Algorithm-based mining system for community service-related big data

Abstract

Provided is an algorithm-based mining system for community service-related big data, the system comprising: a data acquisition module, a data storage module, a data processing module, an algorithm-based mining module, and a result output module; the data acquisition module is configured for acquiring data and information generated during a community service; the data storage module is configured for storing the acquired data and information to a database; the data processing module is configured for cleaning, integrating, and normalizing the stored data and information; the algorithm-based mining module is configured for performing deep mining on the processed data, so as to identify a user requirement; the result output module is configured for presenting a visualized mining result to community administrators and service providers.

Description

A community service big data algorithm mining system TECHNICAL FIELD

[0001] The present application belongs to the field of community service, and specifically relates to a community service big data algorithm mining system. BACKGROUND

[0002] With the rapid advancement of urbanization and the rapid development of Internet technology, community services, as an important part of urban management and residents' life, are facing unprecedented challenges and opportunities. The growing demand for community services has prompted us to continuously explore and innovate service models to better meet the diverse and personalized needs of residents. However, in this process, the community service field has accumulated a large amount of data, which covers user information, service records, community activities and other aspects, and is an important resource for understanding community operation, grasping residents' needs and optimizing service strategies.

[0003] However, existing community service systems often struggle to effectively cope with the challenges brought by big data. On the one hand, data comes from a wide range of sources and has various formats, making data collection, integration and storage complex and difficult; on the other hand, data quality is uneven, with a large amount of redundant, missing and erroneous data, directly affecting subsequent data analysis and mining results. In addition, traditional data analysis methods are not up to the task when dealing with massive data, making it difficult to extract valuable information and patterns from them, resulting in a lack of scientific basis for service decisions, low service efficiency and poor user experience.

[0004] SUMMARY

[0005] To solve the above problems, the present application proposes a community service big data algorithm mining system, which includes a data collection module, a data storage module, a data processing module, an algorithm mining module and a result output module. Through the collaborative work of each module, efficient analysis and processing of community service big data are realized, and then accurate service strategies are provided.

[0006] The technical solution of the present application is as follows:

[0007] A community service big data algorithm mining system, the system comprising: a data collection module, a data storage module, a data processing module, an algorithm mining module and a result output module;

[0008] The data collection module is used to collect data information in community services.

[0009] The data storage module is used to store the collected data information into a database.

[0010] The data processing module is used to clean, integrate and standardize the stored data information.

[0011] The algorithm mining module is used for deep mining of the processed data to identify user needs;

[0012] The result output module is used for presenting the mining results in a visual form to community managers and service providers.

[0013] Further, the data information in the community service includes user information, service records, and community activities;

[0014] The user information includes age, gender, and interest preferences;

[0015] The service records include service types, service times, and service evaluations;

[0016] The community activities include activity types, participant numbers, and activity effects.

[0017] Further, the data collection module includes:

[0018] A data collection platform collects data information in the community service through multiple channels;

[0019] A data verification and preprocessing platform is used for preliminary quality inspection and preprocessing of the collected data information.

[0020] Further, the data storage module includes:

[0021] A distributed storage system is used for storing data information according to a predetermined directory structure and data model;

[0022] A data indexing mechanism is used for constructing data indexes to improve the speed and efficiency of data retrieval.

[0023] A data backup and recovery system is used for regularly backing up data in the distributed storage system to a secure storage medium and developing a disaster recovery plan to ensure quick recovery in case of data loss or system failure.

[0024] Further, the data processing module includes:

[0025] A data cleaner is used for cleaning errors, missing values, and outliers in data information, identifying and correcting logical errors and format inconsistencies in data;

[0026] A data integrator is used for integrating data information from different data sources according to business logic, eliminating data redundancy and conflicts, and forming a unified format data set;

[0027] A data standardizer is used for encoding, classifying, and formatting data sets.

[0028] Further, the algorithm mining module includes:

[0029] Algorithm library for integrating various machine learning algorithms to select appropriate algorithms for mining according to business needs;

[0030] Model training and optimization platform for algorithm training, optimizing model parameters, improving mining accuracy and efficiency, and evaluating model performance;

[0031] Deep mining engine platform for trained models to perform deep mining on processed data to discover user demand patterns, service hotspots and community activities.

[0032] Further, the result output module comprises:

[0033] Visualization tool for displaying mining results in various forms;

[0034] Report generator for automatically generating detailed reports including data overview, mining results and recommended strategies based on mining results.

[0035] Further, the visualization tool also supports custom report templates and interactive query functions.

[0036] Compared with the prior art, the present application has the following advantages:

[0037] The community service big data algorithm mining system of the present application can significantly improve the data processing capability and intelligent level of community services, helping community managers and service providers accurately grasp community needs, optimize resource allocation, and improve service efficiency and quality. BRIEF DESCRIPTION OF DRAWINGS

[0038] The accompanying drawings generally illustrate various embodiments of the present application and, together with the description given, serve to explain the principles of the present application. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or similar parts. Such embodiments are illustrative rather than restrictive and are intended to provide examples of the present device or method. In the drawings:

[0039] Figure 1 shows a schematic diagram of the framework of the present application. DETAILED DESCRIPTION

[0040] It should be noted that the embodiments and features in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0041] The present application provides a community service big data algorithm mining system, which aims to help community managers and service providers better understand user needs, optimize service resource allocation, and improve community service quality and resident satisfaction through efficient data collection, storage, processing, mining, and result display. The following is a specific embodiment of the present application:

[0042] 1. System architecture and module functions

[0043] The present system mainly consists of five core modules: data collection module, data storage module, data processing module, algorithm mining module, and result output module.

[0044] 1.1 Data collection module

[0045] The data collection module is responsible for collecting various types of data information in community services, including:

[0046] Data collection platform: Use IoT devices, mobile applications, web forms, and other channels to collect user information, service records, and community activity data in real time or periodically. For example, obtain user access records through intelligent access control systems and collect user feedback on participating activities through community APPs.

[0047] Data verification and preprocessing platform: Perform preliminary quality checks on collected data, such as data integrity and format correctness, and perform simple preprocessing, such as removing duplicate data and filling missing values, to ensure data quality meets subsequent processing requirements.

[0048] 1.2 Data storage module

[0049] The data storage module is responsible for safely and efficiently storing collected data:

[0050] Distributed storage system: Use Hadoop HDFS or similar technologies to achieve high availability and scalability of data storage, design reasonable directory structure and data model according to business logic, and facilitate data management and access.

[0051] Data indexing mechanism: Establish an efficient data index, such as using Elasticsearch search engine, to improve data query speed and accuracy.

[0052] Data backup and recovery system: Regularly backup data to cloud storage or physical disks, set automatic backup strategies and disaster recovery plans, and ensure data security.

[0053] 1.3 Data processing module

[0054] The data processing module is responsible for cleaning, integrating, and standardizing stored data:

[0055] Data Cleaner: Use ETL tools (such as Talend, Pentaho, etc.) or custom scripts to identify and correct errors, missing values, and outliers in the data, ensuring data quality.

[0056] Data Integrator: Integrate data from different sources according to business rules, such as eliminating duplicate records through primary key matching, ensuring data consistency.

[0057] Data Standardizer: Encode data (such as using international standard coding systems), classify data (such as classifying by service type), and format data (such as uniform date format) to facilitate subsequent analysis.

[0058] 1.4 Algorithm Mining Module

[0059] The algorithm mining module is the core of the system, responsible for deep analysis of data and mining valuable information:

[0060] Algorithm Library: Integrate decision trees, random forests, neural networks, and other machine learning algorithms, and select the most suitable algorithm according to specific needs.

[0061] Model Training and Optimization Platform: Use TensorFlow, PyTorch, and other frameworks to train selected algorithms, improve model performance through cross-validation and parameter tuning.

[0062] Deep Mining Engine Platform: Deploy trained models to perform deep analysis on processed data, identify user demand trends, service hotspots, community activity participation, and other key information.

[0063] 1.5 Results Output Module

[0064] The results output module is responsible for presenting the mining results to users in an intuitive and easy-to-understand manner:

[0065] Visualization Tools: Use Tableau, PowerBI, and other tools to generate charts, dashboards, and other visualizations to display data distribution, trend changes, and support custom report templates and interactive queries to meet the individual needs of different users.

[0066] Report Generator: Based on the mining results, automatically generate detailed reports containing data overview, key findings, and recommended strategies in PDF, HTML, and other formats for sharing and archiving.

[0067] 2. System Implementation Process

[0068] (1) Data Preparation Phase: Configure data collection platforms, determine data collection scope and frequency, and start data collection work.

[0069] (2) Data storage and preprocessing: Store the collected data into a distributed storage system, perform data cleaning, integration and standardization operations, and prepare high-quality data sets.

[0070] (3) Model construction and training: Select appropriate machine learning algorithms, use processed data for model training, and continuously optimize model parameters until the desired effect is achieved.

[0071] (4) Deep mining and analysis: Use the trained model to deeply mine the data and reveal hidden patterns and associations.

[0072] (5) Result display and application: Present the mining results to community managers and service providers in an intuitive form through visualization tools and report generators, and provide support for their decision-making.

[0073] 3. System advantages

[0074] The present application realizes fast response and deep insight of community service data through integrated data processing and analysis process, which helps to improve the intelligent level of community service, enhance the scientificity and pertinence of community management, and promote the harmonious development of community.

[0075] In summary, the community service big data algorithm mining system of the present application provides comprehensive and in-depth data support for community service through fine module design and efficient data processing mechanism, and has high practical value and promotion prospect.

[0076] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can make equivalent replacement or change within the technical range disclosed by the present application according to the technical solution and inventive concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. A community service big data algorithm mining system, characterized in that, The system comprises a data collection module, a data storage module, a data processing module, an algorithm mining module, and a result output module. The data collection module is used to collect data information in community services. The data storage module is used to store the collected data information in a database. The data processing module is used to clean, integrate, and standardize the stored data information. The algorithm mining module is used to deeply mine the processed data to identify user needs. The result output module is used to present the mining results in a visual form to community managers and service providers.

2. The community service big data algorithm mining system of claim 1, wherein, The data information in community services includes user information, service records, and community activities. User information includes age, gender, and interest preferences. Service records include service types, service times, and service evaluations. Community activities include activity types, participant numbers, and activity effects.

3. The community service big data algorithm mining system of claim 1, wherein, The data collection module comprises: A data collection platform that collects data information in community services through multiple channels. A data verification and preprocessing platform that performs preliminary quality checks and preprocessing on the collected data information.

4. The community service big data algorithm mining system of claim 1, wherein, The data storage module comprises: A distributed storage system that stores data information according to a predetermined directory structure and data model. A data indexing mechanism that builds data indexes to improve data retrieval speed and efficiency. A data backup and recovery system that regularly backs up data in the distributed storage system to secure storage media and develops disaster recovery plans to ensure quick recovery in case of data loss or system failure.

5. The community service big data algorithm mining system of claim 1, wherein, The data processing module comprises: A data cleaner that cleans errors, missing values, and outliers in data information, identifies and corrects logical errors and format inconsistencies in data. A data integrator that integrates data information from different data sources according to business logic, eliminates data redundancy and conflicts, and forms a unified format data set. A data standardizer that encodes, classifies, and formats data sets.

6. The community service big data algorithm mining system of claim 1, wherein, The algorithm mining module comprises: An algorithm library that integrates multiple machine learning algorithms and selects appropriate algorithms for mining based on business needs. A model training and optimization platform that trains algorithms, optimizes model parameters, improves mining accuracy and efficiency, and evaluates model performance. A deep mining engine platform that uses trained models to deeply mine processed data to discover user demand patterns, service hotspots, and community activities.

7. The community service big data algorithm mining system of claim 1, wherein, The result output module comprises: A visualization tool that displays mining results in multiple forms. A report generator that automatically generates detailed reports based on mining results, including data overview, mining results, and suggested strategies.

8. The community service big data algorithm mining system of claim 1, wherein, The visualization tool also supports custom report templates and interactive query functions.

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