Engineering management promotion service system based on Internet

Abstract

The invention belongs to the technical field of Internet engineering services, and discloses an engineering management promotion service system based on the Internet. The data acquisition module is responsible for capturing and integrating data required by engineering management from various sources; the data processing module is used for receiving the original data from the data acquisition layer and carrying out cleaning, integration, conversion and storage processing; the business logic module is responsible for realizing specific business processes and rules of project management; and the application service module meets diversified requirements of project management personnel. Through the well-designed interface layout, the operation process conforming to the habits of the user and rich interaction elements, it is ensured that the user can easily and happily interact with the system, the operation efficiency of the user is improved, the satisfaction degree and loyalty of the user are enhanced, and the user experience is improved through continuous optimization and iteration. The system can continuously adapt to the requirements and changes of the user, and provides more personalized experience.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet engineering services, and specifically is an Internet-based engineering management improvement service system. Background Art

[0002] During the project implementation process, the challenges faced are becoming increasingly complex and diverse. These problems are not only related to the success of the project, but also directly affect the economic benefits and market competitiveness of the enterprise. Irrational resource allocation is one of the common problems. It may be caused by inaccurate estimation of project demand, omissions in supply chain management or untimely on-site scheduling, resulting in excess or shortage of resources, which in turn affects the progress and quality of the project. Construction progress delays may be caused by a variety of factors, including weather changes, design changes, delayed material supply, insufficient labor or mismatched skills, etc. These problems are intertwined, making it difficult to control the project timeline. Improper cost control is another major problem. It may be caused by negligence in budget formulation, inaccurate cost estimates, waste in the implementation process or unauthorized In the case of cost overruns, the profitability of the project will be seriously affected and may even lead to project failure. The traditional engineering management method is highly dependent on manual operations and paper documents. This model may be able to cope with small-scale and simple projects, but with the continuous expansion of engineering scale and the significant increase in complexity, its limitations are becoming more and more obvious. Manual operations are not only inefficient and prone to errors, but also difficult to cope with the rapid processing and real-time analysis of large amounts of data. Paper documents are easy to lose, difficult to share, and difficult to trace. They cannot meet the high requirements of modern engineering projects for information accuracy and timeliness. Therefore, many engineering companies have begun to seek changes and urgently need a system that can improve management efficiency, reduce management costs, and enhance management capabilities. Summary of the invention

[0003] The purpose of the present invention is to provide an Internet-based engineering management improvement service system to solve the problems raised in the above-mentioned background technology.

[0004] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an Internet-based engineering management improvement service system, the service system comprising:

[0005] Data acquisition module: responsible for capturing and integrating data required for engineering management from various sources;

[0006] Data processing module: receives raw data from the data acquisition layer and performs cleaning, integration, conversion and storage;

[0007] Business logic module: responsible for implementing specific business processes and rules for engineering management;

[0008] Application service module: meet the diverse needs of engineering managers;

[0009] User interaction layer module: responsible for presenting the functions provided by the application service layer to users in an intuitive and friendly manner.

[0010] Preferably, the specific implementation steps of the data acquisition module include:

[0011] A1, Demand Analysis and Planning:

[0012] Clarify data requirements: According to the business requirements of the engineering management system, clarify the types of data that need to be collected;

[0013] Develop a data collection plan: Based on data requirements, develop a detailed data collection plan, including the time, frequency, method of collection, and the way data is stored and processed.

[0014] A2, data source identification and access:

[0015] Identify data sources: Identify devices, systems, and platforms as sources of data;

[0016] Access data source: Connect data source to data collection layer through API interface, database connection, file transfer, etc.

[0017] A3, Data collection implementation:

[0018] Configure collection tools: Configure corresponding data collection tools or software according to the characteristics of the data source;

[0019] Set collection rules: define data collection rules;

[0020] Execute collection tasks: According to the collection plan, start the data collection task, grab data from the data source, and temporarily store it in the local or cloud data buffer;

[0021] A4, Data verification and cleaning:

[0022] Data verification: Conduct preliminary verification of the collected data to check the completeness, accuracy and consistency of the data;

[0023] Data cleaning: remove duplicate data, correct erroneous data, and fill in missing data;

[0024] A5, Data Integration and Storage:

[0025] Data integration: Integrate the cleaned data according to a certain logical structure;

[0026] Data storage: storing the integrated data in a database, data warehouse or distributed storage system;

[0027] A6, Monitoring and Optimization:

[0028] Performance monitoring: real-time monitoring of the data collection process;

[0029] Optimization and adjustment: Based on the monitoring results, optimize and adjust the data collection plan, collection rules, storage strategy, etc.

[0030] Preferably, the specific implementation steps of the data processing module include:

[0031] B1, data reception and buffering:

[0032] Data reception: The data processing layer receives the original data stream from the data acquisition layer;

[0033] Data buffering: The data processing layer sets up a data buffer to temporarily store data to be processed;

[0034] B2, data cleaning:

[0035] Deduplication: Identify and delete duplicate data records to avoid bias in subsequent analysis;

[0036] Error detection and correction: Check the data for erroneous values, outliers, or data that does not conform to business rules, and correct or mark them;

[0037] Missing value processing: For missing data fields, fill in default values, interpolate, or delete records based on business needs and data characteristics;

[0038] B3, Data Integration and Standardization:

[0039] Data integration: Integrate data from different data sources and formats into a unified data model;

[0040] Data standardization: standardize the naming, format, and unit conversion of data fields;

[0041] B4, Data Conversion and Mapping:

[0042] Data conversion: perform conversion operations such as calculation, aggregation, and splitting on data according to business needs;

[0043] Data mapping: Establish a mapping relationship between data fields and business concepts to ensure that the business meaning of the data is clear and unambiguous;

[0044] B5, Distributed Processing and Storage:

[0045] Big data processing technology: Use Hadoop and Spark big data processing technologies to efficiently process massive data;

[0046] Distributed storage system: Use distributed storage systems such as HDFS, HBase, and Cassandra to store data on multiple nodes;

[0047] B6, Preliminary data analysis:

[0048] Data statistics: perform basic statistical analysis on data;

[0049] Trend forecasting: Use time series analysis and regression analysis methods to forecast data trends;

[0050] Data visualization: Visualize data through charts and dashboards;

[0051] B7, Quality Control and Monitoring:

[0052] Data quality monitoring: perform quality checks on processed data;

[0053] Performance monitoring: Real-time monitoring of the processing performance of the data processing layer, including processing speed and resource consumption.

[0054] Preferably, the specific implementation steps of the business logic module include:

[0055] C1, demand analysis and algorithm selection:

[0056] In-depth understanding of business needs: Analyze business needs;

[0057] Algorithm evaluation and selection: Evaluate and select appropriate algorithms based on business needs;

[0058] Algorithm feasibility analysis: Evaluate the feasibility of the selected algorithm under the existing system architecture and hardware conditions;

[0059] C2, Algorithm design and implementation: Design the specific implementation plan of the algorithm according to business needs, including the algorithm's logical flow, data structure selection, and parameter setting;

[0060] C3, business logic and algorithm integration:

[0061] Interface definition: define a clear interface for algorithm implementation;

[0062] Integration of business logic and algorithms: Integrate algorithms into business logic;

[0063] Data stream processing: design data stream processing flow;

[0064] C4, Testing and Verification:

[0065] Unit testing: Perform unit testing on algorithms and business logic to verify the correctness of the algorithms and the accuracy of the business logic;

[0066] Integration testing: Integrate the business logic layer with other layers;

[0067] Performance testing: Perform performance testing on the business logic layer containing the algorithm;

[0068] Algorithm effect evaluation: Evaluate the actual effect of the algorithm based on business needs;

[0069] C5, Maintenance and Optimization:

[0070] Algorithm update: As business develops and technology changes, the algorithm will be regularly evaluated and updated;

[0071] Performance monitoring: monitor the performance of the business logic layer;

[0072] Logging: records key information about algorithm execution and business logic processing;

[0073] Optimization and iteration: Continuously optimize business logic and algorithm implementation based on user feedback and system operation status.

[0074] Preferably, the specific implementation steps of the application service module include:

[0075] D1, Demand Analysis:

[0076] Understand business requirements: Have a deep understanding of business requirements, clarify the functions that the application service layer needs to support the business, and the specific implementation requirements of the functions;

[0077] Define service interface: Define the service interface that the application service layer needs to provide according to business requirements;

[0078] D2, Service Design and Implementation:

[0079] Service division: Divide complex business logic into multiple independent service modules, each module is responsible for processing a part of the business logic;

[0080] Interface definition: At the code level, define clear interfaces for each service module and clarify the input and output of the service;

[0081] Business logic implementation: Write specific business logic code according to business requirements and service division;

[0082] Service registration and discovery: In a microservice architecture, the application service layer contains multiple microservice instances;

[0083] D3, service test:

[0084] Unit testing: Write unit test code to verify key methods and functions in the application service layer;

[0085] Integration testing: Perform integration testing on the application service layer and other layers to verify whether the data interaction and business logic processing between the layers are smooth;

[0086] Performance testing: Perform performance testing on the application service layer to evaluate its processing capabilities and stability under high concurrency and large data volumes;

[0087] D4, service deployment and operation and maintenance:

[0088] Service deployment: deploy the compiled application service layer code to the production environment;

[0089] Monitoring and logging: In a production environment, a monitoring and logging system should be set up to monitor the running status and performance of the service;

[0090] Service upgrade and maintenance: With the development of business and the update of technology, the application service layer needs to be upgraded and maintained;

[0091] Preferably, the specific implementation steps of the user interaction layer module include:

[0092] E1, User Research and Demand Analysis:

[0093] Target user positioning: clarify the target user group of the system;

[0094] Requirements collection: Collect information about target users’ expectations, usage habits, and preferences for system functions through questionnaires, interviews, and user observations;

[0095] Demand analysis: organize and analyze the collected requirements to extract key user needs and pain points;

[0096] E2, User Interface Design:

[0097] Information architecture design: plan the information structure of the system, determine the main functional modules and page layout;

[0098] Prototyping: Use prototyping tools to draw user interface prototypes;

[0099] Visual design: Design interface visual elements that meet aesthetic standards based on brand image and user preferences;

[0100] E3, interactive logic design:

[0101] Operation process design: design the operation process for users to complete tasks based on user needs and business logic;

[0102] Interactive element design: Design various interactive elements to ensure that users can easily identify and interact with them;

[0103] Feedback mechanism design: provide immediate and clear feedback for user operations;

[0104] E4, User Experience Optimization:

[0105] Usability testing: Invite target users to participate in prototype testing, observe and record the usage process, collect feedback, and identify potential problems;

[0106] Iterative optimization: Iteratively optimize the interface design and interaction logic based on test results and user feedback;

[0107] Performance optimization: ensure that the user interaction layer has good response speed and stability, and reduce loading time and jamming;

[0108] E5, user feedback and continuous optimization.

[0109] Preferably, the specific steps of designing and implementing the algorithm in C2 include:

[0110] F1, Algorithm Design:

[0111] Select a pivot value: Select an element from the array to be sorted as the pivot value. There are many ways to select a pivot value, the most common ones are to select the first element, the last element, the middle element of the array, or randomly select an element.

[0112] Partition operation: rearrange the array, all elements smaller than the base value are placed in front of the base value, and all elements larger than the base value are placed behind the base value (the same number is placed on either side). After this partition is exited, the base value is in the middle of the array. This is called a partition operation.

[0113] Recursively sort subarrays: recursively sort the subarrays of elements less than the base value and the subarrays of elements greater than the base value;

[0114] F2, pseudo code:

[0115] F3, code implementation: convert the pseudocode in F2 into code in a specific programming language (such as Java);

[0116] F4, algorithm tuning: Optimize the performance of quick sort by choosing a more appropriate benchmark value, tail recursion optimization, and using insertion sort for small arrays.

[0117] Preferably, the specific steps of user feedback and continuous optimization in E5 include:

[0118] G1, collect user feedback: collect user feedback through user surveys, customer service feedback, user reviews and other channels;

[0119] G2, Data Analysis: Use data analysis tools to mine and analyze user behavior data to find out the hot spots and pain points of user usage;

[0120] G3 continuously optimizes the user interface and interaction logic based on user feedback and data analysis results.

[0121] Preferably, the specific steps of user feedback and continuous optimization in E5 include:

[0122] G1, collect user feedback: collect user feedback through user surveys, customer service feedback, user reviews and other channels;

[0123] G2, Data Analysis: Use data analysis tools to mine and analyze user behavior data to find out the hot spots and pain points of user usage;

[0124] G3 continuously optimizes the user interface and interaction logic based on user feedback and data analysis results.

[0125] The beneficial effects of the present invention are as follows:

[0126] 1. The present invention ensures that users can interact with the system easily and happily through a carefully designed interface layout, an operation process that conforms to user habits, and rich interactive elements. This not only improves the user's operating efficiency, but also enhances the user's satisfaction and loyalty. Through continuous optimization and iteration, the system can continuously adapt to user needs and changes and provide a more personalized experience.

[0127] 2. The present invention makes the system easier to use by enhancing the ease of use of the system and reducing the user's learning cost. Through an intuitive interface, clear prompts and immediate feedback, users can quickly understand the functions and operation processes of the system, reducing misoperation and confusion. This ease of use not only improves the user's work efficiency, but also reduces training and support costs, saving resources for the enterprise.

[0128] 3. The present invention realizes effective communication between users and the system by collecting user feedback and behavior data. This helps enterprises to understand users' needs and pain points in a timely manner, and provides an important basis for product optimization and improvement. At the same time, users can also express their opinions and suggestions through feedback channels, participate in the design and development process of products, and enhance users' sense of participation and belonging. This two-way communication mechanism helps to build closer user relationships and enhance the brand image and market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 This is a structural diagram of the service system module of the present invention;

[0130] Figure 2 This is the demand analysis and planning flow chart of the present invention. DETAILED DESCRIPTION

[0131] 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.

[0132] like Figure 1 to Figure 2 As shown, the embodiment of the present invention provides an Internet-based engineering management improvement service system, the service system includes:

[0133] Data acquisition module: responsible for capturing and integrating data required for engineering management from various sources;

[0134] Data processing module: receives raw data from the data acquisition layer and performs cleaning, integration, conversion and storage;

[0135] Business logic module: responsible for implementing specific business processes and rules for engineering management;

[0136] Application service module: meet the diverse needs of engineering managers;

[0137] User interaction layer module: responsible for presenting the functions provided by the application service layer to users in an intuitive and friendly manner.

[0138] Among them, the specific implementation steps of the data acquisition module include:

[0139] A1, Demand Analysis and Planning:

[0140] Clarify data requirements: Based on the business requirements of the project management system, clearly define the types of data that need to be collected; these data may include real-time environmental parameters of the construction site, the location and status of personnel and equipment, records of project progress and quality, and interaction data with partners or third-party systems.

[0141] Develop a data collection plan: Based on data requirements, develop a detailed data collection plan, including the time, frequency, method of collection, and the way data is stored and processed.

[0142] A2, data source identification and access:

[0143] Identify data sources: Identify devices, systems, and platforms as data sources. This includes IoT sensors (such as temperature sensors, pressure sensors, cameras, etc.), RFID tags, GPS trackers, ERP systems, CRM systems, etc.

[0144] Access to data sources: Connect data sources to the data collection layer through API interfaces, database connections, file transfers, etc.; details such as interface specifications and data transmission protocols need to be negotiated with the data source provider;

[0145] A3, Data collection implementation:

[0146] Configure collection tools: According to the characteristics of the data source, configure the corresponding data collection tools or software, such as data collectors, ETL (Extract, Transform, Load) tools, crawlers, etc.

[0147] Set collection rules: define the data collection rules, including which fields to collect, how to parse the data format, and how to handle abnormal data;

[0148] Execute collection tasks: According to the collection plan, start the data collection task, grab data from the data source, and temporarily store it in the local or cloud data buffer;

[0149] A4, Data verification and cleaning:

[0150] Data verification: Perform preliminary verification on the collected data to check the completeness, accuracy and consistency of the data, and mark or discard the data that does not meet the requirements;

[0151] Data cleaning: remove duplicate data, correct erroneous data, and fill in missing data to ensure data quality and availability;

[0152] A5, Data Integration and Storage:

[0153] Data integration: Integrate the cleaned data according to a certain logical structure to form a unified data format and storage specification;

[0154] Data storage: Storing the integrated data in a database, data warehouse or distributed storage system for subsequent data processing and analysis;

[0155] A6, Monitoring and Optimization:

[0156] Performance monitoring: Real-time monitoring of the data collection process, including collection speed, data quality, system load, etc., to ensure the stability and reliability of data collection;

[0157] Optimization and adjustment: Based on the monitoring results, optimize and adjust the data collection plan, collection rules, storage strategy, etc. to improve the efficiency and accuracy of data collection.

[0158] Among them, the specific implementation steps of the data processing module include:

[0159] B1, data reception and buffering:

[0160] Data reception: The data processing layer receives the original data stream from the data collection layer. This data may arrive through various methods such as API calls, message queues, and file transfers.

[0161] Data buffering: In order to alleviate the mismatch between data arrival rate and processing rate, the data processing layer sets up a data buffer (such as a memory queue, temporary database, etc.) to temporarily store data to be processed;

[0162] B2, data cleaning:

[0163] Deduplication: Identify and delete duplicate data records to avoid bias in subsequent analysis;

[0164] Error detection and correction: Check the data for erroneous values, outliers, or data that does not conform to business rules, and correct or mark them;

[0165] Missing value processing: For missing data fields, fill in default values, interpolate, or delete records based on business needs and data characteristics;

[0166] B3, Data Integration and Standardization:

[0167] Data integration: Integrate data from different data sources and formats into a unified data model to ensure data integrity and consistency;

[0168] Data standardization: standardize the naming, format, and unit conversion of data fields to make the data conform to the preset standards or specifications, which is convenient for subsequent analysis and comparison;

[0169] B4, Data Conversion and Mapping:

[0170] Data conversion: According to business needs, data is converted by calculation, aggregation, splitting and other operations to meet the needs of subsequent analysis;

[0171] Data mapping: Establish a mapping relationship between data fields and business concepts to ensure that the business meaning of the data is clear and unambiguous;

[0172] B5, Distributed Processing and Storage:

[0173] Big data processing technology: Use Hadoop and Spark big data processing technologies to efficiently process massive data; significantly improve the speed and scale of data processing through distributed computing, in-memory computing, etc.

[0174] Distributed storage system: Use distributed storage systems such as HDFS, HBase, and Cassandra to store data on multiple nodes to achieve data scalability, high availability, and fault tolerance;

[0175] B6, Preliminary data analysis:

[0176] Data statistics: perform basic statistical analysis on the data, such as sum, average, maximum, minimum, standard deviation, etc., to understand the overall distribution of the data;

[0177] Trend forecasting: Use time series analysis and regression analysis methods to forecast data trends and provide forward-looking support for business decisions;

[0178] Data visualization: Data is visualized in the form of charts and dashboards to help users understand the business meaning behind the data more intuitively;

[0179] B7, Quality Control and Monitoring:

[0180] Data quality monitoring: Perform quality checks on processed data to ensure data accuracy, completeness, and consistency;

[0181] Performance monitoring: Real-time monitoring of the processing performance of the data processing layer, including processing speed and resource consumption, to promptly identify and resolve potential performance bottlenecks.

[0182] Among them, the specific implementation steps of the business logic module include:

[0183] C1, demand analysis and algorithm selection:

[0184] In-depth understanding of business needs: Analyze business needs and clarify the problems that need to be solved and the goals that need to be achieved at the business logic layer;

[0185] Algorithm evaluation and selection: Evaluate and select appropriate algorithms based on business needs; the selection of algorithms should take into account their efficiency, accuracy, maintainability, and scalability; common algorithms include sorting algorithms, search algorithms, optimization algorithms (such as genetic algorithms, simulated annealing algorithms), machine learning algorithms (such as classification, regression, clustering), etc.

[0186] Algorithm feasibility analysis: Evaluate the feasibility of the selected algorithm under the existing system architecture and hardware conditions, including computing resources, memory consumption, response time, etc.;

[0187] C2, Algorithm design and implementation: Design the specific implementation plan of the algorithm according to business needs, including the algorithm's logical flow, data structure selection, and parameter setting;

[0188] C3, business logic and algorithm integration:

[0189] Interface definition: define a clear interface for the algorithm implementation so that the business logic layer can call the algorithm for data processing;

[0190] Integration of business logic and algorithm: Integrate the algorithm into the business logic to ensure that the business logic can correctly call the algorithm and process the results returned by the algorithm;

[0191] Data stream processing: Design data stream processing flow to ensure that data is correctly transmitted and processed between business logic and algorithms;

[0192] C4, Testing and Verification:

[0193] Unit testing: Perform unit testing on algorithms and business logic to verify the correctness of the algorithms and the accuracy of the business logic;

[0194] Integration testing: Integrate the business logic layer with other layers (such as presentation layer, data access layer, etc.) to verify the functionality and performance of the entire system;

[0195] Performance testing: Perform performance testing on the business logic layer containing the algorithm to evaluate its processing capacity and response time under different loads;

[0196] Algorithm effect evaluation: According to business needs, evaluate the actual effect of the algorithm, such as accuracy, recall rate, F1 score, etc., to ensure that the algorithm meets business needs;

[0197] C5, Maintenance and Optimization:

[0198] Algorithm update: As business develops and technology is updated, algorithms are regularly evaluated and updated to adapt to new business needs and improve system performance;

[0199] Performance monitoring: Monitor the performance of the business logic layer to promptly identify and resolve performance bottlenecks;

[0200] Logging: records key information about algorithm execution and business logic processing to facilitate problem tracking and troubleshooting;

[0201] Optimization and iteration: Based on user feedback and system operation status, we continuously optimize business logic and algorithm implementation to improve user experience and system performance.

[0202] Among them, the specific implementation steps of the application service module include:

[0203] D1, Demand Analysis:

[0204] Understand business requirements: Have a deep understanding of business requirements, clarify the functions that the application service layer needs to support the business, and the specific implementation requirements of the functions;

[0205] Define service interface: Based on business requirements, define the service interface that the application service layer needs to provide, including interface name, parameters, return value, etc. These interfaces will become the bridge for the presentation layer to call the application service layer;

[0206] D2, Service Design and Implementation:

[0207] Service division: Divide complex business logic into multiple independent service modules, each module is responsible for processing a part of the business logic; it helps to improve the readability, maintainability and scalability of the code;

[0208] Interface definition: At the code level, define clear interfaces for each service module and clarify the input and output of the service; interface definitions should follow certain specifications, such as the RESTful API specification, to facilitate integration and calls between different systems;

[0209] Business logic implementation: Write specific business logic code according to business needs and service divisions; in the coding process, attention should be paid to the standardization, readability and maintainability of the code; at the same time, full use should be made of technical means such as design patterns and exception handling to improve the quality and stability of the code;

[0210] Service registration and discovery: In a microservice architecture, the application service layer contains multiple microservice instances. In order to ensure mutual calls and load balancing between these microservices, service registration and discovery are required. This is usually achieved through a service registry (such as Eureka, Consul, etc.).

[0211] D3, service test:

[0212] Unit testing: Write unit test code to verify key methods and functions in the application service layer; unit testing should cover all possible input conditions to ensure the correctness and reliability of the service;

[0213] Integration testing: Perform integration testing on the application service layer and other layers (such as presentation layer, data access layer, etc.) to verify whether the data interaction and business logic processing between the layers are smooth;

[0214] Performance testing: Perform performance testing on the application service layer to evaluate its processing capabilities and stability under high concurrency and large data volumes; optimize and adjust services based on the test results;

[0215] D4, service deployment and operation and maintenance:

[0216] Service deployment: Deploy the compiled application service layer code to the production environment. During the deployment process, pay attention to the configuration and dependencies of the service to ensure that the service can start and run normally.

[0217] Monitoring and logging: In a production environment, a monitoring and logging system should be set up to monitor the running status and performance of the service. At the same time, log files should be checked and analyzed regularly to identify and solve problems in a timely manner.

[0218] Service upgrade and maintenance: With the development of business and the update of technology, the application service layer needs to be upgraded and maintained. During the upgrade and maintenance process, the smooth transition of services and the security of data should be ensured;

[0219] Among them, the specific implementation steps of the user interaction layer module include:

[0220] E1, User Research and Demand Analysis:

[0221] Target user positioning: Identify the target user groups of the system, including their age, occupation, skill level, usage scenarios, etc., so as to design a user interface that meets their needs and habits;

[0222] Requirements collection: Collect information about target users’ expectations, usage habits, and preferences for system functions through questionnaires, interviews, and user observations;

[0223] Demand analysis: Organize and analyze the collected requirements, extract key user needs and pain points, and provide guidance for subsequent design;

[0224] E2, User Interface Design:

[0225] Information architecture design: Plan the information structure of the system, determine the main functional modules and page layout, and ensure that the information hierarchy is clear and easy to understand;

[0226] Prototype design: Use prototyping tools (such as Sketch, Figma, Axure, etc.) to draw user interface prototypes, including page layout, control placement, interaction flow, etc., so as to communicate with the development team and the testing team;

[0227] Visual design: Design interface visual elements that meet aesthetic standards, such as colors, fonts, icons, pictures, etc., based on brand image and user preferences to ensure that the interface is beautiful and harmonious;

[0228] E3, interactive logic design:

[0229] Operation process design: Design the operation process for users to complete tasks based on user needs and business logic, ensuring that the process is concise, efficient, and in line with user habits;

[0230] Interactive element design: Design the style and behavior of various interactive elements (such as buttons, forms, drop-down menus, pop-ups, etc.) to ensure that users can easily identify and interact with them;

[0231] Feedback mechanism design: provide immediate and clear feedback (such as success prompts, error prompts, progress bars, etc.) for user operations to enhance user confidence and satisfaction;

[0232] E4, User Experience Optimization:

[0233] Usability testing: Invite target users to participate in prototype testing, observe and record the usage process, collect feedback, and identify potential problems;

[0234] Iterative optimization: Iteratively optimize the interface design and interaction logic based on test results and user feedback to improve user experience;

[0235] Performance optimization: ensure that the user interaction layer has good response speed and stability, reduce loading time and jamming, and improve user satisfaction;

[0236] E5, user feedback and continuous optimization:

[0237] Collect user feedback: Collect user feedback through user surveys, customer service feedback, user reviews and other channels to understand user satisfaction with the system and suggestions for improvement;

[0238] Data analysis: Use data analysis tools to mine and analyze user behavior data, identify user hot spots and pain points, and provide a basis for optimization;

[0239] Continuous optimization: Based on user feedback and data analysis results, we continuously optimize the user interface and interaction logic to improve user experience and system performance.

[0240] Among them, the specific steps of algorithm design and implementation in C2 include:

[0241] F1, Algorithm Design:

[0242] Select a pivot value: Select an element from the array to be sorted as the pivot value. There are many ways to select a pivot value, the most common ones are to select the first element, the last element, the middle element of the array, or randomly select an element.

[0243] Partition operation: rearrange the array, put all elements smaller than the base value in front of the base value, and put all elements larger than the base value behind the base value (the same number can be on either side). After this partition is exited, the base value is in the middle of the array. This is called a partition operation.

[0244] Recursively sort subarrays: recursively sort the subarrays of elements less than the base value and the subarrays of elements greater than the base value;

[0245] F2, pseudo code:

[0246]

[0247]

[0248] F3, code implementation: convert the pseudocode in F2 into code in a specific programming language (such as Java);

[0249] F4, algorithm tuning: optimize the performance of quick sort by choosing a more appropriate benchmark value (such as the three-number median method), tail recursion optimization, and using insertion sort for small arrays.

[0250] Among them, the specific steps implemented by the code in F3 include:

[0251]

[0252]

[0253]

[0254] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0255] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An Internet-based engineering management improvement service system, characterized by: The service system comprises: Data acquisition module: responsible for capturing and integrating data required for engineering management from various sources; Data processing module: receives raw data from the data acquisition layer and performs cleaning, integration, conversion and storage; Business logic module: responsible for implementing specific business processes and rules for engineering management; Application service module: meet the diverse needs of engineering managers; User interaction layer module: responsible for presenting the functions provided by the application service layer to users in an intuitive and friendly manner.

2. The Internet-based engineering management improvement service system according to claim 1, characterized in that: The specific implementation steps of the data acquisition module include: A1, Demand Analysis and Planning: Clarify data requirements: According to the business requirements of the engineering management system, clarify the types of data that need to be collected; Develop a data collection plan: Based on data requirements, develop a detailed data collection plan, including the time, frequency, method of collection, and the way of data storage and processing; A2, data source identification and access: Identify data sources: Identify devices, systems, and platforms as sources of data; Access data source: connect the data source to the data collection layer through API interface, database connection, and file transfer; A3, Data collection implementation: Configure collection tools: Configure corresponding data collection tools or software according to the characteristics of the data source; Set collection rules: define data collection rules; Execute collection tasks: According to the collection plan, start the data collection task, grab data from the data source, and temporarily store it in the local or cloud data buffer; A4, Data verification and cleaning: Data verification: Conduct preliminary verification of the collected data to check the completeness, accuracy and consistency of the data; Data cleaning: remove duplicate data, correct erroneous data, and fill in missing data; A5, Data Integration and Storage: Data integration: Integrate the cleaned data according to a certain logical structure; Data storage: storing the integrated data in a database, data warehouse or distributed storage system; A6, Monitoring and Optimization: Performance monitoring: real-time monitoring of the data collection process; Optimization and adjustment: Optimize and adjust data collection plans, collection rules, and storage strategies based on monitoring results.

3. The Internet-based engineering management improvement service system according to claim 1 is characterized in that: The specific implementation steps of the data processing module include: B1, data reception and buffering: Data reception: The data processing layer receives the original data stream from the data acquisition layer; Data buffering: The data processing layer sets up a data buffer to temporarily store data to be processed; B2, data cleaning: Deduplication: Identify and delete duplicate data records to avoid bias in subsequent analysis; Error detection and correction: Check the data for erroneous values, outliers, or data that does not conform to business rules, and correct or mark them; Missing value processing: For missing data fields, fill in default values, interpolate, or delete records based on business needs and data characteristics; B3, Data Integration and Standardization: Data integration: Integrate data from different data sources and formats into a unified data model; Data standardization: standardize the naming, format, and unit conversion of data fields; B4, Data Conversion and Mapping: Data conversion: Calculate, aggregate, split and convert data according to business needs; Data mapping: Establish a mapping relationship between data fields and business concepts to ensure that the business meaning of the data is clear and unambiguous; B5, Distributed Processing and Storage: Big data processing technology: Use Hadoop and Spark big data processing technologies to efficiently process massive data; Distributed storage system: HDFS, HBase, and Cassandra distributed storage systems are used to store data in multiple nodes; B6, Preliminary data analysis: Data statistics: perform basic statistical analysis on data; Trend forecasting: Use time series analysis and regression analysis methods to forecast data trends; Data visualization: Visualize data through charts and dashboards; B7, Quality Control and Monitoring: Data quality monitoring: perform quality checks on processed data; Performance monitoring: Real-time monitoring of the processing performance of the data processing layer, including processing speed and resource consumption.

4. The Internet-based engineering management improvement service system according to claim 1 is characterized in that: The specific implementation steps of the business logic module include: C1, demand analysis and algorithm selection: In-depth understanding of business needs: Analyze business needs; Algorithm evaluation and selection: Evaluate and select appropriate algorithms based on business needs; Algorithm feasibility analysis: Evaluate the feasibility of the selected algorithm under the existing system architecture and hardware conditions; C2, Algorithm design and implementation: Design the specific implementation plan of the algorithm according to business needs, including the algorithm's logical flow, data structure selection, and parameter setting; C3, business logic and algorithm integration: Interface definition: define a clear interface for algorithm implementation; Integration of business logic and algorithms: Integrate algorithms into business logic; Data stream processing: design data stream processing flow; C4, Testing and Verification: Unit testing: Perform unit testing on algorithms and business logic to verify the correctness of the algorithms and the accuracy of the business logic; Integration testing: Integrate the business logic layer with other layers; Performance testing: Perform performance testing on the business logic layer containing the algorithm; Algorithm effect evaluation: Evaluate the actual effect of the algorithm based on business needs; C5, Maintenance and Optimization: Algorithm update: As business develops and technology changes, the algorithm will be regularly evaluated and updated; Performance monitoring: monitor the performance of the business logic layer; Logging: records key information about algorithm execution and business logic processing; Optimization and iteration: Continuously optimize business logic and algorithm implementation based on user feedback and system operation status.

5. The Internet-based engineering management improvement service system according to claim 1 is characterized by: The specific implementation steps of the application service module include: D1, Demand Analysis: Understand business requirements: Have a deep understanding of business requirements, clarify the functions that the application service layer needs to support the business, and the specific implementation requirements of the functions; Define service interface: Define the service interface that the application service layer needs to provide according to business requirements; D2, Service Design and Implementation: Service division: Divide complex business logic into multiple independent service modules, each module is responsible for processing a part of the business logic; Interface definition: At the code level, define clear interfaces for each service module and clarify the input and output of the service; Business logic implementation: Write specific business logic code according to business requirements and service division; Service registration and discovery: In a microservice architecture, the application service layer contains multiple microservice instances; D3, service test: Unit testing: Write unit test code to verify key methods and functions in the application service layer; Integration testing: Perform integration testing on the application service layer and other layers to verify whether the data interaction and business logic processing between the layers are smooth; Performance testing: Perform performance testing on the application service layer to evaluate its processing capabilities and stability under high concurrency and large data volumes; D4, service deployment and operation and maintenance: Service deployment: deploy the compiled application service layer code to the production environment; Monitoring and logging: In a production environment, a monitoring and logging system should be set up to monitor the running status and performance of the service; Service upgrade and maintenance: With the development of business and the update of technology, the application service layer needs to be upgraded and maintained.

6. The Internet-based engineering management improvement service system according to claim 1 is characterized by: The specific implementation steps of the user interaction layer module include: E1, User Research and Demand Analysis: Target user positioning: clarify the target user group of the system; Requirements collection: Collect information about target users’ expectations, usage habits, and preferences for system functions through questionnaires, interviews, and user observations; Demand analysis: organize and analyze the collected requirements to extract key user needs and pain points; E2, User Interface Design: Information architecture design: plan the information structure of the system, determine the main functional modules and page layout; Prototyping: Use prototyping tools to draw user interface prototypes; Visual design: Design interface visual elements that meet aesthetic standards based on brand image and user preferences; E3, interactive logic design: Operation process design: design the operation process for users to complete tasks based on user needs and business logic; Interactive element design: Design various interactive elements to ensure that users can easily identify and interact with them; Feedback mechanism design: provide immediate and clear feedback for user operations; E4, User Experience Optimization: Usability testing: Invite target users to participate in prototype testing, observe and record the usage process, collect feedback, and identify potential problems; Iterative optimization: Iteratively optimize the interface design and interaction logic based on test results and user feedback; Performance optimization: ensure that the user interaction layer has good response speed and stability, and reduce loading time and jamming; E5, user feedback and continuous optimization.

7. The Internet-based engineering management improvement service system according to claim 1 is characterized by: The specific steps of algorithm design and implementation in C2 include: F1, Algorithm Design: Select a pivot value: Select an element from the array to be sorted as the pivot value (pivot); there are many ways to select a pivot value, the most common ones are to select the first element, the last element, the middle element of the array, or randomly select an element; Partition operation: rearrange the array, put all elements smaller than the base value in front of the base value, and put all elements larger than the base value behind the base value (the same number goes to either side); after this partition is exited, the base value is in the middle of the array; this is called a partition operation; Recursively sort subarrays: recursively sort the subarrays of elements less than the base value and the subarrays of elements greater than the base value; F2, pseudo code: F3, code implementation; F4, algorithm tuning: Optimize the performance of quick sort by choosing a more appropriate benchmark value, tail recursion optimization, and using insertion sort for small arrays.

8. The Internet-based engineering management improvement service system according to claim 6 is characterized by: The specific steps of user feedback and continuous optimization in E5 include: G1, collect user feedback: collect user feedback through user surveys, customer service feedback, user reviews and other channels; G2, Data Analysis: Use data analysis tools to mine and analyze user behavior data to find out the hot spots and pain points of user usage; G3 continuously optimizes the user interface and interaction logic based on user feedback and data analysis results.

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