A numerical control intelligent APP supply chain management system and a supplier precise matching method

Abstract

The application relates to a numerical control intelligent APP supply chain management system and a supplier precise matching method, aiming to solve the problems of poor information, low coordination efficiency, insufficient prediction ability and the like in traditional supply chain management. The system integrates multiple function modules such as commodity management, sales management and procurement management, collects and analyzes the data of each link of the supply chain in real time, constructs user portraits and supplier portraits, realizes precise supplier matching and commodity recommendation. Meanwhile, the system also has real-time monitoring and early warning functions, ensures the smooth operation of the supply chain, brings significant economic benefits and social benefits to enterprises, and has wide market prospects and application value.

Description

Technical Field

[0001] This invention relates to the fields of information technology and supply chain management technology, specifically to a numerical control intelligent APP supply chain management system and a method for precise supplier matching. Background Technology

[0002] Traditional supply chain management often relies on manual operations and information silos, resulting in poor information flow and low collaboration efficiency between different links in the supply chain. Furthermore, due to a lack of effective data analysis and forecasting tools, companies often struggle to accurately grasp market demand and supplier dynamics, leading to frequent problems such as inventory backlogs and high procurement costs.

[0003] To address these issues, various supply chain management software and systems have emerged in the market. However, most of these systems are single-function and lack intelligence and integration. For example, some systems only provide product management or sales management functions and cannot achieve comprehensive digital management of the supply chain; others, while integrating multiple functional modules, lack intelligent data analysis and forecasting capabilities, and cannot provide enterprises with accurate market insights and decision support.

[0004] Therefore, there is an urgent need for a CNC intelligent APP-based supply chain management system that can comprehensively cover all aspects of supply chain management and possess intelligent data analysis and forecasting capabilities. This system should be able to collect and analyze data from all stages of the supply chain in real time, build user and supplier profiles, and achieve accurate supplier matching and product recommendations. Simultaneously, it should also have real-time monitoring and early warning functions to ensure the smooth operation of the supply chain and the sustainable development of the enterprise.

[0005] In summary, a CNC intelligent APP supply chain management system and a precise supplier matching method are needed to solve the above problems. Summary of the Invention

[0006] To address the problems of existing technologies, this invention provides a numerical control intelligent APP supply chain management system and a method for precise supplier matching.

[0007] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: Firstly, a numerical control intelligent APP supply chain management system, comprising:

[0008] The user-facing app is used to display product information, receive user orders, and provide technical support and after-sales service.

[0009] The management backend system is used for product management, sales management, procurement management, data analysis, and intelligent recommendation.

[0010] The product management module supports enterprises in publishing product information, including but not limited to CNC equipment and auxiliary consumables, and provides product images, prices, and detailed inventory information. The sales management module supports online ordering, payment, and order tracking. The procurement management module is used to manage suppliers, generate and track purchase orders. The data analysis and intelligent recommendation module performs in-depth analysis based on user behavior data, product sales data, and supplier information to build user profiles and supplier profiles, and makes accurate recommendations based on these profiles.

[0011] In this aspect, comprehensive digital management of the supply chain is achieved by integrating a user-facing app and a management backend system. The user-facing app provides functions such as product display, order receipt, technical support, and after-sales service, greatly enhancing the user experience. The management backend system covers multiple modules including product management, sales management, procurement management, data analysis, and intelligent recommendations, providing enterprises with comprehensive management tools.

[0012] Through digital management, businesses can monitor inventory, sales, and procurement progress in real time, thereby improving operational efficiency. Based on user behavior data and product sales data, the system can build user profiles, enabling precise recommendations and personalized marketing, thus increasing conversion rates. The system supports online ordering, payment, and order tracking, simplifying procurement and sales processes and reducing operating costs. The real-time online customer service module provides immediate technical support and after-sales service, enhancing user satisfaction and loyalty.

[0013] Compared to traditional technologies, the CNC intelligent APP supply chain management system realizes transparency, intelligence and efficiency in the supply chain, significantly improving the competitiveness and market responsiveness of enterprises.

[0014] In some specific implementations of the first aspect, the user-side APP also includes a real-time online customer service module to provide immediate technical support and after-sales service.

[0015] In some specific implementations of the first aspect, the management backend system also includes a user management and access control module, which is used to realize user authentication, role assignment and access control, and ensure system security.

[0016] Secondly, a supplier precision matching method based on a CNC intelligent APP supply chain management system includes the following steps:

[0017] Step 1: Collect, but not limited to, user behavior data, product sales data, and supplier information;

[0018] Step 2: Perform in-depth analysis on the data to construct user profiles and supplier profiles;

[0019] Step 3: Based on user profiles and supplier profiles, use machine learning algorithms to accurately match suppliers with demand;

[0020] Step 4: Based on the matching results, optimize resource allocation and provide accurate product recommendations and services.

[0021] In this regard, a supplier precision matching method based on a CNC intelligent APP supply chain management system is proposed. This method collects user behavior data, product sales data, and supplier information to construct user and supplier profiles, and then utilizes machine learning algorithms for precise matching. This approach optimizes resource allocation, provides accurate product recommendations and services, and meets users' personalized needs.

[0022] Through deep analysis and machine learning algorithms, the system can accurately match user needs with supplier capabilities, reducing matching errors. Based on the matching results, enterprises can rationally allocate resources and improve the overall efficiency of the supply chain. Precise matching can provide users with goods and services that better meet their needs, improving user satisfaction. Based on the matching results and user feedback, the system can continuously optimize machine learning algorithms to improve matching accuracy and efficiency.

[0023] Compared to traditional manual matching or simple rule-based matching methods, the supplier precision matching method has higher matching accuracy and efficiency, and can bring greater business value to enterprises.

[0024] In some specific implementations of the second aspect, the in-depth analysis in step two includes the analysis of data including but not limited to user purchase history, browsing records and search keywords, as well as the evaluation of information including but not limited to supplier reputation, product quality and delivery speed.

[0025] In some specific implementations of the second aspect, the machine learning algorithms in step three include, but are not limited to, support vector machines, random forests, and neural networks.

[0026] In some specific implementations of the second aspect, step five is also included: continuously optimizing the machine learning algorithm based on the matching results and user feedback to improve matching accuracy.

[0027] Thirdly, a CNC intelligent APP supply chain management system and a supplier precision matching method, wherein the data analysis and intelligent recommendation module further includes:

[0028] The trend prediction submodule is used to predict future market demand trends based on historical sales data and user behavior data;

[0029] The user preference analysis submodule is used to analyze user purchasing habits, preferences and feedback to build personalized user profiles;

[0030] The supplier evaluation submodule comprehensively evaluates and grades suppliers based on multi-dimensional information including but not limited to product quality, on-time delivery rate, and after-sales service evaluation.

[0031] The intelligent recommendation engine combines trend prediction, user preference analysis, and supplier evaluation results to recommend the most suitable products and services to users.

[0032] In this regard, the data analysis and intelligent recommendation module is an important component of the CNC intelligent APP supply chain management system. By analyzing historical sales data, user behavior data, and supplier information, it constructs sub-modules such as trend prediction, user preference analysis, and supplier evaluation to provide users with the most suitable product and service recommendations.

[0033] The trend prediction submodule uses time series analysis and machine learning models to accurately predict future market demand trends, providing a basis for enterprises to formulate reasonable production plans. The user preference analysis submodule uses collaborative filtering and content-based recommendation algorithms, combined with users' historical behavior and preferences, to generate personalized product and service recommendation lists, improving user satisfaction and conversion rates. The supplier evaluation submodule comprehensively evaluates and grades suppliers based on multi-dimensional information, providing a reference for enterprises to select high-quality suppliers. The intelligent recommendation engine combines the analysis results of multiple submodules to provide enterprises with comprehensive decision support, improving decision-making efficiency and accuracy.

[0034] In some specific implementations of the three aspects, the trend forecasting submodule uses time series analysis and machine learning models to forecast market demand in order to improve the accuracy of the forecast.

[0035] In some specific implementations of the three aspects, the user preference analysis submodule utilizes collaborative filtering and content-based recommendation algorithms, combined with users' historical behavior and preferences, to generate personalized product and service recommendation lists.

[0036] The beneficial effects of this invention are as follows:

[0037] 1. This invention, by leveraging the product management, sales management, and procurement management functions of the management backend system, automates and standardizes the supply chain process, reduces manual intervention, and significantly improves operational efficiency. The application of data analysis and intelligent recommendation modules enables enterprises to respond more quickly to market demands, optimize resource allocation, and further enhance operational efficiency.

[0038] 2. Accurate market demand forecasting and personalized product recommendations help companies accurately grasp market dynamics, meet consumers' personalized needs, and thus enhance market competitiveness. Optimized supplier management and precise matching methods ensure that companies can select the most suitable suppliers, improve product quality and service levels, and further consolidate their market position.

[0039] 3. The real-time online customer service module provided by the user-side APP ensures that users can get technical support and after-sales service in a timely manner, which improves user satisfaction and loyalty. Personalized product recommendations and high-quality service experience further enhance the user's shopping experience and satisfaction.

[0040] 4. The CNC market presents boundless business opportunities in this sunrise industry, with enormous potential for future growth. The intelligent knowledge economy will be the driving force behind socio-economic development for the next few decades. In the future, any movement involving physical quantities such as force, speed, acceleration, distance, angle (direction), and displacement will rely heavily on CNC technology, exhibiting a rigid and fundamental demand. It boasts strong technical support capabilities, broad industry coverage, and significant platform advantages for promotion. Its wide reach and significant influence will inevitably lead to the emergence of numerous new industry brands and business models. Under the influence of CNC technology, the quality of industries will undoubtedly be improved. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the system connection of the present invention.

[0042] Figure 2 This is a flowchart illustrating the present invention. Detailed Implementation

[0043] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1: As Figures 1 to 2 As shown, a CNC intelligent APP supply chain management system includes: a user-side APP for displaying product information, receiving user orders, and providing technical support and after-sales service; and a management-side backend system for product management, sales management, procurement management, and data analysis and intelligent recommendation. The product management module supports enterprises in publishing product information, including but not limited to CNC equipment and auxiliary consumables, and provides product images, prices, and detailed inventory information. The sales management module supports online ordering, payment, and order tracking. The procurement management module is used for supplier management, procurement order generation, and tracking. The data analysis and intelligent recommendation module performs in-depth analysis based on user behavior data, product sales data, and supplier information to construct user and supplier profiles, and makes accurate recommendations based on these profiles.

[0045] The user-side app also includes a real-time online customer service module to provide immediate technical support and after-sales service. The management backend system also includes a user management and access control module to implement user authentication, role assignment, and access control, ensuring system security.

[0046] This embodiment introduces a CNC intelligent APP supply chain management system, which mainly consists of two parts: a user-end APP and a management-end backend system.

[0047] User-side APP functions:

[0048] Product information display: Show users product information such as CNC equipment and auxiliary materials and consumables.

[0049] Receive user orders: Allow users to place orders for goods online.

[0050] Technical support and after-sales service: Provides immediate technical support and after-sales service, including a real-time online customer service module.

[0051] Management backend system functions:

[0052] Product Management Module: Supports enterprises in publishing product information, including detailed information such as product images, prices, and inventory.

[0053] Sales Management Module: Enables online ordering, payment, and order tracking.

[0054] Procurement Management Module: Used for supplier management, purchase order generation and tracking.

[0055] Data Analysis and Intelligent Recommendation Module: Based on in-depth analysis of user behavior data, product sales data, supplier information, etc., it builds user profiles and supplier profiles, and makes accurate recommendations.

[0056] User management and access control module: Implements user authentication, role assignment and access control to ensure system security.

[0057] Example 2, as Figures 1 to 2 The method for precise supplier matching based on a CNC intelligent APP supply chain management system, as shown, includes the following steps: Step 1: Collecting, but not limited to, user behavior data, product sales data, and supplier information; Step 2: Performing in-depth analysis on the data to construct user profiles and supplier profiles; Step 3: Based on the user profiles and supplier profiles, using machine learning algorithms to perform precise matching between suppliers and demand; Step 4: Optimizing resource allocation based on the matching results to provide accurate product recommendations and services.

[0058] The machine learning algorithms in step three include, but are not limited to, support vector machines, random forests, and neural networks. Step five also includes continuously optimizing the machine learning algorithms based on matching results and user feedback to improve matching accuracy.

[0059] This embodiment introduces a supplier precision matching method based on a CNC intelligent APP supply chain management system. The method includes the following steps:

[0060] Step 1: Collect data

[0061] Collect user behavior data, product sales data, and supplier information.

[0062] Step Two: Building a Profile

[0063] We conduct in-depth analysis of the collected data to build user profiles and supplier profiles.

[0064] Step 3: Precise Matching

[0065] Based on user profiles and supplier profiles, machine learning algorithms (such as support vector machines, random forests, neural networks, etc.) are used to accurately match suppliers with demand.

[0066] Step 4: Optimize resource allocation

[0067] Based on the matching results, optimize resource allocation and provide accurate product recommendations and services.

[0068] Step 5: Continuous Optimization

[0069] Based on matching results and user feedback, we continuously optimize machine learning algorithms to improve matching accuracy.

[0070] Example 3, as Figures 1 to 2 As shown, a CNC intelligent APP supply chain management system and supplier precision matching method further include a data analysis and intelligent recommendation module comprising: a trend prediction submodule, used to predict future market demand trends based on historical sales data and user behavior data; a user preference analysis submodule, used to analyze user purchasing habits, preferences, and feedback to build personalized user profiles; a supplier evaluation submodule, used to comprehensively evaluate and classify suppliers based on multi-dimensional information including but not limited to product quality, on-time delivery rate, and after-sales service evaluation; and an intelligent recommendation engine, which combines trend prediction, user preference analysis, and supplier evaluation results to recommend the most suitable products and services to users.

[0071] The trend prediction submodule uses time series analysis and machine learning models to forecast market demand, thereby improving prediction accuracy. The user preference analysis submodule utilizes collaborative filtering and content-based recommendation algorithms, combined with users' historical behavior and preferences, to generate personalized lists of recommended goods and services.

[0072] This embodiment further optimizes the CNC intelligent APP supply chain management system and the supplier precision matching method based on Embodiment 1 and Embodiment 2, especially in the data analysis and intelligent recommendation module.

[0073] Data analysis and intelligent recommendation module optimization:

[0074] Trend Forecasting Submodule: Employs time series analysis and machine learning models to forecast market demand and improve forecast accuracy.

[0075] The user preference analysis submodule utilizes collaborative filtering and content-based recommendation algorithms, combined with users' historical behavior and preferences, to generate personalized lists of recommended products and services.

[0076] Supplier evaluation submodule: Based on multi-dimensional information such as product quality, on-time delivery rate, and after-sales service evaluation, suppliers are comprehensively evaluated and classified.

[0077] Intelligent recommendation engine: Combining trend prediction, user preference analysis, and supplier evaluation results, it recommends the most suitable products and services to users.

[0078] Through the above optimizations, the CNC intelligent APP supply chain management system and supplier precision matching method of this embodiment can more accurately predict market demand, recommend goods and services in a more personalized way, and improve the comprehensive evaluation and management level of suppliers.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A numerical control intelligent APP supply chain management system, characterized in that, include: The user-facing app is used to display product information, receive user orders, and provide technical support and after-sales service. The management backend system is used for product management, sales management, procurement management, data analysis, and intelligent recommendation. The product management module supports enterprises in publishing product information, including but not limited to CNC equipment and auxiliary consumables, and provides product images, prices, and detailed inventory information. The sales management module supports online ordering, payment, and order tracking. The procurement management module is used to manage suppliers, generate and track purchase orders. The data analysis and intelligent recommendation module performs in-depth analysis based on user behavior data, product sales data, and supplier information to build user profiles and supplier profiles, and makes accurate recommendations based on these profiles.

2. The CNC intelligent APP supply chain management system according to claim 1, characterized in that: The user-side app also includes a real-time online customer service module to provide immediate technical support and after-sales service.

3. The CNC intelligent APP supply chain management system according to claim 1, characterized in that: The management backend system also includes a user management and access control module, which is used to realize user authentication, role assignment and access control, and ensure system security.

4. A supplier precision matching method based on a CNC intelligent APP supply chain management system, characterized in that, Includes the following steps: Step 1: Collect, but not limited to, user behavior data, product sales data, and supplier information; Step 2: Perform in-depth analysis on the data to construct user profiles and supplier profiles; Step 3: Based on user profiles and supplier profiles, use machine learning algorithms to accurately match suppliers with demand; Step 4: Based on the matching results, optimize resource allocation and provide accurate product recommendations and services.

5. The supplier precision matching method based on a CNC intelligent APP supply chain management system according to claim 4, characterized in that: The in-depth analysis in step two includes, but is not limited to, the analysis of user purchase history, browsing records and search keyword data, as well as the evaluation of information including but not limited to supplier reputation, product quality and delivery speed.

6. The supplier precision matching method based on a CNC intelligent APP supply chain management system according to claim 4, characterized in that: The machine learning algorithms in step three include, but are not limited to, support vector machines, random forests, and neural networks.

7. The supplier precision matching method based on a CNC intelligent APP supply chain management system according to claim 5, characterized in that: It also includes step five: continuously optimizing the machine learning algorithm based on the matching results and user feedback to improve matching accuracy.

8. The CNC intelligent APP supply chain management system and supplier precise matching method according to claim 1, characterized in that: The data analysis and intelligent recommendation module further includes: The trend prediction submodule is used to predict future market demand trends based on historical sales data and user behavior data; The user preference analysis submodule is used to analyze user purchasing habits, preferences and feedback to build personalized user profiles; The supplier evaluation submodule comprehensively evaluates and grades suppliers based on multi-dimensional information including but not limited to product quality, on-time delivery rate, and after-sales service evaluation. The intelligent recommendation engine combines trend prediction, user preference analysis, and supplier evaluation results to recommend the most suitable products and services to users.

9. The CNC intelligent APP supply chain management system and supplier precise matching method according to claim 8, characterized in that: The trend prediction submodule uses time series analysis and machine learning models to predict market demand in order to improve the accuracy of the prediction.

10. The CNC intelligent APP supply chain management system and supplier precise matching method according to claim 8, characterized in that: The user preference analysis submodule utilizes collaborative filtering and content-based recommendation algorithms, combined with users' historical behavior and preferences, to generate personalized product and service recommendation lists.

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