A numerical control self-media intelligent management and content recommendation system

Abstract

The present application relates to a kind of numerical control self-media intelligent management and content recommendation system, integrates data acquisition, content creation, intelligent recommendation and data analysis and other functions. By collecting numerical control equipment data and user behavior data, construct accurate user interest portrait, and realize the personalized recommendation of content using advanced machine learning algorithm. The system also provides convenient content creation tools and automatically generated material library, improves content creation efficiency and quality. At the same time, it has perfect content review and management functions to ensure the compliance and high quality of published content. The system aims to solve the problem of complicated content management and inaccurate recommendation of numerical control self-media platform, provides efficient, convenient self-media creation and publishing platform for numerical control practitioners, and improves user experience and satisfaction.

Description

Technical Field

[0001] This invention relates to the fields of CNC and Internet technology, specifically to a CNC self-media intelligent management and content recommendation system. Background Technology

[0002] With the rapid development of information technology and the popularization of the internet, self-media, as an emerging media form, has deeply penetrated people's daily lives. Especially in the CNC field, self-media platforms have become an important channel for CNC professionals to exchange technologies, share experiences, and showcase achievements. However, with the continuous emergence of CNC self-media platforms and the rapid growth of content, how to efficiently manage self-media content and accurately recommend it to users has become a major challenge for CNC self-media platforms.

[0003] Traditional CNC self-media platforms mostly rely on manual editing and recommendations, which is not only inefficient but also fails to meet users' personalized needs. Furthermore, due to the rapid pace of technological updates and the vast variety of content in the CNC field, traditional recommendation methods often struggle to capture users' true interests and needs, resulting in poor recommendation effectiveness.

[0004] To address these issues, in recent years, an increasing number of CNC self-media platforms have begun to incorporate intelligent management and content recommendation technologies. These technologies collect and analyze user browsing history, interaction data, and other information to build user interest profiles, thereby enabling personalized content recommendations. However, most existing intelligent recommendation systems still suffer from several problems, such as incomplete data collection, inaccurate recommendation algorithms, and inadequate user feedback mechanisms, resulting in room for improvement in recommendation effectiveness.

[0005] Based on the above background, we have developed a CNC self-media intelligent management and content recommendation system. Summary of the Invention

[0006] To address the problems of existing technologies, this invention provides a numerical control self-media intelligent management and content recommendation system.

[0007] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: 1. In a first aspect, a CNC self-media intelligent management and content recommendation system, comprising: a data acquisition and integration module, used to acquire operating data from the CNC equipment, including equipment status, working parameters, and fault warning information, and upload it to the system center in real time through Internet of Things technology; at the same time, it integrates user behavior data on the self-media platform to construct user interest profiles;

[0008] The content creation and management module provides content creation tools to support CNC professionals in creating self-media content and links it with CNC equipment data to automatically generate a material library based on equipment status, technical cases, troubleshooting, etc. It also has content review and management functions to ensure the quality of published content.

[0009] The intelligent recommendation and distribution module uses machine learning algorithms to intelligently recommend self-media content based on user interest profiles and CNC equipment data; and achieves accurate content distribution based on user's region, occupation, equipment type, and other dimensions.

[0010] The data analysis and optimization module conducts in-depth analysis of the operational data of the self-media platform, evaluates the popularity of content and user feedback, and generates optimization suggestions based on the analysis results to guide creators in adjusting content strategies and optimizing recommendation algorithms.

[0011] In this aspect, the system collects key operational data of CNC equipment in real time, such as status, parameters, and fault warnings, providing data support for equipment maintenance and management. It integrates and analyzes user behavior data from self-media platforms to construct accurate user interest profiles, providing a basis for personalized content recommendations. It offers rich creation tools to lower the barrier to content creation, improve efficiency, and automatically generate a material library based on equipment data, enriching content creation resources. Through content review and management functions, it ensures the quality and compliance of published content. Based on user interest profiles and real-time behavior data, it achieves personalized content recommendations, improving user satisfaction and stickiness. It accurately distributes content, achieving precise reach based on user location, occupation, and equipment type. It collects user feedback, continuously optimizes recommendation algorithms, and improves the accuracy and effectiveness of recommendations. It deeply analyzes the operational data of self-media platforms, assesses content popularity and user feedback, providing a basis for adjusting content strategies, predicting future trends in user interests, and offering forward-looking content creation suggestions to creators. Through data analysis, it discovers potential user needs and market opportunities, supporting the commercial monetization of self-media platforms.

[0012] In some specific implementations of the first aspect, the data acquisition and integration module implements the following steps:

[0013] Step 1: Set up the data acquisition interface and protocol, and connect it with the CNC equipment and the self-media platform;

[0014] Step 2: Collect data in real time and preprocess it, including data cleaning and format conversion;

[0015] Step 3: Build user interest profiles and extract interest preferences by analyzing user behavior data through algorithms.

[0016] The module uses IoT technologies, such as MQTT and CoAP protocols, to collect real-time operating data from the CNC equipment, including but not limited to equipment status (such as running, stopping, fault, etc.), operating parameters (such as speed, temperature, pressure, etc.), and fault warning information (such as abnormal vibration, overheating alarm, etc.).

[0017] The collected data undergoes cleaning, noise reduction, and standardization to improve its accuracy and reliability. This process may include filling in missing values, detecting and handling outliers, and converting data formats.

[0018] Meanwhile, this module integrates user behavior data from self-media platforms, such as browsing history, click behavior, dwell time, likes, comments, and shares, to build user interest profiles, including user interests, areas of interest, and active periods.

[0019] In some specific implementations of the first aspect, the content creation and management module is implemented through the following steps:

[0020] Step 1: Develop content creation tools, including a text editor and a video editor;

[0021] Step 2: Link data with CNC equipment to automatically generate a material library based on equipment status, technical cases, etc.

[0022] Step 3: Set content review rules, including but not limited to sensitive word filtering and copyright detection, to ensure content quality;

[0023] Step 4: Provide a template library for creators to quickly select and edit content.

[0024] It provides content creation tools, such as text editors, video editors, and live streaming tools, to support CNC professionals in creating self-media content.

[0025] By linking data with CNC equipment, a material library is automatically generated based on equipment status, technical cases, troubleshooting, and other content, including images, video clips, and documents, for creators to quickly select and edit.

[0026] It has content review and management functions, such as sensitive word filtering, copyright detection, and content quality assessment, to ensure that the quality of published content complies with platform standards.

[0027] It includes a template library, providing a variety of preset templates for self-media content such as text, images, videos, and live streams, simplifying the creation process and improving creation efficiency.

[0028] In some specific implementations of the first aspect, the intelligent recommendation and distribution module is implemented through the following steps:

[0029] Step 1: Develop an intelligent recommendation algorithm that uses collaborative filtering and deep learning to make recommendations based on user interest profiles;

[0030] Step 2: Distribute content based on user dimensions (region, occupation, device type, etc.);

[0031] Step 3: Set up a user feedback mechanism, collect and analyze user feedback to optimize the recommendation algorithm.

[0032] Based on user interest profiles and CNC equipment data, machine learning algorithms (such as collaborative filtering and deep learning) are used to intelligently recommend self-media content.

[0033] Based on factors such as user location, occupation, and equipment type, content can be accurately distributed, such as recommending equipment troubleshooting solutions for specific industries to relevant practitioners.

[0034] It includes a user feedback mechanism to collect user feedback on recommended content (such as likes, comments, and number of shares), and dynamically adjusts the recommendation algorithm based on the feedback to improve the accuracy of recommendations and user satisfaction.

[0035] In some specific implementations of the first aspect, the data analysis and optimization module is implemented using the following steps:

[0036] Step 1: Develop data analysis tools to conduct in-depth mining and analysis of operational data;

[0037] Step 2: Generate optimization suggestions based on the analysis results, such as recommending popular topics and adjusting the posting time;

[0038] Step 3: Develop a trend prediction model to predict future trends in user interests using historical and current data.

[0039] Conduct in-depth analysis of the operational data of self-media platforms, including key indicators such as content views, likes, comments, and shares, to assess the popularity of the content and user feedback.

[0040] Based on the analysis results, optimization suggestions are generated, such as recommending trending topics, adjusting content publication time, and optimizing titles and covers, to guide creators in adjusting their content strategies.

[0041] It includes a trend prediction function that uses historical and current data, time series analysis, machine learning models and other technologies to predict the changing trends of user interests over a period of time, providing creators with forward-looking content creation suggestions.

[0042] Secondly, a method for intelligent management and content recommendation of CNC self-media includes the following steps:

[0043] S1: Collects CNC equipment data and user behavior data through the data acquisition and integration module;

[0044] S2: Use the content creation and management module to create and manage self-media content;

[0045] S3: Intelligent recommendation and distribution module for self-media content;

[0046] S4: Utilize the data analysis and optimization module to analyze operational data, generate optimization suggestions, and adjust content strategies and recommendation algorithms.

[0047] Thirdly, a numerical control self-media intelligent management and content recommendation system construction device, the device including: processor and memory;

[0048] The memory is used to store one or more program instructions;

[0049] A processor is used to run one or more program instructions to execute the steps of the above-described method for intelligent management and content recommendation of CNC self-media.

[0050] Fourthly, a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the aforementioned numerical control self-media intelligent management and content recommendation method.

[0051] Fifthly, a computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method for intelligent management and content recommendation of CNC self-media.

[0052] The beneficial effects of this invention are as follows:

[0053] 1. The system's convenient creation tools and automatically generated resource library enable creators to quickly generate high-quality self-media content without spending a lot of time and effort collecting and organizing materials. Meanwhile, the system's built-in content review and management functions ensure the compliance and high quality of published content, effectively avoiding disputes and negative impacts caused by content issues.

[0054] 2. Based on user interest profiles and CNC equipment data, the system employs advanced machine learning algorithms for intelligent recommendations, enabling personalized content display. Users can receive highly relevant content recommendations based on their interests and needs, thereby improving their reading experience and satisfaction. Furthermore, the system accurately distributes content based on factors such as user location, occupation, and equipment type, ensuring that content reaches the most relevant audience, further enhancing the content's dissemination effect and influence.

[0055] 3. By conducting in-depth analysis of operational data from self-media platforms, the system can assess content popularity and user feedback, providing creators with valuable optimization suggestions. These suggestions not only help creators adjust their content strategies but also optimize the recommendation algorithm, further enhancing the system's intelligence and user experience. Simultaneously, the system can predict future trends in user interests, providing creators with forward-looking creative suggestions, thus enabling them to plan ahead and seize market opportunities. The implementation of these functions makes the operational strategies of self-media platforms more scientific and rational, significantly improving user activity. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the internal workings of the system of the present invention.

[0057] Figure 2 This is a schematic diagram of the method flow of the present invention.

[0058] Figure 3 This is a block diagram of the electronic device of the present invention. Detailed Implementation

[0059] 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.

[0060] like Figures 1 to 3 The CNC self-media intelligent management and content recommendation system shown includes: a data acquisition and integration module, used to collect operational data from CNC equipment, including equipment status, working parameters, and fault warning information, and upload it to the system center in real time via IoT technology; it also integrates user behavior data from self-media platforms to construct user interest profiles; a content creation and management module, providing content creation tools to support CNC professionals in creating self-media content, and associating it with CNC equipment data to automatically generate a material library based on equipment status, technical cases, fault solutions, etc.; it also has content review and management functions to ensure the quality of published content; an intelligent recommendation and distribution module, which uses machine learning algorithms to intelligently recommend self-media content based on user interest profiles and CNC equipment data; and achieves accurate content distribution based on user location, occupation, equipment type, and other dimensions; and a data analysis and optimization module, which conducts in-depth analysis of the self-media platform's operational data, evaluates the popularity of content and user feedback, and generates optimization suggestions based on the analysis results to guide creators in adjusting content strategies and optimizing recommendation algorithms.

[0061] It is a comprehensive platform integrating data collection, content creation, intelligent recommendation, and data analysis. The system first collects key operational data in real time from CNC equipment, covering equipment status, operating parameters, and fault warning information, and then uses IoT technology to upload this data to the system center instantly. Simultaneously, the system also integrates user behavior data from self-media platforms, such as browsing history and interaction patterns, to build precise user interest profiles.

[0062] In terms of content creation, the system provides CNC professionals with convenient creation tools, enabling them to easily create self-media content. More importantly, the system achieves deep integration with CNC equipment data, automatically generating a rich resource library containing equipment status, technical cases, troubleshooting, and other information, greatly improving the efficiency and richness of content creation. Furthermore, the system has comprehensive content review and management functions, ensuring that all published content complies with platform specifications and high-quality standards.

[0063] The intelligent recommendation and distribution module is one of the core components of this system. Based on user interest profiles and CNC equipment data, it uses advanced machine learning algorithms to intelligently recommend self-media content, achieving personalized content display. Simultaneously, the system also performs precise distribution based on multiple dimensions such as user location, occupation, and device type, ensuring that content reaches the most relevant audience.

[0064] Finally, the data analysis and optimization module conducted a comprehensive and in-depth analysis of the self-media platform's operational data. By evaluating content popularity and user feedback, it provided creators with valuable optimization suggestions. These suggestions not only help creators adjust their content strategies but also optimize the recommendation algorithm, further enhancing the system's intelligence and user experience.

[0065] A CNC self-media intelligent management and content recommendation system includes the following steps for its data acquisition and integration module: Step 1: Set up the data acquisition interface and protocol to connect with the CNC equipment and the self-media platform; Step 2: Collect data in real time and perform preprocessing, including data cleaning and format conversion; Step 3: Build user interest profiles and extract interest preferences by analyzing user behavior data through algorithms.

[0066] A CNC self-media intelligent management and content recommendation system includes the following steps for implementing the content creation and management module: Step 1: Develop content creation tools, including a text editor and a video editor; Step 2: Associate with CNC equipment data to automatically generate a material library based on equipment status, technical cases, etc.; Step 3: Set content review rules, including but not limited to sensitive word filtering and copyright detection, to ensure content quality; Step 4: Provide a template library for creators to quickly select and edit content.

[0067] The intelligent recommendation and distribution module is implemented in the following steps: Step 1: Develop an intelligent recommendation algorithm, using collaborative filtering and deep learning to make recommendations based on user interest profiles; Step 2: Distribute content according to user dimensions (region, occupation, device type, etc.); Step 3: Set up a user feedback mechanism to collect and analyze user feedback to optimize the recommendation algorithm.

[0068] The data analysis and optimization module is implemented in the following steps: Step 1: Develop data analysis tools to deeply mine and analyze operational data; Step 2: Generate optimization suggestions based on the analysis results, such as recommending popular topics and adjusting the release time; Step 3: Develop a trend prediction model to predict future trends in user interests using historical and current data.

[0069] A method for intelligent management and content recommendation of CNC self-media includes the following steps: S1: Collecting CNC equipment data and user behavior data through a data acquisition and integration module; S2: Creating and managing self-media content using a content creation and management module; S3: Intelligently recommending and accurately distributing self-media content through an intelligent recommendation and distribution module; S4: Analyzing operational data using a data analysis and optimization module, generating optimization suggestions, and adjusting content strategies and recommendation algorithms.

[0070] A device for constructing a CNC self-media intelligent management and content recommendation system includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of the aforementioned CNC self-media intelligent management and content recommendation method.

[0071] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for intelligent management and content recommendation of CNC self-media.

[0072] A computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method for intelligent management and content recommendation of CNC self-media.

[0073] Example 1: Construction of a CNC self-media intelligent management and content recommendation system.

[0074] Hardware Basics

[0075] Processor: Employs a high-performance multi-core processor to meet the system's high requirements for data processing and computation.

[0076] Storage: Equipped with large-capacity RAM and solid-state drives to store system programs and temporary data during runtime, while ensuring fast data read and write speeds.

[0077] Software Architecture

[0078] Operating System: Choose a stable operating system that supports multitasking, such as Linux or Windows Server, to provide a stable operating environment for the system.

[0079] Programming Language: Use efficient and easy-to-maintain programming languages ​​such as Java or Python for system development.

[0080] Database: Relational databases (such as MySQL) or non-relational databases (such as MongoDB) are used to store and manage data such as CNC equipment data, user behavior data, and social media content.

[0081] Module implementation

[0082] Data acquisition and integration module:

[0083] Set up data acquisition interfaces and protocols to interface with CNC equipment and self-media platforms to ensure real-time data acquisition.

[0084] Data is collected in real time and preprocessed, including data cleaning, format conversion, and deduplication.

[0085] We use machine learning algorithms to analyze user behavior data, build user interest profiles, and extract user interest preferences.

[0086] Content creation and management module:

[0087] We develop text editors, video editors, and other content creation tools to help CNC professionals easily create self-media content.

[0088] By linking data with CNC equipment, a material library based on equipment status, technical cases, and other content can be automatically generated, improving the efficiency and richness of content creation.

[0089] Set content review rules, including sensitive word filtering and copyright detection, to ensure the quality of published content.

[0090] It provides a template library, allowing creators to quickly select and edit content, thus lowering the barrier to entry for creators.

[0091] Intelligent recommendation and distribution module:

[0092] Develop intelligent recommendation algorithms, employing collaborative filtering, deep learning, and other algorithms to provide personalized recommendations based on user interest profiles.

[0093] Content is distributed based on factors such as user location, occupation, and device type to ensure that the content reaches the most relevant audience.

[0094] Establish a user feedback mechanism to collect and analyze user feedback, which will be used to optimize the recommendation algorithm and improve the accuracy and satisfaction of recommendations.

[0095] Data Analysis and Optimization Module:

[0096] Develop data analytics tools to deeply mine and analyze operational data, including metrics such as content popularity and user activity.

[0097] Based on the analysis results, optimization suggestions are generated, such as recommending trending topics and adjusting the posting time, to guide creators in adjusting their content strategies.

[0098] Develop trend prediction models to predict future trends in user interests using historical and current data, providing creators with forward-looking creative suggestions.

[0099] II. System Application

[0100] Data collection and integration

[0101] The system successfully integrated with CNC equipment and self-media platforms, collecting real-time operational data from the CNC equipment and user behavior data. Through preprocessing and cleaning, the data was effectively integrated into the system center, providing a solid foundation for subsequent content creation and intelligent recommendation.

[0102] Content creation and management

[0103] CNC professionals have easily created a large amount of self-media content related to CNC equipment using the system's provided creation tools and an automatically generated resource library. At the same time, the system rigorously reviews and manages the published content to ensure its quality and compliance.

[0104] Intelligent recommendation and distribution

[0105] The system intelligently recommends and precisely distributes self-media content based on user interest profiles and CNC equipment data. User feedback shows that the recommended content highly matches their interests and needs, greatly improving user satisfaction and engagement.

[0106] Data Analysis and Optimization

[0107] The system conducted a comprehensive and in-depth analysis of operational data, providing creators with valuable optimization suggestions. Based on these suggestions, creators adjusted their content strategies and optimized parameters such as release time, further improving content popularity and user engagement.

[0108] III. Implementation Results

[0109] By building and applying this intelligent management and content recommendation system for CNC self-media, CNC professionals can create and manage self-media content more efficiently, while users can receive more personalized and accurate content recommendations. The system's implementation not only improves the efficiency and richness of content creation but also optimizes the user's reading experience, injecting new vitality into the development of CNC self-media.

[0110] 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 numerically controlled self-media intelligent management and content recommendation system, characterized in that, include: The data acquisition and integration module is used to collect operational data from CNC equipment, including equipment status, working parameters, and fault warning information, and upload it to the system center in real time via IoT technology; at the same time, it integrates user behavior data from self-media platforms to build user interest profiles. The content creation and management module provides content creation tools to support CNC professionals in creating self-media content and links it with CNC equipment data to automatically generate a material library based on equipment status, technical cases, troubleshooting, etc. It also has content review and management functions to ensure the quality of published content. The intelligent recommendation and distribution module uses machine learning algorithms to intelligently recommend self-media content based on user interest profiles and CNC equipment data; and achieves accurate content distribution based on user's region, occupation, equipment type, and other dimensions. The data analysis and optimization module conducts in-depth analysis of the operational data of the self-media platform, evaluates the popularity of content and user feedback, and generates optimization suggestions based on the analysis results to guide creators in adjusting content strategies and optimizing recommendation algorithms.

2. The CNC self-media intelligent management and content recommendation system according to claim 1, characterized in that: The data acquisition and integration module is implemented in the following steps: Step 1: Set up the data acquisition interface and protocol, and connect it with the CNC equipment and the self-media platform; Step 2: Collect data in real time and preprocess it, including data cleaning and format conversion; Step 3: Build user interest profiles and extract interest preferences by analyzing user behavior data through algorithms.

3. The CNC self-media intelligent management and content recommendation system according to claim 1, characterized in that: The content creation and management module is implemented through the following steps: Step 1: Develop content creation tools, including a text editor and a video editor; Step 2: Link data with CNC equipment to automatically generate a material library based on equipment status, technical cases, etc. Step 3: Set content review rules, including but not limited to sensitive word filtering and copyright detection, to ensure content quality; Step 4: Provide a template library for creators to quickly select and edit content.

4. The CNC self-media intelligent management and content recommendation system according to claim 1, characterized in that: The intelligent recommendation and distribution module is implemented in the following steps: Step 1: Develop an intelligent recommendation algorithm that uses collaborative filtering and deep learning to make recommendations based on user interest profiles; Step 2: Distribute content based on user dimensions (region, occupation, device type, etc.); Step 3: Set up a user feedback mechanism, collect and analyze user feedback to optimize the recommendation algorithm.

5. The CNC self-media intelligent management and content recommendation system according to claim 1, characterized in that: The data analysis and optimization module is implemented in the following steps: Step 1: Develop data analysis tools to conduct in-depth mining and analysis of operational data; Step 2: Generate optimization suggestions based on the analysis results, such as recommending popular topics and adjusting the posting time; Step 3: Develop a trend prediction model to predict future trends in user interests using historical and current data.

6. A method for intelligent management and content recommendation of CNC self-media, characterized in that, Includes the following steps: S1: Collects CNC equipment data and user behavior data through the data acquisition and integration module; S2: Use the content creation and management module to create and manage self-media content; S3: Intelligent recommendation and distribution module for self-media content; S4: Utilize the data analysis and optimization module to analyze operational data, generate optimization suggestions, and adjust content strategies and recommendation algorithms.

7. A CNC self-media intelligent management and content recommendation system construction device, characterized in that: The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions to perform the steps of the numerical control self-media intelligent management and content recommendation method as described in claim 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the numerical control self-media intelligent management and content recommendation method as described in claim 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the numerical control self-media intelligent management and content recommendation method as described in claim 6.

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