Alarm reminding tool for task whole-process visual management

Abstract

The invention relates to electric power operation and maintenance, and particularly discloses an alarm reminding tool for task whole-process visual management, which comprises a data acquisition module used for automatically acquiring related work task information in all business systems by using a Python technology; the data processing module is used for cleaning, screening and sorting the acquired data; the data analysis module is used for analyzing the scheduling operation business data in the database; the data input module is used for acquiring data input manually or imported through an excel file; and the visual display module is used for visually displaying the analysis result in the calendar table in the form of key words and color categories according to requirements. According to the alarm reminding tool for visual management of the whole task process, visual management and alarm reminding of the whole task process can be achieved, and then the problems that in the prior art, the working efficiency is low, and the task completion punctuality rate is low are solved.

Description

Technical Field

[0001] The present invention relates to electric power operation and maintenance, and in particular to an alarm reminder tool for visual management of the entire task process. Background Art

[0002] In the field of modern supply chain management, with the rapid development of information technology, enterprise-level information systems have been widely used. These systems usually include multiple modules such as enterprise resource planning (ERP), supply chain management (SCM), customer relationship management (CRM), etc., which together constitute the information infrastructure for daily operations of enterprises. However, although these systems have played a huge role in improving business process automation and data processing capabilities, they still have the following problems and limitations in task management:

[0003] Information island problem: There is often data isolation between different business systems, which makes it difficult to share and integrate work task information, thus affecting the efficiency and accuracy of task management.

[0004] Difficulty in task tracking: In complex business processes, there are many task nodes, and existing systems often lack effective task tracking and monitoring mechanisms, making it difficult for managers to grasp the progress of tasks in real time.

[0005] Imperfect feedback mechanism: The existing system lacks a timely feedback mechanism during task execution, resulting in problems not being discovered and solved in a timely manner, affecting the overall execution effect of the task.

[0006] Insufficient visualization: Most systems have a relatively simple visualization display for task management, which cannot provide managers with an intuitive and comprehensive task execution status, making the decision-making process lack effective basis.

[0007] Weak customization capabilities: Existing task management tools often lack sufficient customization capabilities and cannot provide customized task monitoring and reminder functions according to the needs of different managers.

[0008] In view of the above problems and limitations, those skilled in the art have been seeking more efficient and convenient task management solutions in order to improve the refinement and intelligence level of task management, thereby improving the overall operational efficiency and management level of the enterprise. The present invention is proposed based on such a background, and aims to solve the problems existing in the prior art through an innovative alarm reminder tool for visual management of the entire task process.

[0009] The above information is presented as background information only to assist with understanding the present disclosure and no determination or admission is made as to whether any of the above may be used as prior art with respect to the present disclosure. Summary of the invention

[0010] The present invention provides an alarm reminder tool for visual management of the entire task process, which is used to achieve visual management and alarm reminders of the entire task process, thereby solving the problems of low work efficiency and low task completion punctuality rate in the prior art.

[0011] To achieve the above-mentioned purpose, the present invention provides an alarm reminder tool for visual management of the whole task process, including:

[0012] Data acquisition module, used to automatically obtain relevant work task information in all business systems using Python technology;

[0013] Data processing module, used to clean, filter and organize the acquired data;

[0014] Data analysis module, used to analyze the scheduling operation business data in the database;

[0015] Data entry module, used to obtain data entered manually or imported through Excel files;

[0016] The visualization module is used to visualize the analysis results in a calendar table with key words and color categories according to needs.

[0017] Optionally, it is characterized in that all business systems include ERP module, SCM module, and CRM module

[0018] Optionally, it is characterized in that the use of Python technology to automatically obtain relevant work task information in all business systems includes:

[0019] S101: Interface call and data capture:

[0020] S1011: API connection: For systems that support API interfaces, use Python's requests library or HTTP client library to write scripts to connect to the target system API; obtain task data in the system by sending HTTP requests;

[0021] S1012: Web scraping: For systems that do not support API interfaces, use Python's BeautifulSoup, Scrapy, or selenium library to crawl web pages, parse the HTML page structure, and extract task information;

[0022] S102: Data analysis and conversion:

[0023] S1021: JSON parsing: For the JSON format data returned by the API, use Python's built-in json library to parse it and extract the task name, task status, deadline, and key information of the person in charge;

[0024] S1022: XML parsing: If the data is returned in XML format, it is parsed using the xml.etree.ElementTree library;

[0025] S1023: Data cleaning: Clean the captured or parsed data to remove irrelevant information and ensure the accuracy and availability of the data;

[0026] S103: Data Structuring:

[0027] S1031: Object mapping: Map the parsed data to Python class or dictionary objects to form structured data for subsequent processing and analysis;

[0028] S1032: Data verification: Verify the structured data to ensure the integrity and correctness of the data, check the non-emptiness of necessary fields and the matching of data types;

[0029] S104: Exception handling and logging:

[0030] S1041: Error handling: During the data acquisition process, possible network errors, data format errors, etc. are captured and processed to ensure the robustness of the program;

[0031] S1042: Logging: Use the logging library to record key steps and exception information in the data acquisition process to facilitate problem tracking and debugging;

[0032] S105: Scheduled tasks and real-time monitoring:

[0033] S1051: Scheduled execution: Use Python's schedule library or APScheduler scheduled task library to set scheduled tasks and regularly obtain the latest task information from all business systems;

[0034] S1052: Real-time monitoring: For tasks that require real-time monitoring, long polling or WebSocket methods are used to achieve real-time updates of tasks;

[0035] S106: Safety considerations:

[0036] S1061: Authentication and authorization: When acquiring data, ensure that the authentication and authorization mechanism of the target system is followed, and use OAuth and API key methods for security authentication;

[0037] S1062: Data encryption: Encrypt sensitive data during transmission to protect data security

[0038] Optionally, it is characterized in that the cleaning, screening and arranging of the acquired data includes:

[0039] S201: Data import:

[0040] Import data obtained through API calls or web scraping into the Python environment and use the DataFrame object of the pandas library to store and manage data;

[0041] S202: Data cleaning:

[0042] S2021: Remove empty values: Check for empty, NaN, or null values ​​in a dataset and decide whether to fill these values ​​or delete records containing these values;

[0043] S2022: Handling duplicate values: Identify and delete duplicate records to ensure that each task information is unique in the dataset;

[0044] S2023: Correct data types: Ensure that the data types of all columns are correct, convert strings to date format, and convert numeric strings to numeric types;

[0045] S2024: Text standardization: Standardize text data, unify uppercase and lowercase letters, remove leading and trailing spaces, and replace special characters;

[0046] S203: Data screening:

[0047] S2031: Condition filtering: filter out relevant data rows according to specific conditions, filter out tasks in a specific status, and tasks within a specific time range;

[0048] S2032: Column selection: Select columns that are useful for task management, exclude irrelevant information, and simplify the data set;

[0049] S204: Data collation:

[0050] S2041: Data sorting: Sort data by importance and urgency of tasks, by deadline, and by priority;

[0051] S2042: Data splitting: If some columns contain complex information, they can be split into multiple columns to facilitate subsequent analysis;

[0052] S2043: Data aggregation: For data that needs to be summarized, the number of tasks is summarized by department or project classification, and the groupby function is used for data aggregation;

[0053] S2044: Data transformation: Convert data into a form more suitable for analysis, encode categorical data into numbers, or create new derived columns to represent specific business logic;

[0054] S205: Data Verification:

[0055] S2051: Logic verification: Check whether the data conforms to the business logic, such as the task start time should not be later than the deadline;

[0056] S2052: Scope Verification: Verify that the data value is within a reasonable range and the estimated working hours of the task should not exceed the total working hours of the project;

[0057] S206: Data output:

[0058] S2061: Output the cleaned, filtered and sorted data as a new data set to facilitate subsequent data analysis and visualization;

[0059] S2062: Save the cleaned data to a file

[0060] Optionally, it is characterized in that the analyzing the scheduling operation business data in the database includes:

[0061] S301: Database connection:

[0062] Use Python's database connection library to establish a connection with the database;

[0063] Configure database connection parameters, including database type, server address, port, database name, user name, and password;

[0064] S302: Data query:

[0065] Write SQL query statements to extract scheduling operation business data from the database; the query involves multiple tables and requires the use of JOIN operations to associate data;

[0066] Use parameterized queries to prevent SQL injection attacks and ensure data security;

[0067] S303: Data reading:

[0068] Execute SQL queries and read the query results into the Python environment using the read_sql_query or read_sql function of the pandas library;

[0069] S304: Data preprocessing:

[0070] Perform a preliminary check on the read data, including data type, missing values, and outliers;

[0071] Perform necessary conversions on the data, unify the date and time formats, and convert data types;

[0072] S305: Data Analysis:

[0073] Descriptive statistical analysis: Calculate the statistics of key indicators, including mean, median, standard deviation, minimum and maximum values;

[0074] Trend analysis: Analyze the business data of scheduling operations over time to identify cyclical or seasonal patterns;

[0075] Correlation analysis: exploring the relationship between different variables, including the relationship between task completion time and resource allocation;

[0076] Anomaly detection: identifying outliers or unusual patterns in data and analyzing possible causes;

[0077] Performance evaluation: Evaluate business performance based on the efficiency, cost, and punctuality indicators of scheduling operations;

[0078] S306: Data mining:

[0079] Apply data mining techniques, including classification, clustering, and predictive modeling, to discover deeper patterns in data or predict future trends;

[0080] Build predictive models, including using regression analysis to predict task completion times;

[0081] S307: Interpretation of results:

[0082] Interpret the analysis results to extract valuable information and insights;

[0083] Prepare analysis reports summarizing issues, trends, and recommendations identified during the analysis;

[0084] S308: Data feedback:

[0085] Feedback analysis results to the database, update the status of related business data or add analysis comments;

[0086] Provide analysis results to decision support systems to help management make more informed decisions

[0087] Optionally, it is characterized in that the obtaining of data manually entered or imported through an Excel file includes:

[0088] S401: Manual data entry

[0089] User interface design: Provide an intuitive user interface so that users can fill in data directly in the system;

[0090] Input guidance: Provide clear labels and input rules for each data entry field to reduce input errors;

[0091] Real-time validation: Implement front-end data validation, including data type, format, and range checks to ensure data accuracy;

[0092] S402: Excel file data import

[0093] File upload support: allows users to upload Excel files and use pandas to read the file content;

[0094] Import template: Provide standardized Excel templates to specify data format and structure so that users can follow

[0095] Optionally, it is characterized in that step S402 further includes:

[0096] Format validation: Checks whether the uploaded file conforms to the expected format, including .xlsx or .xls;

[0097] Structural verification: ensure that the columns in the Excel file match the data model in the system, and that the column order and column names are correct;

[0098] Content cleaning: clean the imported data, remove extra spaces, process special characters, and convert data types;

[0099] Integrity checking: identifying and handling null values, outliers, and duplicate records;

[0100] Data mapping: Map the data fields in Excel to the corresponding fields in the system database;

[0101] Import confirmation: Provide a preview function before data is imported, allowing users to confirm that the data is correct, and display a success message after importing

[0102] Optionally, it is characterized in that, according to the requirements, the analysis results are visually displayed in the calendar table with key words and color categories, including:

[0103] 504: Implementing Visual Display

[0104] Date mapping: Map the date field in the data to the specific date in the calendar table;

[0105] Key information display:

[0106] 5041: Extract key text information, including task name and important notes;

[0107] 5042: Assign colors to different information categories according to a preset color coding scheme;

[0108] Dynamic Rendering:

[0109] 5043: Use front-end technology to dynamically render the calendar;

[0110] 5044: Implement the interactivity of the calendar, including clicking to view details and dragging to adjust the date;

[0111] 505: User Interaction

[0112] Event response: add event listeners to elements in the calendar, including click and hover, to respond to user operations;

[0113] Information pop-up window: When the user clicks on a task in the calendar, a detailed information window pops up;

[0114] 506: Visualization Optimization

[0115] Interface optimization: adjust the calendar interface layout and color scheme based on user feedback to improve user experience;

[0116] Performance optimization: ensure that the calendar table can render quickly even when the amount of data is large, reducing loading time;

[0117] 507: Testing and Feedback

[0118] Functional testing: Test all functions of the calendar, including date mapping, color coding, and user interaction;

[0119] User feedback collection: Collect user feedback on the visualization for subsequent optimization iterations;

[0120] 508: Deployment and Application

[0121] System Integration: Integrate the calendar visualization tool into existing business systems;

[0122] User training: Provide users with training PPTs or videos to help them become familiar with and use new visualization tools.

[0123] Compared with the prior art, the present invention has the following beneficial effects, providing an alarm reminder tool for visual management of the entire task process:

[0124] ①Automated data acquisition: Use Python technology to automatically obtain relevant work task information from other systems to ensure the real-time and accuracy of data.

[0125] ②Data cleaning and organization: Clean, filter and organize the acquired data, remove invalid data and improve data availability.

[0126] ③Database data analysis: Analyze the scheduling operation business data in the database to identify business trends and potential problems.

[0127] ④Manual and automated data entry: supports manual entry and data import through Excel files, improving data flexibility and diversity.

[0128] ⑤ Visual display: According to needs, the analysis results are visualized in the calendar table with key words and color categories to make task management more intuitive and easy to understand.

[0129] ⑥User interaction and optimization: Provide user interaction functions, such as clicking to view details, dragging to adjust dates, etc., and optimize the interface and performance based on user feedback.

[0130] ⑦ Security and Compliance: During data acquisition and processing, ensure compliance with data protection regulations and company policies to protect data security.

[0131] ⑧Integration and training: Easy to integrate into existing business systems, and provide users with training to help them quickly get started and use new tools.

[0132] Through these advantages, the present invention can effectively solve the problems of low work efficiency and low task completion punctuality in the prior art, improve the refinement and intelligence level of task management, and thus improve the overall operational efficiency and management level of the enterprise.

[0133] The present invention has other features and advantages, which will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0135] Figure 1 It is a structural block diagram of an alarm reminder tool for visual management of the entire task process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0136] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0137] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0138] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0139] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0140] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0141] Without further limitations, in this application, the words "include", "comprises", "has" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0142] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0143] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0144] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0145] Please refer to the attached Figure 1 , an alarm reminder tool for visual management of the entire task process provided by an embodiment of the present invention, is used to achieve visual management and alarm reminders of the entire task process, thereby solving the problems of low work efficiency and low task completion punctuality in the prior art.

[0146] See also Figure 1 In this embodiment, the alarm reminder tool for visual management of the entire task process includes:

[0147] Data acquisition module 1 is used to automatically obtain relevant work task information in all business systems using Python technology;

[0148] Data processing module 2, used for cleaning, screening and arranging the acquired data;

[0149] Data analysis module 3, used to analyze the scheduling operation business data in the database;

[0150] Data entry module 4, used to obtain data entered manually or imported through Excel files;

[0151] The visualization display module 5 is used to visualize the analysis results in a calendar table by key words and color categories according to needs.

[0152] Optionally, the use of Python technology to automatically obtain relevant work task information in all business systems includes:

[0153] S101: Interface call and data capture:

[0154] S1011: API connection: For systems that support API interfaces, use Python's requests library or HTTP client library to write scripts to connect to the target system API; obtain task data in the system by sending HTTP requests, such as GET or POST requests;

[0155] S1012: Web scraping: For systems that do not support API interfaces, use Python's BeautifulSoup, Scrapy, or selenium libraries to crawl web pages, parse the HTML page structure, and extract task information;

[0156] S102: Data analysis and conversion:

[0157] S1021: JSON parsing: For the JSON format data returned by the API, use Python's built-in json library to parse it and extract key information such as task name, task status, deadline, person in charge, etc.

[0158] S1022: XML parsing: If the data is returned in XML format, it is parsed using the xml.etree.ElementTree library;

[0159] S1023: Data cleaning: Clean the captured or parsed data to remove irrelevant information, such as HTML tags, script codes, etc., to ensure the accuracy and availability of the data;

[0160] S103: Data Structuring:

[0161] S1031: Object mapping: Map the parsed data to Python class or dictionary objects to form structured data for subsequent processing and analysis;

[0162] S1032: Data verification: Verify the structured data to ensure the integrity and correctness of the data, such as checking the non-emptiness of necessary fields and the matching of data types;

[0163] S104: Exception handling and logging:

[0164] S1041: Error handling: During the data acquisition process, possible network errors, data format errors, etc. are captured and processed to ensure the robustness of the program;

[0165] S1042: Logging: Use the logging library to record key steps and exception information in the data acquisition process to facilitate problem tracking and debugging;

[0166] S105: Scheduled tasks and real-time monitoring:

[0167] S1051: Scheduled execution: Use Python's schedule library or APScheduler and other scheduled task libraries to set scheduled tasks and regularly obtain the latest task information from all business systems;

[0168] S1052: Real-time monitoring: For tasks that require real-time monitoring, long polling or WebSocket and other technologies can be used to achieve real-time updates of tasks;

[0169] S106: Safety considerations:

[0170] S1061: Authentication and authorization: When acquiring data, ensure that the authentication and authorization mechanism of the target system is followed, such as using OAuth, API keys, etc. for security authentication;

[0171] S1062: Data encryption: Encrypt sensitive data during transmission to protect data security.

[0172] Through the implementation of the above technical details, the present invention can automatically obtain task information from other systems efficiently and stably, and provide a reliable data source for the alarm reminder tool for visual management of the entire task process.

[0173] Optionally, the cleaning, screening and arranging of the acquired data comprises the following steps:

[0174] S201: Data import:

[0175] Import data obtained through API calls or web scraping into the Python environment, usually using the DataFrame object of the pandas library for data storage and management.

[0176] S202: Data cleaning:

[0177] S2021: Remove Empty Values: Check for empty, NaN, or null values ​​in a dataset and decide whether to fill these values ​​or delete records containing these values.

[0178] S2022: Handle duplicate values: Identify and delete duplicate records to ensure that each task information is unique in the dataset.

[0179] S2023: Correct data types: Ensure that the data types of all columns are correct, such as converting strings to date formats, converting numeric strings to numeric types, etc.

[0180] S2024: Text standardization: Standardize text data, such as unifying upper and lower case, removing leading and trailing spaces, replacing special characters, etc.

[0181] S203: Data screening:

[0182] S2031: Condition filtering: Filter out relevant data rows according to specific conditions, such as filtering out tasks in a specific status, tasks within a specific time range, etc.

[0183] S2032: Column selection: Select columns that are useful for task management, exclude irrelevant information, and simplify the data set.

[0184] S2033: Value filtering: For some columns, you may need to filter out data that matches a specific value range, such as selecting only tasks with a high priority.

[0185] S204: Data collation:

[0186] S2041: Data sorting: Sort data according to the importance and urgency of the task, such as by deadline, priority, etc.

[0187] S2042: Data splitting: If some columns contain complex information, they can be split into multiple columns to facilitate subsequent analysis.

[0188] S2043: Data aggregation: For data that needs to be summarized, such as summarizing the number of tasks by department or project classification, use the groupby function to aggregate the data.

[0189] S2044: Data transformation: Converting data into a form more suitable for analysis, such as encoding categorical data into numbers, or creating new derived columns to represent specific business logic.

[0190] S205: Data Verification:

[0191] S2051: Logical verification: Check whether the data conforms to the business logic, such as the task start time should not be later than the deadline.

[0192] S2052: Scope Verification: Verify whether the data value is within a reasonable range, for example, the estimated working hours of a task should not exceed the total working hours of the project.

[0193] S206: Data output:

[0194] S2061: Output the cleaned, filtered and organized data as a new data set to facilitate subsequent data analysis and visualization.

[0195] S2062: If necessary, the cleaned data can be saved to a file, such as CSV, Excel, or a database.

[0196] Through the above steps, the quality and availability of data can be ensured, providing accurate and efficient data support for the alarm reminder tool for visual management of the entire task process.

[0197] Optionally, analyzing the scheduling operation business data in the database includes the following steps:

[0198] S301: Database connection:

[0199] Use Python's database connection library (such as sqlite3, psycopg2, pyodbc, etc.) to establish a connection with the database;

[0200] Configure database connection parameters, including database type, server address, port, database name, user name and password;

[0201] S302: Data query:

[0202] Write SQL query statements to extract scheduling operation business data from the database; the query may involve multiple tables and require JOIN operations to associate data;

[0203] Use parameterized queries to prevent SQL injection attacks and ensure data security;

[0204] S303: Data reading:

[0205] Execute SQL queries and read the query results into the Python environment, usually using the read_sql_query or read_sql function of the pandas library;

[0206] S304: Data preprocessing:

[0207] Perform a preliminary check on the read data, including data type, missing values, outliers, etc.;

[0208] Perform necessary conversions on the data, such as unifying the date and time formats, converting data types, etc.;

[0209] S305: Data Analysis:

[0210] Descriptive statistical analysis: Calculate the statistics of key indicators, such as mean, median, standard deviation, minimum, maximum, etc.;

[0211] Trend analysis: Analyze the time-varying trends of dispatch operation business data to identify cyclical or seasonal patterns;

[0212] Correlation analysis: exploring the relationship between different variables, such as the relationship between task completion time and resource allocation;

[0213] Anomaly detection: identifying outliers or unusual patterns in data and analyzing possible causes;

[0214] Performance evaluation: Evaluate business performance based on efficiency, cost, punctuality and other indicators of scheduling operations;

[0215] S306: Data mining:

[0216] Apply data mining techniques such as classification, clustering, and predictive modeling to discover deeper data patterns or predict future trends;

[0217] Build predictive models, such as using regression analysis to predict task completion time;

[0218] S307: Interpretation of results:

[0219] Interpret the analysis results to extract valuable information and insights;

[0220] Prepare analysis reports summarizing issues, trends, and recommendations identified during the analysis;

[0221] S308: Data feedback:

[0222] Feedback analysis results to the database, update the status of related business data or add analysis comments;

[0223] Provide analysis results to decision support systems to assist management in making more informed decisions;

[0224] S309: Safety and Compliance Check:

[0225] Ensure that analytical processes comply with data protection regulations and company policies;

[0226] Take appropriate privacy precautions when analyzing sensitive data.

[0227] Through the above steps, valuable information can be extracted from the scheduling operation business data in the database, providing data support for the task management of the supply chain service center, helping to optimize the scheduling operation process and improve business efficiency.

[0228] Optionally, obtaining the data manually entered or imported through an Excel file includes the following steps:

[0229] S401: Manual data entry

[0230] User interface design: Develop an intuitive user interface where users can fill in data directly in the system;

[0231] Input guidance: Provide clear labels and input rules for each data entry field to reduce input errors;

[0232] Real-time validation: Implement front-end data validation, including data type, format, and range checks to ensure data accuracy;

[0233] S402: Excel file data import

[0234] File upload support: allows users to upload Excel files and use libraries such as pandas to read the file content;

[0235] Import template: Provide standardized Excel templates to specify data format and structure for users to follow;

[0236] Data import steps are detailed as follows:

[0237] Format validation: Checks whether the uploaded file conforms to the expected format, such as .xlsx or .xls;

[0238] Structural verification: ensure that the columns in the Excel file match the data model in the system, and that the column order and column names are correct;

[0239] Content cleaning: clean the imported data, remove extra spaces, process special characters, and convert data types;

[0240] Integrity checking: identifying and handling null values, outliers, and duplicate records;

[0241] Data mapping: Map the data fields in Excel to the corresponding fields in the system database;

[0242] Import confirmation: Provide a preview function before data is imported, allowing users to confirm that the data is correct, and display a success message after import;

[0243] The above process ensures that data, whether entered manually or imported through Excel files, can be processed efficiently and accurately by the system, thereby improving the overall data management efficiency and quality.

[0244] Through the above steps, it is ensured that the data, whether entered manually or imported through Excel files, can be efficiently and accurately received and processed by the system.

[0245] Optionally, the analysis results are visualized in a calendar table using key words and color categories according to requirements, including the following steps:

[0246] 501: Demand Analysis

[0247] User demand research: Collect users' specific needs for the visual display of calendars, including key information, color coding preferences, etc.

[0248] Functional requirements determination: Based on user feedback, determine the data fields and visualization elements that the calendar needs to display.

[0249] 502: Data Preparation

[0250] Data filtering: Filter the data that needs to be displayed in the calendar table from the analysis results.

[0251] Data formatting: Convert the filtered data into the format required by the calendar table, such as unifying the date and time formats.

[0252] 503: Designing Visual Templates

[0253] Interface layout: Design the layout of the calendar, including the arrangement of dates, switching between week view and month view, etc.

[0254] Color-coding scheme: Develop a color-coding standard to assign specific colors to different key information or categories.

[0255] 504: Implementing Visual Display

[0256] Date mapping: Map the date field in the data to the specific date in the calendar table;

[0257] Key information display:

[0258] 5041: Extract key text information, such as task name, important notes, etc.;

[0259] 5042: Assign colors to different information categories according to a preset color coding scheme;

[0260] Dynamic Rendering:

[0261] 5043: Use front-end technologies (such as JavaScript, Vue.js, React, etc.) to dynamically render calendars;

[0262] 5044: Implement the interactivity of the calendar, such as clicking to view details, dragging to adjust the date, etc.

[0263] 505: User Interaction

[0264] Event response: add event listeners to elements in the calendar, such as click, hover, etc., to respond to user operations;

[0265] Information pop-up window: When the user clicks on a task in the calendar, a detailed information window pops up;

[0266] 506: Visualization Optimization

[0267] Interface optimization: Adjust the calendar interface layout and color scheme based on user feedback to improve user experience;

[0268] Performance optimization: ensure that the calendar table can render quickly even when the amount of data is large, reducing loading time;

[0269] 507: Testing and Feedback

[0270] Functional testing: Test all functions of the calendar, including date mapping, color coding, user interaction, etc.

[0271] User feedback collection: Collect user feedback on the visualization for subsequent optimization iterations;

[0272] 508: Deployment and Application

[0273] System Integration: Integrate the calendar visualization tool into existing business systems;

[0274] User training: Provide users with training PPTs or videos to help them become familiar with and use new visualization tools.

[0275] By following the above steps, you can ensure that the analysis results are effectively and intuitively displayed in the calendar table with key words and color categories, thereby improving the efficiency of task management and user experience.

[0276] In summary, the alarm reminder tool for visual management of the entire task process provided by this embodiment has the following advantages:

[0277] ①Automated data acquisition: Use Python technology to automatically obtain relevant work task information from other systems to ensure the real-time and accuracy of data.

[0278] ②Data cleaning and organization: Clean, filter and organize the acquired data, remove invalid data and improve data availability.

[0279] ③Database data analysis: Analyze the scheduling operation business data in the database to identify business trends and potential problems.

[0280] ④Manual and automated data entry: supports manual entry and data import through Excel files, improving data flexibility and diversity.

[0281] ⑤ Visual display: According to needs, the analysis results are visualized in the calendar table with key words and color categories to make task management more intuitive and easy to understand.

[0282] ⑥User interaction and optimization: Provide user interaction functions, such as clicking to view details, dragging to adjust dates, etc., and optimize the interface and performance based on user feedback.

[0283] ⑦ Security and Compliance: During data acquisition and processing, ensure compliance with data protection regulations and company policies to protect data security.

[0284] ⑧Integration and training: Easy to integrate into existing business systems, and provide users with training to help them quickly get started and use new tools.

[0285] Through these advantages, the present invention can effectively solve the problems of low work efficiency and low task completion punctuality in the prior art, improve the refinement and intelligence level of task management, and thus improve the overall operational efficiency and management level of the enterprise.

[0286] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. An alarm reminder tool for visual management of the entire task process. It is characterized in that include: Data acquisition module, used to automatically obtain relevant work task information in all business systems using Python technology; Data processing module, used to clean, filter and organize the acquired data; Data analysis module, used to analyze the scheduling operation business data in the database; Data entry module, used to obtain data entered manually or imported through Excel files; The visualization module is used to visualize the analysis results in a calendar table using key words and color categories according to needs.

2. The alarm reminder tool for visual management of the entire task process according to claim 1, It is characterized in that The above-mentioned business systems include ERP module, SCM module and CRM module.

3. The alarm reminder tool for visual management of the entire task process according to claim 2, It is characterized in that The use of Python technology to automatically obtain relevant work task information in all business systems includes: S101: Interface call and data capture: S1011: API connection: For systems that support API interfaces, use Python's requests library or HTTP client library to write scripts to connect to the target system API; obtain task data in the system by sending HTTP requests; S1012: Web scraping: For systems that do not support API interfaces, use Python's BeautifulSoup, Scrapy, or selenium library to crawl web pages, parse the HTML page structure, and extract task information; S102: Data analysis and conversion: S1021: JSON parsing: For the JSON format data returned by the API, use Python's built-in json library to parse it and extract the task name, task status, deadline, and key information of the person in charge; S1022: XML parsing: If the data is returned in XML format, it is parsed using the xml.etree.ElementTree library; S1023: Data cleaning: Clean the captured or parsed data to remove irrelevant information and ensure the accuracy and availability of the data; S103: Data Structuring: S1031: Object mapping: Map the parsed data to Python class or dictionary objects to form structured data for subsequent processing and analysis; S1032: Data verification: Verify the structured data to ensure the integrity and correctness of the data, check the non-emptiness of necessary fields and the matching of data types; S104: Exception handling and logging: S1041: Error handling: During the data acquisition process, possible network errors, data format errors, etc. are captured and processed to ensure the robustness of the program; S1042: Logging: Use the logging library to record key steps and exception information in the data acquisition process to facilitate problem tracking and debugging; S105: Scheduled tasks and real-time monitoring: S1051: Scheduled execution: Use Python's schedule library or APScheduler scheduled task library to set scheduled tasks and regularly obtain the latest task information from all business systems; S1052: Real-time monitoring: For tasks that require real-time monitoring, long polling or WebSocket methods are used to achieve real-time updates of tasks; S106: Safety considerations: S1061: Authentication and authorization: When acquiring data, ensure that the authentication and authorization mechanism of the target system is followed, and use OAuth and API key methods for security authentication; S1062: Data encryption: Encrypt sensitive data during transmission to protect data security.

4. The alarm reminder tool for visual management of the entire task process according to claim 3, It is characterized in that The cleaning, screening and collating of the acquired data includes: S201: Data import: Import data obtained through API calls or web scraping into the Python environment and use the DataFrame object of the pandas library to store and manage data; S202: Data cleaning: S2021: Remove empty values: Check for empty, NaN, or null values ​​in a dataset and decide whether to fill these values ​​or delete records containing these values; S2022: Handling duplicate values: Identify and delete duplicate records to ensure that each task information is unique in the dataset; S2023: Correct data types: Ensure that the data types of all columns are correct, convert strings to date format, and convert numeric strings to numeric types; S2024: Text standardization: Standardize text data, unify uppercase and lowercase letters, remove leading and trailing spaces, and replace special characters; S203: Data screening: S2031: Condition filtering: filter out relevant data rows according to specific conditions, filter out tasks in a specific status, and tasks within a specific time range; S2032: Column selection: Select columns that are useful for task management, exclude irrelevant information, and simplify the data set; S204: Data collation: S2041: Data sorting: Sort data by importance and urgency of tasks, by deadline, and by priority; S2042: Data splitting: If some columns contain complex information, they can be split into multiple columns to facilitate subsequent analysis; S2043: Data aggregation: For data that needs to be summarized, the number of tasks is summarized by department or project classification, and the groupby function is used for data aggregation; S2044: Data transformation: Convert data into a form more suitable for analysis, encode categorical data into numbers, or create new derived columns to represent specific business logic; S205: Data Verification: S2051: Logic verification: Check whether the data conforms to the business logic, such as the task start time should not be later than the deadline; S2052: Scope Verification: Verify that the data value is within a reasonable range and the estimated working hours of the task should not exceed the total working hours of the project; S206: Data output: S2061: Output the cleaned, filtered and sorted data as a new data set to facilitate subsequent data analysis and visualization; S2062: Save the cleaned data to a file.

5. The alarm reminder tool for visual management of the entire task process according to claim 4, It is characterized in that The analyzing of the scheduling operation business data in the database includes: S301: Database connection: Use Python's database connection library to establish a connection with the database; Configure database connection parameters, including database type, server address, port, database name, user name, and password; S302: Data query: Write SQL query statements to extract scheduling operation business data from the database; the query involves multiple tables and requires the use of JOIN operations to associate data; Use parameterized queries to prevent SQL injection attacks and ensure data security; S303: Data reading: Execute SQL queries and read the query results into the Python environment using the read_sql_query or read_sql function of the pandas library; S304: Data preprocessing: Perform a preliminary check on the read data, including data type, missing values, and outliers; Perform necessary conversions on the data, unify the date and time formats, and convert data types; S305: Data Analysis: Descriptive statistical analysis: Calculate the statistics of key indicators, including mean, median, standard deviation, minimum and maximum values; Trend analysis: Analyze the time-varying trends of dispatch operation business data to identify cyclical or seasonal patterns; Correlation analysis: exploring the relationship between different variables, including the relationship between task completion time and resource allocation; Anomaly detection: identifying outliers or unusual patterns in data and analyzing possible causes; Performance evaluation: Evaluate business performance based on the efficiency, cost, and punctuality indicators of scheduling operations; S306: Data mining: Apply data mining techniques, including classification, clustering, and predictive modeling, to discover deeper patterns in data or predict future trends; Build predictive models, including using regression analysis to predict task completion times; S307: Interpretation of results: Interpret the analysis results to extract valuable information and insights; Prepare analysis reports summarizing issues, trends, and recommendations identified during the analysis; S308: Data feedback: Feedback analysis results to the database, update the status of related business data or add analysis comments; Provide analysis results to decision support systems to assist management in making more informed decisions.

6. The alarm reminder tool for visual management of the entire task process according to claim 5, It is characterized in that The data obtained by manually entering or importing data through an Excel file includes: S401: Manual data entry User interface design: Provide an intuitive user interface so that users can fill in data directly in the system; Input guidance: Provide clear labels and input rules for each data entry field to reduce input errors; Real-time validation: Implement front-end data validation, including data type, format, and range checks to ensure data accuracy; S402: Excel file data import File upload support: allows users to upload Excel files and use pandas to read the file content; Import template: Provide standardized Excel templates that specify data format and structure for users to follow.

7. The alarm reminder tool for visual management of the entire task process according to claim 6, It is characterized in that Step S402 also includes: Format validation: Checks whether the uploaded file conforms to the expected format, including .xlsx or .xls; Structural verification: ensure that the columns in the Excel file match the data model in the system, and that the column order and column names are correct; Content cleaning: clean the imported data, remove extra spaces, process special characters, and convert data types; Integrity checking: identifying and handling null values, outliers, and duplicate records; Data mapping: Map the data fields in Excel to the corresponding fields in the system database; Import confirmation: Provide a preview function before data is imported to allow users to confirm that the data is correct, and display a success message after import.

8. The alarm reminder tool for visual management of the entire task process according to claim 1, It is characterized in that According to the requirements, the analysis results are visualized in the calendar table with key words and color categories, including: 504: Implementing Visual Display Date mapping: Map the date field in the data to the specific date in the calendar table; Key information display: 5041: Extract key text information, including task name and important notes; 5042: Assign colors to different information categories according to a preset color coding scheme; Dynamic Rendering: 5043: Use front-end technology to dynamically render the calendar; 5044: Implement the interactivity of the calendar, including clicking to view details and dragging to adjust the date; 505: User Interaction Event response: add event listeners to elements in the calendar, including click and hover, to respond to user operations; Information pop-up window: When the user clicks on a task in the calendar, a detailed information window pops up; 506: Visualization Optimization Interface optimization: adjust the calendar interface layout and color scheme based on user feedback to improve user experience; Performance optimization: ensure that the calendar table can render quickly even when the amount of data is large, reducing loading time; 507: Testing and Feedback Functional testing: Test all functions of the calendar, including date mapping, color coding, and user interaction; User feedback collection: Collect user feedback on the visualization for subsequent optimization iterations; 508: Deployment and Application System Integration: Integrate the calendar visualization tool into existing business systems; User training: Provide users with training PPTs or videos to help them become familiar with and use new visualization tools.

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