to collect information required for dish cooking, and dish cooking database is prepared; S2: market evaluation unit, connected with intelligent screening unit, collects basic information, preference degree and audience degree of all dishes, wherein the basic information includes specific dish raw material information and raw material quantity information, and the audience degree includes evaluation and determination of required dish raw material storage quantity; S3: intelligent screening unit, connected with dish structure building unit, including screening required menu of specific restaurant, and determining required dishes and quantity through the menu selected by the market evaluation unit; S4: dish structure building unit, connected with the distance feeling judgment module, obtains multi-level structure and grade determination system of required food through the dish cooking database. 3.The dish individualization recognition control system based on a big data environment according to claim 2, characterized in that, The distance feeling judgment module comprises: S5: measurement determination unit, connected with the dish structure building unit, including determining the position of the food to be grabbed, and judging whether the distance of the food to be grabbed can be measured for touch feeling; S6: folding and stretching unit, connected with the measurement determination unit and the touch feeling analysis module, including increasing the grabbing distance when the food to be grabbed is too long. 4.The dish individualization recognition control system based on a big data environment according to claim 3, wherein, The touch feeling analysis module comprises: S7: flexible body sensing unit, connected with the folding and stretching unit; S8: conveying unit, connected with the flexible body sensing unit and the switching recognition module, including conveying food after the completion of the identification by the flexible body sensing unit. 5.The dish individualization recognition control system based on a big data environment according to claim 4, wherein, The switching recognition module comprises: S9: PLC touch point coding recognition unit, connected with the touch feeling analysis module and intelligent error correction learning module; S10: 3D visual recognition unit, connected with the touch feeling analysis module and intelligent error correction learning module. 6.The dish personalization recognition control system based on a big data environment according to claim 5, wherein, The PLC touch point coding recognition module analyzes the data as texture level and surface curve. 7.The dish personalization recognition control system based on a big data environment according to claim 5, wherein, The intelligent error correction learning module comprises: S11: the database error correction unit, connected with the switching identification module, including determining whether the database corrects or updates when the deviation between the feature data and the original data is large; S12: the mechanical fault error correction unit, connected with the database error correction unit, including mechanical fault confirmation when the database has no error but accurate information is not recognized. 8.The dish personalization recognition control system based on a big data environment according to claim 1 or 5 or 7, characterized in that, The intelligent error correction learning module is connected with the cloud server through an SSH client.

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