Fresh flower matching method based on big data

Abstract

The invention discloses a fresh flower collocation method based on big data. The method comprises a data acquisition module, a data analysis module, a collocation generation module, a user interaction module, a scheme display module and a data storage module. The data acquisition module is electrically connected with the data storage module, the data storage module is electrically connected with the matching generation module, the matching generation module is electrically connected with the scheme display module and the data analysis module, and the data analysis module is electrically connected with the user interaction module. According to the fresh flower matching method based on the big data, the favors of the user can be judged according to the conditions input by the user according to the fresh flowers matched through the matching method, and the thoughts which are not expressed by the user are mined through a deep mining algorithm, so that the generated scheme can better fit the thoughts of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of flower matching methods, and in particular to a flower matching method based on big data. Background Art

[0002] In today's digital age, big data technology is increasingly penetrating into various fields, including the field of flower matching. With the rapid development of information technology and the continuous improvement of people's pursuit of quality of life, the demand for flower matching has become more diversified and personalized. The emergence of big data has provided strong technical support for flower matching methods. By widely collecting and analyzing massive amounts of user preference data, flower feature data, and various scene-related data, we can gain in-depth insights into consumers' preferences for different flower combinations. By using advanced data mining and analysis algorithms, we can discover potential patterns and rules from huge data sets, such as the popularity of specific color combinations, the adaptability of different flower combinations in different occasions, etc. At the same time, big data can also combine seasonal changes, regional cultural differences and other factors to provide more accurate guidance for flower matching. Through comprehensive analysis of historical sales data and user feedback data, we can adjust and optimize flower matching plans in real time to meet the needs of different markets and user groups. In addition, intelligent algorithms can quickly process and analyze big data to achieve efficient flower matching recommendations. With the help of big data technology, we can break the limitations of traditional flower matching and bring consumers more novel, unique and suitable flower combinations, enhance the expressiveness and appeal of flower art, and meet people's pursuit of beauty and emotional expression in different scenarios. There is an urgent need for a flower matching method that can combine the needs of users through big data search and comparison to provide users with suitable flower matching plans. Summary of the invention

[0003] To achieve the above objectives, the present invention is implemented through the following technical solutions: a flower matching method based on big data, including a data acquisition module, a data analysis module, a matching generation module, a user interaction module, a solution display module and a data storage module; The data acquisition module is electrically connected to the data storage module, the data storage module is electrically connected to the collocation generation module, the collocation generation module is electrically connected to the solution display module and the data analysis module, and the data analysis module is electrically connected to the user interaction module.

[0004] Preferably, the data collection module is used to widely collect various data related to flower matching through multiple channels, including but not limited to detailed flower variety information obtained from professional flower databases, covering their Latin scientific names, common names, variety characteristics, different flower color distribution and popular trends extracted from flower market transaction records, flower shape, size, number and arrangement of petals collected using image recognition technology, various flower languages ​​and their symbolic meanings compiled from flower language books and cultural materials, and relevant data on flower matching preferences in wedding scenes, celebration scenes, and home decoration scenes.

[0005] Preferably, the data analysis module uses big data analysis algorithms such as cluster analysis and association rule mining to deeply process and analyze the collected data to extract effective matching features and rules, including the matching affinity between different flower varieties, the visual effect rules of specific color combinations, the coordination of matching flowers of different forms, and the matching adaptability with other elements in different scenes.

[0006] Preferably, the matching generation module automatically generates a variety of flower matching schemes that meet different needs and preferences based on the comprehensive and accurate results obtained by the data analysis module through an intelligent algorithm. These schemes not only take into account the characteristics of the flowers themselves, but also integrate multiple factors such as scene requirements and user preferences.

[0007] Preferably, the user interaction module allows the user to input his or her preferences for certain flowers, preferences for specific colors, and types of scenes that require matching, and can adjust the matching scheme in real time based on the user input to provide a personalized match that better meets the user's actual needs.

[0008] Preferably, the scheme display module is used to present the generated flower matching scheme to the user in an intuitive form, such as displaying the overall effect after matching through high-definition pictures, displaying the dynamic matching process and details through videos, or introducing the characteristics and advantages of the matching scheme in detail through text descriptions.

[0009] Preferably, the user interaction module includes an interaction module and an evaluation module, and the interaction module is electrically connected to the evaluation module and the data analysis module.

[0010] Preferably, the solution display module includes a display module and a judgment module, the display module is electrically connected to the judgment module and the data storage module, and the judgment module is electrically connected to the evaluation module.

[0011] Preferably, the data storage module is used to store the information collected by the data collection module.

[0012] The present invention provides a flower matching method based on big data, which has the following beneficial effects: The flower matching method based on big data can not only judge the user's preferences according to the conditions input by the user, but also dig out the user's unexpressed ideas through the deep mining algorithm, so that the generated plan can be more in line with the user's ideas and improve the adoption rate of the plan. The plans adopted by past users can also be stored. The next time the user enters similar conditions, the previously adopted plans can be recommended first. At the same time, the adopted plans will be scored, and the average value of multiple scores is defined as the real score of the plan. The degree of match between the plan and the user's description can be judged by the height of the real score, so that the user can be provided with a reference through the real score, and the plans with higher real scores are displayed first during display, which can also improve the adoption rate of the plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a module schematic diagram of the flower matching method based on big data of the present invention; Figure 2 This is a schematic diagram showing the modules of the solution of the present invention; Figure 3 Schematic diagram of the user interaction module of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] For the first embodiment, please refer to Figure 1 ,The present invention provides a technical solution: a flower matching method based on big data, including a data acquisition module, a data analysis module, a matching generation module, a user interaction module, a solution display module and a data storage module; The data acquisition module is electrically connected to the data storage module, the data storage module is electrically connected to the collocation generation module, the collocation generation module is electrically connected to the solution display module and the data analysis module, and the data analysis module is electrically connected to the user interaction module.

[0016] The data collection module is used to widely collect various data related to flower matching through multiple channels, including but not limited to detailed flower variety information obtained from professional flower databases, covering their Latin scientific names, common names, and variety characteristics, the distribution and popular trends of different flower colors extracted from flower market transaction records, the flower shape, size, number and arrangement of petals of flowers collected using image recognition technology, various flower languages ​​and their symbolic meanings compiled from flower language books and cultural materials, and relevant data on flower matching preferences in wedding scenes, celebration scenes, and home decoration scenes.

[0017] The data analysis module uses big data analysis algorithms such as cluster analysis and association rule mining to deeply process and analyze the collected data to extract effective matching features and rules, including the matching affinity between different flower varieties, the visual effect rules of specific color combinations, the coordination of matching flowers of different forms, and the matching adaptability with other elements in different scenes.

[0018] The matching generation module automatically generates a variety of flower matching solutions that meet different needs and preferences based on the comprehensive and accurate results obtained by the data analysis module through intelligent algorithms. These solutions not only take into account the characteristics of the flowers themselves, but also integrate multiple factors such as scene requirements and user preferences.

[0019] The user interaction module allows the user to input preferences for certain flowers, preferences for specific colors, and types of scenes that need to be matched, and can adjust the matching scheme in real time according to the user input to provide personalized matching that better meets the actual needs of the user.

[0020] The scheme display module is used to present the generated flower matching scheme to the user in an intuitive form, such as displaying the overall effect after matching through high-definition pictures, displaying the dynamic matching process and details through videos, or introducing the characteristics and advantages of the matching scheme in detail through text descriptions; The data storage module is used to store the information collected by the data collection module; Usage methods include: Step 1: Through the data collection module, using web crawlers and other means, comprehensively collect flower feature data, such as the specific colors of various flowers, such as red, pink, white, etc., shapes, such as round, long, etc., sizes, such as large, medium, small, smells, such as fragrant, elegant, etc., and detailed attributes such as the length of flowering period. At the same time, collect user preference data such as good and bad reviews, number of likes, collection status, etc. on different flower combinations left by users on various platforms, and obtain detailed scene-related data such as specific requirements and expectations for flower combinations in different scenes, such as romantic weddings, grand celebrations, serious meetings, etc., and pass the collected data to the data storage module for storage; Step 2: The user enters personal preferences for flowers through the user interaction module, such as information related to the favorite flower variety, color, flower shape and size, as well as information related to specific scenes, such as weddings, birthday parties, and gatherings with friends, and enters the desired number of flowers, and can also enter a corresponding quantity range; Step 3: The data analysis module receives the information input by the user, analyzes and classifies the information input by the user, and not only performs basic operations such as classification and clustering, but also uses data mining algorithms to deeply explore the hidden connections in the data, so as to accurately judge the user's preferences and pass the judgment results to the matching generation module; Step 4: Based on the detailed analysis results, use intelligent algorithms to generate a flower matching plan that suits the user's preferences. The plan will specify the specific flower variety combination and accurately give the quantity ratio of each variety. At the same time, it will determine the overall style of the matching and the color of the flowers to be used based on the matching style. Finally, the matched flower combination can be displayed to the user in the form of a graphic.

[0021] The flowers matched by this matching method can judge the user's preferences based on the conditions input by the user, and through the deep mining algorithm, the user's unexpressed ideas can be excavated, so that the generated plan can be more in line with the user's ideas and improve the adoption rate of the plan.

[0022] For the second embodiment, please refer to Figure 2 ,The present invention provides a technical solution: a flower matching method based on big data, including a data acquisition module, a data analysis module, a matching generation module, a user interaction module, a solution display module and a data storage module; The data acquisition module is electrically connected to the data storage module, the data storage module is electrically connected to the collocation generation module, the collocation generation module is electrically connected to the solution display module and the data analysis module, and the data analysis module is electrically connected to the user interaction module.

[0023] The data collection module is used to widely collect various data related to flower matching through multiple channels, including but not limited to detailed flower variety information obtained from professional flower databases, covering their Latin scientific names, common names, and variety characteristics, the distribution and popular trends of different flower colors extracted from flower market transaction records, the flower shape, size, number and arrangement of petals of flowers collected using image recognition technology, various flower languages ​​and their symbolic meanings compiled from flower language books and cultural materials, and relevant data on flower matching preferences in wedding scenes, celebration scenes, and home decoration scenes.

[0024] The data analysis module uses big data analysis algorithms such as cluster analysis and association rule mining to deeply process and analyze the collected data to extract effective matching features and rules, including the matching affinity between different flower varieties, the visual effect rules of specific color combinations, the coordination of matching flowers of different forms, and the matching adaptability with other elements in different scenes.

[0025] The matching generation module automatically generates a variety of flower matching solutions that meet different needs and preferences based on the comprehensive and accurate results obtained by the data analysis module through intelligent algorithms. These solutions not only take into account the characteristics of the flowers themselves, but also integrate multiple factors such as scene requirements and user preferences.

[0026] The user interaction module allows the user to input preferences for certain flowers, preferences for specific colors, and types of scenes that need to be matched, and can adjust the matching scheme in real time according to the user input to provide personalized matching that better meets the actual needs of the user.

[0027] The scheme display module is used to present the generated flower matching scheme to the user in an intuitive form, such as displaying the overall effect after matching through high-definition pictures, displaying the dynamic matching process and details through videos, or introducing the characteristics and advantages of the matching scheme in detail through text descriptions; The solution display module includes a display module and a judgment module, and the display module is electrically connected to the judgment module and the data storage module; The data storage module is used to store the information collected by the data collection module; Usage methods include: Step 1: Through the data collection module, using web crawlers and other means, comprehensively collect flower feature data, such as the specific colors of various flowers, such as red, pink, white, etc., shapes, such as round, long, etc., sizes, such as large, medium, small, smells, such as fragrant, elegant, etc., and detailed attributes such as the length of flowering period. At the same time, collect user preference data such as good and bad reviews, number of likes, collection status, etc. on different flower combinations left by users on various platforms, and obtain detailed scene-related data such as specific requirements and expectations for flower combinations in different scenes, such as romantic weddings, grand celebrations, serious meetings, etc., and pass the collected data to the data storage module for storage; Step 2: The user enters personal preferences for flowers through the user interaction module, such as information related to the favorite flower variety, color, flower shape and size, as well as information related to specific scenes, such as weddings, birthday parties, and gatherings with friends, and enters the desired number of flowers, and can also enter a corresponding quantity range; Step 3: The data analysis module receives the information input by the user, analyzes and classifies the information input by the user, and not only performs basic operations such as classification and clustering, but also uses data mining algorithms to deeply explore the hidden connections in the data, so as to accurately judge the user's preferences and pass the judgment results to the matching generation module; Step 4: Based on the detailed analysis results, an intelligent algorithm is used to generate a flower matching plan that meets the user's preferences. The plan will specify the specific flower variety combination and accurately give the quantity ratio of each variety. At the same time, the overall matching style is determined. The color of the flowers to be used is determined based on the matching style. Finally, the matched flower combination can be displayed to the user in the form of a graphic; Step 5: After the user sees the matching plan, the judgment module determines whether to adopt it. When the judgment module determines that the user adopts the plan, the display module transmits the plan to the data storage module, and the data storage module stores the plan adopted by the user.

[0028] The flowers matched by this matching method can not only judge the user's preferences based on the conditions entered by the user, but also dig out the user's unexpressed ideas through in-depth mining algorithms, so that the generated plan can be more in line with the user's ideas and improve the adoption rate of the plan. It can also store the plans adopted by past users, so that the next time the user enters similar conditions, the previously adopted plans can be recommended first.

[0029] For the third embodiment, please refer to Figure 3 ,The present invention provides a technical solution: a flower matching method based on big data, including a data acquisition module, a data analysis module, a matching generation module, a user interaction module, a solution display module and a data storage module; The data acquisition module is electrically connected to the data storage module, the data storage module is electrically connected to the collocation generation module, the collocation generation module is electrically connected to the solution display module and the data analysis module, and the data analysis module is electrically connected to the user interaction module.

[0030] The data collection module is used to widely collect various data related to flower matching through multiple channels, including but not limited to detailed flower variety information obtained from professional flower databases, covering their Latin scientific names, common names, and variety characteristics, the distribution and popular trends of different flower colors extracted from flower market transaction records, the flower shape, size, number and arrangement of petals of flowers collected using image recognition technology, various flower languages ​​and their symbolic meanings compiled from flower language books and cultural materials, and relevant data on flower matching preferences in wedding scenes, celebration scenes, and home decoration scenes.

[0031] The data analysis module uses big data analysis algorithms such as cluster analysis and association rule mining to deeply process and analyze the collected data to extract effective matching features and rules, including the matching affinity between different flower varieties, the visual effect rules of specific color combinations, the coordination of matching flowers of different forms, and the matching adaptability with other elements in different scenes.

[0032] The matching generation module automatically generates a variety of flower matching solutions that meet different needs and preferences based on the comprehensive and accurate results obtained by the data analysis module through intelligent algorithms. These solutions not only take into account the characteristics of the flowers themselves, but also integrate multiple factors such as scene requirements and user preferences.

[0033] The user interaction module allows the user to input preferences for certain flowers, preferences for specific colors, and types of scenes that need to be matched, and can adjust the matching scheme in real time according to the user input to provide personalized matching that better meets the actual needs of the user.

[0034] The scheme display module is used to present the generated flower matching scheme to the user in an intuitive form, such as displaying the overall effect after matching through high-definition pictures, displaying the dynamic matching process and details through videos, or introducing the characteristics and advantages of the matching scheme in detail through text descriptions; The user interaction module includes an interaction module and an evaluation module, and the interaction module is electrically connected to the evaluation module and the data analysis module; The solution display module includes a display module and a judgment module, the display module is electrically connected to the judgment module and the data storage module, and the judgment module is electrically connected to the evaluation module; The data storage module is used to store the information collected by the data collection module; Usage methods include: Step 1: Through the data collection module, using web crawlers and other means, comprehensively collect flower feature data, such as the specific colors of various flowers, such as red, pink, white, etc., shapes, such as round, long, etc., sizes, such as large, medium, small, smells, such as fragrant, elegant, etc., and detailed attributes such as the length of flowering period. At the same time, collect user preference data such as good and bad reviews, number of likes, collection status, etc. on different flower combinations left by users on various platforms, and obtain detailed scene-related data such as specific requirements and expectations for flower combinations in different scenes, such as romantic weddings, grand celebrations, serious meetings, etc., and pass the collected data to the data storage module for storage; Step 2: The user enters personal preferences for flowers through the user interaction module, such as information related to the favorite flower variety, color, flower shape and size, as well as information related to specific scenes, such as weddings, birthday parties, and gatherings with friends, and enters the desired number of flowers, and can also enter a corresponding quantity range; Step 3: The data analysis module receives the information input by the user, analyzes and classifies the information input by the user, and not only performs basic operations such as classification and clustering, but also uses data mining algorithms to deeply explore the hidden connections in the data, so as to accurately judge the user's preferences and pass the judgment results to the collocation generation module; Step 4: Based on the detailed analysis results, an intelligent algorithm is used to generate a flower matching plan that meets the user's preferences. The plan will specify the specific flower variety combination and accurately give the quantity ratio of each variety. At the same time, the overall matching style is determined. The color of the flowers to be used is determined based on the matching style. Finally, the matched flower combination can be displayed to the user in the form of a graphic; Step 5: After the user sees the matching plan, he / she will rate the plan through the rating module. The full score is 10 points. A score of 6 or above means that the plan is adopted, and a score of less than 6 means that the plan is not adopted. After the user rates, the rating module will pass the rating to the judgment module. Step 6: The judgment module determines whether the user adopts the solution. After the judgment module determines that the user adopts the solution, the display module transmits the solution to the data storage module, and the data storage module stores the solution adopted by the user.

[0035] The flowers matched by this matching method can not only judge the user's preferences based on the conditions input by the user, but also dig out the user's unexpressed ideas through the deep mining algorithm, so that the generated plan can be more in line with the user's ideas and improve the adoption rate of the plan. The plans adopted by past users can also be stored. The next time the user enters similar conditions, the previously adopted plans can be recommended first. At the same time, the adopted plans will be scored, and the average of multiple scores is defined as the real score of the plan. The degree of match between the plan and the user's description can be judged by the height of the real score, so that the user can be provided with a reference through the real score, and plans with higher real scores are given priority during display, which can also improve the adoption rate of plans.

[0036] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A flower matching method based on big data, characterized in that: It includes data collection module, data analysis module, collocation generation module, user interaction module, solution display module and data storage module; The data acquisition module is electrically connected to the data storage module, the data storage module is electrically connected to the collocation generation module, the collocation generation module is electrically connected to the solution display module and the data analysis module, and the data analysis module is electrically connected to the user interaction module.

2. A flower matching method based on big data according to claim 1, characterized in that: The data collection module is used to widely collect various data related to flower matching through multiple channels, including but not limited to detailed flower variety information obtained from professional flower databases, covering their Latin scientific names, common names, and variety characteristics, the distribution and popular trends of different flower colors extracted from flower market transaction records, the flower shape, size, number and arrangement of petals of flowers collected using image recognition technology, various flower languages ​​and their symbolic meanings compiled from flower language books and cultural materials, and relevant data on flower matching preferences in wedding scenes, celebration scenes, and home decoration scenes.

3. The flower matching method based on big data according to claim 1, characterized in that: The data analysis module uses big data analysis algorithms such as cluster analysis and association rule mining to deeply process and analyze the collected data to extract effective matching features and rules, including the matching affinity between different flower varieties, the visual effect rules of specific color combinations, the coordination of matching flowers of different forms, and the matching adaptability with other elements in different scenes.

4. The flower matching method based on big data according to claim 1, characterized in that: The matching generation module automatically generates a variety of flower matching solutions that meet different needs and preferences based on the comprehensive and accurate results obtained by the data analysis module through intelligent algorithms. These solutions not only take into account the characteristics of the flowers themselves, but also integrate multiple factors such as scene requirements and user preferences.

5. The flower matching method based on big data according to claim 1, characterized in that: The user interaction module allows the user to input preferences for certain flowers, preferences for specific colors, and types of scenes that need to be matched, and can adjust the matching scheme in real time according to the user input to provide personalized matching that better meets the actual needs of the user.

6. The flower matching method based on big data according to claim 1, characterized in that: The scheme display module is used to present the generated flower matching scheme to the user in an intuitive form, such as displaying the overall effect after matching through high-definition pictures, displaying the dynamic matching process and details through videos, or introducing the characteristics and advantages of the matching scheme in detail through text descriptions.

7. The flower matching method based on big data according to claim 1, characterized in that: The user interaction module includes an interaction module and an evaluation module, and the interaction module is electrically connected to the evaluation module and the data analysis module.

8. The flower matching method based on big data according to claim 7, characterized in that: The solution display module includes a display module and a judgment module. The display module is electrically connected to the judgment module and the data storage module. The judgment module is electrically connected to the evaluation module.

9. The flower matching method based on big data according to claim 1, characterized in that: The data storage module is used to store the information collected by the data collection module.

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