CN112861905B - Tree species classification platform based on internet - Google Patents
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Abstract
The invention discloses an internet-based tree species classification platform, which comprises a client and a tree species classification platform, wherein data interaction is realized between the client and the tree species classification platform through the internet, the client comprises an information transmission module and an information display module, the information transmission module sends information, the information display module feeds back results, the tree species classification platform comprises a user management module, an information collection module, an analysis comparison module, a learning storage module, an identification storage module, an integration management module and an information sending module, the user management module manages user information, the information collection module collects and classifies information, the comparison analysis module analyzes the information, the learning storage module and the identification storage module store the information, the integration management module carries out integration management, and the information sending module is used for sending information. The tree species classification platform is high in analysis efficiency, high in user use activity and convenient for tree classification standard data to be rapidly increased.
Description
Technical Field
The invention mainly relates to the technical field of tree species classification, in particular to a tree species classification platform based on the Internet.
Background
The trees on the earth are various in variety, people need to classify the tree types in order to fully know the trees, and the tree classification platform is built by using the internet technology so as to be convenient for people to accurately classify the trees.
According to the tree classification method and system based on deep learning provided in patent document with application number of CN201711272966.0, the product comprises the steps of collecting sample images for labeling, constructing a data set by using the labeled sample images and preprocessing, extracting image features from the preprocessed image data set by using a convolutional neural network, acquiring a feature vector set, and training and verifying a thirty classifier by using the acquired feature vector set. And finally, collecting leaf image data of the tree species to be tested, performing preprocessing operation, extracting feature vectors, and judging and classifying the extracted leaf image feature vectors by using a trained thirty classifier to realize automatic classification of the tree species. The method solves the problem that the existing tree classification method cannot completely reflect the outline characteristics of tree leaves, so that the result is inaccurate, and ensures the accuracy of tree classification.
The accuracy of tree classification is guaranteed to the product in above-mentioned patent, but analysis efficiency is lower and there is the condition that tree classification standard data increase slowly in the database, therefore need to design a kind of analysis efficiency high, and the user uses the aggressiveness height, is convenient for tree classification standard data rapid increase's tree classification platform.
Disclosure of Invention
The invention mainly provides a tree classification platform based on the Internet, which is used for solving the technical problems in the background technology.
The technical scheme adopted for solving the technical problems is as follows:
the utility model provides a tree species classifying platform based on internet, includes customer end and tree species classifying platform, the customer end with realize data interaction through the internet between the tree species classifying platform, the customer end includes information transmission module and information display module, information transmission module is used for the user to send information to tree species classifying platform, information display module is with the feedback result after showing tree species classifying platform carries out analysis processing to user information, tree species classifying platform includes user management module, information collection module, analysis contrast module, study storage module, discernment storage module, point management module and information transmission module, user management module is used for managing user information and provides user registration login function, information collection module carries out the classification and carries out analysis contrast module with the information that the user sent, the information collection module is divided into two types, is study information and identification information respectively, comparison analysis module can carry out analysis discernment and compare it with the tree classification standard template of discernment storage module with the information after analysis processing, and the relevant tree classification standard template of identification information is waited for setting up as the relevant tree classification template of the discernment information, and is used for carrying out the analysis module and is used for drawing the point information after the analysis module is used for drawing the relevant point information to log-in the module and carries out the analysis log-on the relevant information, the analysis module is used for drawing the point information and the relevant information of the module and the information is used for carrying out the analysis module and the relevant information and the analysis module is used for drawing and the information.
Preferably, the information transmission module comprises a knowledge sharing unit and a classification recognition unit, wherein a user sends information to the tree classification platform in a form of tree names and related pictures through the knowledge sharing unit, the user earns points through knowledge sharing, and the user can edit the pictures or text information through the classification recognition unit and then can view the related information through the information display module after sending the pictures or text information to the tree classification platform. In the preferred embodiment, the user can upload tree species and related pictures of the tree species conveniently through the knowledge sharing unit, the tree classification standard templates in the tree species classification platform can be increased conveniently, and the user can inquire tree species information conveniently through the classification recognition unit.
Preferably, the information transmission module comprises an answering unit, the user can send tree species inquiry information to the point management module in the form of rewarding points through the answering unit, and the user can answer and earn points through the answering unit. In the preferred embodiment, the answering unit is used for facilitating the user to inquire about the network questions when the tree classification platform cannot answer the tree types.
Preferably, the information receiving module comprises an identification unit and a learning unit, the learning unit is used for receiving the information sent by the knowledge sharing unit and transmitting the information to the analysis and comparison module, the analysis and comparison module analyzes and processes the information through a learning method, the identification unit is used for receiving the information sent by the classification and identification unit and transmitting the information to the analysis and comparison module, and the analysis and comparison module analyzes and processes the information through the identification method. In the preferred embodiment, the classification of the information uploaded by the user is facilitated by the authentication unit and the learning unit.
Preferably, the analysis and comparison module comprises a character recognition unit and an image recognition unit, wherein the character recognition unit processes information by using a character recognition technology, and the image recognition unit processes information by using an image recognition technology. In the preferred embodiment, the analysis and comparison module is convenient for carrying out character recognition on the information uploaded by the user and image recognition is convenient for comparing the generated data with the tree classification standard template.
Preferably, the identification storage module comprises a word unit, a first-level gallery unit, a second-level gallery unit, a third-level gallery unit and a fourth-level gallery unit, each word unit is provided with the first-level gallery unit, the second-level gallery unit, the third-level gallery unit and the fourth-level gallery unit corresponding to the word unit, tree species names are stored in the word unit, and tree species pictures corresponding to the tree species names are stored in the first-level gallery unit, the second-level gallery unit, the third-level gallery unit and the fourth-level gallery unit. In the preferred embodiment, the text unit is designed to correspond to the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit so as to facilitate rapid image comparison analysis.
Preferably, the learning method is to perform image recognition and text recognition on information sent by a user by an analysis and comparison module and compare the information with information stored in an identification storage module by a text comparison and image analysis method, wherein the five conditions are respectively that firstly, text is matched and image is matched, at the moment, pictures are stored in the identification storage module and are rewarded for points according to the point exchange rule, secondly, text is matched and image is not matched, at the moment, pictures are stored in the learning storage module and rewarded for points according to the point exchange rule, whether the number of pictures reaches a set value or not is detected, when the number of pictures reaches the set value, the error is not reached, the information is ignored, thirdly, text is not matched, the text information is compared with the information stored in the learning storage module, the text is not matched, at the moment, the text and the pictures are stored in the learning storage module and are rewarded for points according to the point exchange rule, fourth, the characters are not matched, the character information is compared with the storage information of the learning storage module, the characters are matched, the image information is compared with the storage information of the learning storage module, the images are not matched, at the moment, the images are stored in the learning storage module, point rewards are carried out according to point exchange rules, whether the number of the images reaches a set value is detected, if the number of the images reaches the set value, the images are ignored if the number of the images does not reach the set value, fifth, the characters are not matched, the character information is compared with the storage information of the learning storage module, the characters are matched, the image information is compared with the storage information of the learning storage module, the images are matched, at the moment, the images are stored in the learning storage module, point rewards are carried out according to the point exchange rules, whether the number of the images reaches the set value is detected, and if the number of the images does not reach the set value, the information is ignored if the number of the images does not reach the set value. In the preferred embodiment, the tree classification standard template can be conveniently and efficiently generated by the tree classification platform through a learning method, and the tree classification standard template can be continuously optimized.
Preferably, the authentication method is that the analysis comparison module performs image recognition and text recognition on the information sent by the user, and compares the information with the information stored in the recognition storage module through text comparison and image analysis, and the two cases are respectively that firstly, related information is queried, at the moment, the information sending module extracts the related information and sends the related information to the client, secondly, the related information is not queried, no result is sent to the client, and the user is prompted to set point question and answer. In the preferred embodiment, the user is facilitated to quickly query tree species information by authentication.
Preferably, the image analysis method is to record a first-stage gallery unit with similar image and a first-stage gallery unit picture as a feature one component, record a second-stage gallery unit picture with similar image and a second-stage gallery unit picture as a feature two component, record a third-stage gallery unit picture with similar image and a third-stage gallery unit picture as a feature three component, and record a fourth-stage gallery unit picture with similar image and a third-stage gallery unit picture as a related picture. In the preferred embodiment, the user image is conveniently and quickly compared with the images in the gallery unit by an image analysis method.
Preferably, the point management module comprises a point exchange unit and a point questioning and answering unit, wherein the point exchange unit is used for point management and point exchange of the user, and the point questioning and answering unit is used for managing inquiry information issued by the user and point deduction or rewarding. In the preferred embodiment, the stable management of user points is facilitated by the point management module.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, a user can enjoy the point service after registering and logging in, the point can be used for exchanging and rewarding and asking, the user can share knowledge and earn the point through the client, the user can inquire the name of tree species or inquiry of tree species pictures through the client, the user can inquire tree species in the form of rewarding and asking for the points through the client, the information uploaded by the user can be conveniently classified through the identification unit and the learning unit in the tree species classification platform, the character recognition and the image recognition of the information uploaded by the user can be conveniently carried out through the analysis and comparison module, the generated data can be conveniently compared with the tree species classification standard template, the image comparison analysis can be conveniently and rapidly carried out through the design corresponding to the character unit, the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit, the tree species classification platform can be conveniently learned or identified through the analysis and comparison module, the tree species classification standard template can be conveniently and effectively generated through the learning method, the tree classification standard template can be continuously optimized, and the user can conveniently and rapidly inquired the tree species information through the identification method.
The invention will be explained in detail below with reference to the drawings and specific embodiments.
Drawings
FIG. 1 is a diagram of the overall structure of the present invention;
FIG. 2 is a flow chart of the learning method of the present invention;
FIG. 3 is a flow chart of an authentication method according to the present invention;
FIG. 4 is a flow chart of an image analysis method of the present invention.
Detailed Description
In order that the invention may be more fully understood, a more particular description of the invention will be rendered by reference to the appended drawings, in which several embodiments of the invention are illustrated, but which may be embodied in different forms and are not limited to the embodiments described herein, which are, on the contrary, provided to provide a more thorough and complete disclosure of the invention.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may be present, and when an element is referred to as being "connected" to the other element, it may be directly connected to the other element or intervening elements may also be present, the terms "vertical", "horizontal", "left", "right" and the like are used herein for the purpose of illustration only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly connected to one of ordinary skill in the art to which this invention belongs, and the knowledge of terms used in the description of this invention herein for the purpose of describing particular embodiments is not intended to limit the invention, and the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Examples
Example 1
Referring to fig. 1, 2 and 4, an internet-based tree classification platform includes a client and a tree classification platform, wherein the client and the tree classification platform implement data interaction through the internet, the client includes an information transmission module and an information display module, the information transmission module is used for transmitting information to the tree classification platform by a user, the information display module is used for displaying feedback results after analyzing the user information by the tree classification platform, the tree classification platform includes a user management module, an information collection module, an analysis comparison module, a learning storage module, an identification storage module, an integration management module and an information transmission module, the user management module is used for managing user information and providing a user registration login function, the information collection module classifies the information transmitted by the client and transmits the information to an analysis comparison module, the information collection module classifies the information transmitted by the user into two categories, namely learning information and identification information, the comparison analysis module can analyze and compare the identification information with tree classification standards stored in the identification storage module, find out tree classification standards related to the tree classification information to be stored in the identification storage module, the tree classification module is used for extracting relevant tree classification information by the comparison module and extracting information to be used for extracting relevant tree classification information by the comparison module, the comparison module is used for extracting relevant information from the classification module and transmitting information to the classification module, the user sends information to the tree classification platform in the form of tree names and related pictures through the knowledge sharing unit, the user earns points through the knowledge sharing, the user can edit the pictures or text information through the classification recognition unit and can check the related information through the information display module after sending the pictures or text information to the tree classification platform, the information receiving module comprises an identification unit and a learning unit, the learning unit is used for receiving the information sent by the knowledge sharing unit and transmitting the information to the analysis comparison module, the analysis comparison module is used for analyzing and processing the information through a learning method, the identification unit is used for receiving the information sent by the classification recognition unit and transmitting the information to the analysis comparison module, the analysis comparison module is used for analyzing and processing the information through the identification method, the analysis comparison module comprises a text recognition unit and an image recognition unit, the character recognition unit processes information by using a character recognition technology, the image recognition unit processes information by using an image recognition technology, the recognition storage module comprises a character unit, a primary gallery unit, a secondary gallery unit, a tertiary gallery unit and a quaternary gallery unit, each character unit is provided with the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit corresponding to the character unit, tree species names are stored in the character unit, the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit store tree species pictures corresponding to the tree species names, the image analysis method is to record the primary gallery unit with the images similar to the pictures in the primary gallery unit as a characteristic component, the method comprises the steps of marking a feature first component with similar feature in a feature first component with a feature second component, marking a feature second component with similar feature in a feature third component with a feature second component with similar feature third component in a feature third component, marking a feature third component with similar feature in a feature third component with a feature fourth component, comparing the information sent by a user with the information sent by an analysis contrast module through a character comparison and image analysis method with information stored in an identification storage module through a character comparison and image analysis method, comparing the information with the information stored in the identification storage module, wherein the five conditions are respectively that first character matching and image matching are achieved, the picture is stored in the identification storage module and is awarded according to a point exchange rule, second character matching and image mismatching are achieved, the picture is stored in the learning storage module and is awarded according to the point exchange rule, if the number of the picture reaches a set value, the situation is not reached, third character mismatching is ignored, the character information is compared with the stored in the learning storage module, the character mismatching is stored with the information stored in the learning storage module, and the point information is stored according to the set value when the mismatching is not reached, the number of the picture is mismatching, and (3) character matching, namely comparing the image information with the information stored in the learning storage module, and performing image matching, wherein at the moment, the pictures are stored in the learning storage module, point rewards are performed according to a point exchange rule, whether the number of the pictures reaches a set value is detected, and if the number of the pictures reaches the set value, the information is stored in the identification storage module, and if the number of the pictures does not reach the set value, the information is ignored. After a user logs in through a client, user information is transmitted to a user management module and personal information of the user is generated, when the user performs knowledge sharing, the user sends information to a tree classification platform in a form of adding related pictures such as tree names, tree integral pictures, tree surface pictures, tree leaf pictures and tree fruit pictures to the tree classification platform, an information receiving module in the tree classification platform receives the information and classifies the information to a learning unit, the learning unit transmits the information to an analysis comparison module, the analysis comparison module analyzes the information through a learning method, the analysis comparison module performs image recognition and text recognition on the information sent by the user and compares the information with information stored in an identification storage module through a text comparison and image analysis method, the information is subjected to text matching and image matching, the pictures are stored in the identification storage module according to a point value matching rule, the pictures are stored in the identification storage module and are subjected to point rewards according to the point value matching, the character matching and image matching, the pictures are stored in the identification storage module and are subjected to point value matching, the character matching and the image matching are not matched, the pictures are stored in the learning storage module and the point value is converted according to the point value, if the point matching rule is reached, the error is not reached, the character matching is ignored, the character matching is not matched, the character matching is stored with the learning storage module, the point is stored in the storage module, the point matching is compared with the point matching information, and the point is stored in the point matching storage module, and the point is not matched with the point matching information, when the number of the pictures is reached, the information is stored in the identification storage module, and is ignored, the image analysis method is that a first-level gallery unit with similar image and first-level gallery unit pictures is marked as a feature one component, a second-level gallery unit picture in the first-level gallery unit picture is marked as a feature two component, a third-level gallery unit picture in the second-level gallery unit picture is marked as a feature three component, and a fourth-level gallery unit picture in the third-level gallery unit picture is marked as a related picture.
Example 2
Referring to fig. 1, 3 and 4, an internet-based tree classification platform includes a client and a tree classification platform, wherein the client and the tree classification platform implement data interaction through the internet, the client includes an information transmission module and an information display module, the information transmission module is used for transmitting information to the tree classification platform by a user, the information display module is used for displaying feedback results after analyzing the user information by the tree classification platform, the tree classification platform includes a user management module, an information collection module, an analysis comparison module, a learning storage module, an identification storage module, an integration management module and an information transmission module, the user management module is used for managing user information and providing a user registration login function, the information collection module classifies the information transmitted by the client and transmits the information to an analysis comparison module, the information collection module classifies the information transmitted by the user into two categories, namely learning information and identification information, the comparison analysis module can analyze and compare the identification information with tree classification standards stored in the identification storage module, find out tree classification standards related to the tree classification information to be stored in the identification storage module, the tree classification module is used for extracting relevant tree classification information by the comparison module and extracting information to be used for extracting relevant tree classification information by the comparison module, the comparison module is used for extracting relevant information from the classification module and transmitting information to the classification module, the user sends information to the tree classification platform in the form of tree names and related pictures through the knowledge sharing unit, the user earns points through the knowledge sharing, the user can edit the pictures or text information through the classification recognition unit and can check the related information through the information display module after sending the pictures or text information to the tree classification platform, the information receiving module comprises an identification unit and a learning unit, the learning unit is used for receiving the information sent by the knowledge sharing unit and transmitting the information to the analysis comparison module, the analysis comparison module is used for analyzing and processing the information through a learning method, the identification unit is used for receiving the information sent by the classification recognition unit and transmitting the information to the analysis comparison module, the analysis comparison module is used for analyzing and processing the information through the identification method, the analysis comparison module comprises a text recognition unit and an image recognition unit, the character recognition unit processes information by using a character recognition technology, the image recognition unit processes information by using an image recognition technology, the recognition storage module comprises a character unit, a primary gallery unit, a secondary gallery unit, a tertiary gallery unit and a quaternary gallery unit, each character unit is provided with the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit corresponding to the character unit, tree species names are stored in the character unit, the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit store tree species pictures corresponding to the tree species names, the image analysis method is to record the primary gallery unit with the images similar to the pictures in the primary gallery unit as a characteristic component, the method comprises the steps of marking a feature first component with similar comparison selection features in an image and feature first component, marking a feature second component with similar comparison selection features in an image and feature second component and feature third component, marking a feature third component with similar comparison selection features in an image and feature third component, marking an image and feature third component with similar comparison selection features in a fourth-level image and feature third component as a related image, wherein the identification method comprises the steps of carrying out image identification and character identification on information sent by a user by an analysis comparison module, comparing the information with information stored in an identification storage module by a character comparison and image analysis method, dividing the information into two cases respectively, inquiring related information, extracting related information by an information sending module at the moment, sending the related information to a client without inquiring the related information, sending a non-result to the client and prompting the user to set point questions and answers. The method comprises the steps that when a user inquires tree species, characters, such as tree species names and the like, can be edited for information inquiry, pictures, such as tree species integral pictures, tree species epidermis pictures, tree species leaf pictures, tree species fruit pictures and the like, can be edited for information inquiry, an information receiving module in a tree species classification platform receives information and classifies the information into an identification unit, the identification unit transmits the information to an analysis comparison module, the analysis comparison module carries out image recognition and character recognition on the information sent by the user and compares the information with information stored in an identification storage module through a character comparison and image analysis method, the two cases are respectively that related information is inquired, at the moment, an information sending module extracts related information and sends the related information to a client, the second time does not inquire related information, an unobtrusive result is sent to the client, the user can be set with integral question answering, when the image analysis is carried out, the image analysis method is that a first-stage gallery unit which compares images with pictures in a first-stage gallery unit and selects similar features is recorded as a first-stage feature group, the image is compared with second-stage gallery unit pictures in the first-stage gallery unit, the image is compared with the second-stage gallery unit with the images in the first-stage gallery unit, at the moment, the image is compared with the third-stage gallery unit is compared with the image with the third-stage gallery unit to select similar features.
Example 3
Referring to fig. 1, an internet-based tree classification platform includes a client and a tree classification platform, wherein the client and the tree classification platform implement data interaction through the internet, the client includes an information transmission module and an information display module, the information transmission module is used for transmitting information to the tree classification platform by a user, the information display module uses the displayed tree classification platform to analyze the feedback result after the user information is processed, the tree classification platform includes a user management module, an information collection module, an analysis comparison module, a learning storage module, an identification storage module, an integral management module and an information transmission module, the user management module is used for managing user information and providing a user registration login function, the information collection module classifies the information transmitted by the client and transmits the information to an analysis comparison module, the information collection module classifies the information transmitted by the user into two types, namely learning information and identification information, the comparison analysis module can analyze the identification information and compare the identification information with tree classification templates stored in the identification storage module, find out trees related to the identification information, the tree classification template is set as a relevant tree classification template, the comparison module can extract the relevant tree classification information from the tree classification module and store the relevant tree classification information after the comparison module is used for extracting the relevant tree classification information, the relevant information is extracted by the comparison module and the analysis module is used for extracting the relevant tree classification module, the user can send tree species inquiry information to the point management module in the form of rewarding points through the answering unit, and the user can answer and earn points through the answering unit, the point management module comprises a point exchange unit and a point inquiring unit, the point exchange unit is used for point management and point exchange of the user, and the point inquiring unit is used for managing the issued inquiry information and point deduction or rewarding of the user. The user can display personal account number point conditions after logging in through the client, the point can be exchanged for goods in the point exchanging unit, the user can also use the answering unit to set tree species inquiry and pay the point, the point is deducted from the account of the paying user after paying, and the answering user can answer in the answering unit and obtain corresponding paying point rewards after checking.
The specific flow of the invention is as follows:
after a user logs in through a client, user information is transmitted to a user management module and personal information of the user is generated, when the user performs knowledge sharing, the user sends information to a tree classification platform in a form of adding related pictures such as tree names, tree integral pictures, tree surface pictures, tree leaf pictures and tree fruit pictures to the tree classification platform, an information receiving module in the tree classification platform receives the information and classifies the information to a learning unit, the learning unit transmits the information to an analysis comparison module, the analysis comparison module analyzes the information through a learning method, the analysis comparison module performs image recognition and text recognition on the information sent by the user and compares the information with information stored in an identification storage module through a text comparison and image analysis method, the information is subjected to text matching and image matching, the pictures are stored in the identification storage module according to a point value matching rule, the pictures are stored in the identification storage module and are subjected to point rewards according to the point value matching, the character matching and image matching, the pictures are stored in the identification storage module and are subjected to point value matching, the character matching and the image matching are not matched, the pictures are stored in the learning storage module and the point value is converted according to the point value, if the point matching rule is reached, the error is not reached, the character matching is ignored, the character matching is not matched, the character matching is stored with the learning storage module, the point is stored in the storage module, the point matching is compared with the point matching information, and the point is stored in the point matching storage module, and the point is not matched with the point matching information, when the information is reached, the error reporting is ignored, five characters are not matched, the character information is compared with the information stored in the learning storage module, the character matching is carried out, the image information is compared with the information stored in the learning storage module, the image matching is carried out, at the moment, the picture is stored in the learning storage module, the integral rewarding is carried out according to the integral exchange rule, whether the number of the pictures reaches the set value is detected, when the information is reached, the information is stored in the recognition storage module and is not reached, the error reporting information is ignored, a manager can process the error reporting information regularly, the user can edit characters, such as tree names, carry out information inquiry, can edit pictures, such as tree integral pictures, tree skin pictures, tree leaf pictures, tree fruit pictures and the like, carry out information inquiry, the information receiving module in the tree classification platform receives the information and classifies the information into the identification unit, the identification unit transmits information to the analysis and comparison module, the analysis and comparison module carries out image recognition and text recognition on the information sent by the user and compares the information with the information stored in the recognition and storage module through text comparison and an image analysis method, the analysis and comparison is divided into two cases, namely, firstly, related information is queried, at the moment, the information sending module extracts related information such as tree seed names, tree seed integral pictures, tree seed epidermis pictures, tree seed leaf pictures and tree seed fruit pictures and sends the related information to the client, secondly, related information is not queried, no result is sent to the client and prompts the user to set integral questions and answers, the image analysis method is used for comparing and selecting a first-stage picture library unit with similar characteristics with pictures in the first-stage picture library unit as a component of characteristics during image analysis, the method comprises the steps of marking a characteristic first component with similar contrast selection characteristics in a second-level gallery unit picture in the image and the characteristic first component as a characteristic second component, marking a characteristic second component with similar contrast selection characteristics in a third-level gallery unit picture in the image and the characteristic second component as a characteristic third component, marking a characteristic third component with similar contrast selection characteristics in a fourth-level gallery unit picture in the image and the characteristic third component as a related picture, displaying personal account integral conditions after a user logs in through a client, carrying out commodity exchange in an integral exchange unit, setting tree species inquiry and rewarding integral by a user, deducting the integral from an account of a rewarding user after the rewarding, and carrying out answering in the answering unit and obtaining corresponding rewarding integral rewarding after the user passes the auditing.
While the invention has been described above with reference to the accompanying drawings, it will be apparent that the invention is not limited to the embodiments described above, but is intended to be within the scope of the invention, as long as such insubstantial modifications are made by the method concepts and technical solutions of the invention, or the concepts and technical solutions of the invention are applied directly to other occasions without any modifications.
Claims (7)
1. The tree classification platform based on the Internet comprises a client and a tree classification platform, and is characterized in that data interaction is realized between the client and the tree classification platform through the Internet, the client comprises an information transmission module and an information display module, the information transmission module is used for transmitting information to the tree classification platform by a user, the information display module is used for displaying feedback results of analysis processing of the user information by the tree classification platform, the tree classification platform comprises a user management module, an information collection module, an analysis comparison module, a learning storage module, an identification storage module, an integral management module and an information transmission module, the user management module is used for managing the user information and providing a user registration sharing function, the information collection module is used for classifying the information transmitted by the client and transmitting the information to the analysis comparison module, the information collection module is used for classifying the information transmitted by the user into two types, namely learning information and identification information, the information transmission module comprises a knowledge unit and a classification identification unit, the user can transmit information to the tree classification platform in a form of tree classification by adding related pictures through the tree names by the knowledge unit, and can view the related pictures by the user through the classification information collection module, and can be obtained by editing the information through the classification and display module;
the information collection module comprises an identification unit and a learning unit, the learning unit is used for receiving the information sent by the knowledge sharing unit and transmitting the information to the analysis and comparison module, the analysis and comparison module analyzes and processes the information through a learning method, the identification unit is used for receiving the information sent by the classification and identification unit and transmitting the information to the analysis and comparison module, and the analysis and comparison module analyzes and processes the information through the identification method;
the analysis and comparison module can analyze and identify the identification information and compare the identification information with the tree classification standard template stored in the identification storage module, find out the tree classification standard template related to the identification information and set the tree classification standard template as information to be extracted, the analysis and comparison module can store the related information in the learning storage module after comparing the learning information with the tree classification standard template of the identification storage module, the learning method is used for carrying out image identification and text recognition on the information sent by the user by the analysis and comparison module and comparing the information with the information stored in the identification storage module by a text comparison and image analysis method, the situation is divided into five cases, namely, firstly, text matching and image matching, and then storing pictures in the identification storage module and carrying out point rewarding according to a point exchange rule, secondly, text matching and image mismatching, and then storing pictures in the learning storage module and carrying out point rewarding according to the point exchange rule, and detecting whether the number of pictures reaches a set value, if the number of the pictures is not reached, and thirdly, text mismatching is ignored, the text is not matched, the text information is compared with the learning storage module and the stored in the learning storage module and the point exchange rule, and the number of the point is stored in the point store, and the point rewarding is compared with the learning storage module, and the number is not reached, and the point is compared with the point information stored by the point information, and the point matching is stored by the point information, and the point information is stored by the point information when the point matching is not matching and the point information is compared with the point information and the point information is stored, the method comprises the steps of text matching, comparing image information with information stored in a learning storage module, performing image matching, storing pictures in the learning storage module, performing point rewards according to a point exchange rule, detecting whether the number of the pictures reaches a set value, and ignoring if the number of the pictures does not reach the set value;
the point management module is used for managing the points of the registered user after login, and the information sending module is used for extracting information to be extracted and transmitting the information to the information display module.
2. The internet-based tree species classification platform as claimed in claim 1, wherein the information transmission module comprises an answering unit through which a user can send tree species inquiry information to the point management module in the form of a bonus point, and through which the user can answer and earn the point.
3. The internet-based tree species classification platform of claim 1 wherein the analysis and comparison module comprises a word recognition unit that processes information using word recognition techniques and an image recognition unit that processes information using image recognition techniques.
4. The internet-based tree species classification platform as claimed in claim 3, wherein the identification storage module comprises a text unit, a primary gallery unit, a secondary gallery unit, a tertiary gallery unit and a quaternary gallery unit, each text unit has a primary gallery unit, a secondary gallery unit, a tertiary gallery unit and a quaternary gallery unit corresponding to the text unit, tree species names are stored in the text unit, and tree species pictures corresponding to the tree species names are stored in the primary gallery unit, the secondary gallery unit, the tertiary gallery unit and the quaternary gallery unit.
5. The internet-based tree classification platform according to claim 1, wherein the identification method is that the analysis comparison module performs image recognition and text recognition on the information sent by the user and compares the information stored in the recognition storage module through text comparison and image analysis, and the identification method is divided into two cases, namely, firstly, inquiring related information, then, the information sending module extracts related information and sends the related information to the client, secondly, the related information is not inquired, and the no-result is sent to the client and prompts the user to set point questions and answers.
6. The internet-based tree species classification platform of claim 5 wherein the image analysis method is characterized in that a primary gallery unit with similar image and primary gallery unit pictures with similar contrast selection features is marked as a feature group, a feature group with similar image and secondary gallery unit pictures with similar contrast selection features is marked as a feature group, a feature group with similar image and tertiary gallery unit pictures with similar contrast selection features is marked as a feature group, and a feature group with similar image and four-stage gallery unit pictures with similar contrast selection features is marked as a related picture.
7. The internet-based tree species classification platform of claim 2, wherein the point management module comprises a point redemption unit for user point management and point redemption and a point questioning and answering unit for managing user issued inquiry information and point deductions or rewards.
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