CN117787920B - Intelligent data processing system and method for informationized project - Google Patents
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Abstract
The invention discloses an intelligent data processing system and method for an informationized project, comprising S1, a data collection module collects data from a data terminal; s2, the data filling module matches, grabs and fills the acquired data; s3, the data calculation module calculates and analyzes the filled data according to a set calculation model so as to be used for visual presentation of the data; s4, the data storage module stores the result after calculation and analysis in a storage medium so as to facilitate subsequent inquiry, retrieval and use; s5, the data visualization module displays the result after data calculation and analysis on the real equipment in a graphic and/or image mode. The project intelligent decision and optimization are realized by collecting, optimizing, mining and displaying informationized project progress data and processing and analyzing the data by utilizing artificial intelligence and machine learning technologies.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent data processing system and method applied to informationized projects.
Background
With the development of informatization and digitalization, information technology is integrated into various industries, and informatization projects are increased. The informationized project has the characteristics of abstract, difficult definition of targets, frequent change of demands, high intelligence density and the like, and the informationized project often has the problems of delay and uncontrollable quality, and the existing project progress management mode needs to manually input data and automatically report the progress. The operation of filling a large amount of manual data not only consumes a large amount of time and has low efficiency, but also has the problems of inaccuracy and objectivity in filling data. Therefore, there is an urgent need for an automated and intelligent project progress data processing system that can automatically, efficiently and accurately process project data and provide powerful support for project decision-making.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims to provide an intelligent data processing system and method for an informationized project, which can automatically identify, process and analyze project progress data, reduce manual operation and improve the project progress data processing efficiency.
In order to achieve the above object, the present invention provides an intelligent data processing method for an informationized project, the method comprising the steps of:
S1, a data collection module collects data from a data terminal, wherein the data terminal comprises voice equipment, a database and/or a file, and the data is periodically collected according to a set collection time point and collection frequency;
S2, the data filling module matches, grabs and fills the acquired data, and verifies and checks the data in the filling process, including checking the consistency and accuracy of the data;
S3, the data calculation module calculates and analyzes the filled data according to a set calculation model so as to be used for visual presentation of the data;
S4, the data storage module stores the result after calculation and analysis in a storage medium so as to facilitate subsequent inquiry, retrieval and use;
S5, the data visualization module displays the result after data calculation and analysis on the real equipment in a graphic and/or image mode.
Further, in step S1, the data collection module is configured to collect data of the following two aspects, namely, informationized project plan data and adjustment data of the plan in the process; secondly, project task completion data.
Further, step S1 comprises the sub-steps of:
S1.1, decomposing project tasks by using a project management tool WBS, wherein the project tasks are subjected to factor decomposition according to an implementation process to form a WBS dictionary for describing specific task detailed information, and task attributes comprise task responsible persons, task dependency relations, task starting time, task ending time, task acceptance criteria, task delivery objects and task approvers;
S1.2, constructing a project schedule by using an MS project tool, presenting the starting time, the finishing time and the task dependency relation of each task, and displaying the overall plan of the project in the form of a network diagram and a cross-road diagram, wherein the formed project schedule is a management baseline for project propulsion;
S1.3, embedding functions of WBS and Project tools into a system, editing on line through the system, and establishing a responsibility relationship between Project members and Project tasks;
S1.4, converting real-time data aiming at project tasks into texts through external equipment in real time by using voice of a staged conference and/or communication; the text is subjected to rapid word segmentation, part-of-speech tagging, named entity recognition and noun phrase extraction by using a Python natural language processing tool spaCy, and the designated task name and attribute elements thereof are captured from the text.
In step S1.4, the external device includes an intelligent recording pen, a mobile phone or a computer terminal with intelligent recording software, and a device for converting the recording into text.
Further, in step S2, the automatic data filling unit uses Python Selenium automation tools to create WebDriver examples, locates form elements of the automatic data filling unit, and uses the send_keys method to fill the extracted task keywords and attributes into the system form.
Further, in step S3, the data calculation and analysis module is configured to implement progress data calculation and calculate a project progress deviation index.
Further, a project progress deviation index calculation formula spi=cw/SC, wherein SPI is project progress deviation, CW is task actual time consumption, SC is project plan duration; if SPI >1, the project schedule is stated to be advanced, otherwise SPI <1, the project schedule is stated to be delayed, SPI=1, and the project is stated to be strictly carried out according to the plan; setting a first deviation threshold and a second deviation threshold, reminding if the SPI is larger than the first deviation threshold, and performing progress intervention if the SPI is larger than the second deviation threshold; the second deviation threshold is greater than the first deviation threshold.
Further, in step S4, the data storage module stores the collected data and the data generated in the project process in a cloud storage manner.
Further, in step S5, the data visualization module uses Tableau visualization tools to perform data presentation, connects the database through the data connector of Tableau, sorts and groups the data, creates a data presentation dashboard, presents a project SPI line graph, and presents a project progress condition.
On the other hand, the invention provides an intelligent data processing system for an informationized project, which comprises a data collection module, a data reporting module, a data calculation module, a data storage module and a data visualization module; the system is used for realizing the intelligent data processing method of the informationized project.
The beneficial effects are that: according to the project intelligent data processing system and method, the project intelligent decision and optimization are realized by collecting, optimizing, mining and displaying the informationized project progress data and processing and analyzing the data by utilizing artificial intelligence and machine learning technologies. The invention realizes automatic identification, processing and analysis of data by using artificial intelligence technology, greatly improves the data processing efficiency, reduces manual operation and reduces error rate; powerful support can be provided for project decision making, and project success rate is improved. The project intelligent data processing method can realize the operations of automatic data collection, processing, analysis, decision making and the like, and improves the working efficiency and the quality. Meanwhile, through the data visualization module, a user can more intuitively understand and use the data.
Drawings
FIG. 1 illustrates a flow architecture diagram of an informational item intelligence data processing system and method in accordance with the present invention;
FIG. 2 illustrates an exploded schematic view of an item WBS according to the present invention;
Fig. 3 shows an example of a gante diagram of the progress situation of one item.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Specific embodiments of the present invention are described in detail below with reference to fig. 1-3. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
The invention provides an intelligent data processing system for an informationized project, which comprises a data collection module, a data reporting module, a data calculation module, a data storage module and a data visualization module.
FIG. 1 shows the flow of an informationized project intelligent data processing method according to the invention, which comprises the following steps:
S1, firstly, a data collection module collects data from a data terminal, wherein the data collection module comprises voice equipment, a database, a file and the like, the integrity and the legality of the data are ensured in the collection process, meanwhile, the data are required to be collected regularly, and the collection time point and the collection frequency can be defined.
S2, secondly, the data filling module matches, grabs and fills the acquired data so as to meet the requirements of subsequent calculation and analysis. In the process of filling, necessary verification and check, such as checking the consistency and accuracy of the data, are needed.
S3, again, the data calculation module calculates and analyzes the filled data by using the set calculation model so as to be used for visual presentation of the data.
S4, the data storage module stores the calculated and analyzed result in a reliable storage medium so as to facilitate subsequent inquiry, retrieval and use.
And S5, finally, the data processing result is displayed by the data visualization module in a pattern, an image and other modes so as to be convenient for a user to understand and analyze.
Specifically, in step S1, the data collection module is configured to collect data of the following two aspects, namely, informationized project plan data and adjustment data of a plan in the process; secondly, project task completion data. Collection of project plan data comprising the sub-steps of:
s1.1, decomposing project tasks by using a project management tool WBS, wherein the project tasks are decomposed step by step according to an implementation process to form a WBS dictionary for describing specific task detailed information, and task attributes comprise task responsible persons, task dependency relations, task Starting Time (ST), task ending time (OT), task acceptance criteria, task deliverables, task approvers and the like.
The task decomposition according to the implementation process specifically comprises the following steps: defining a project range, defining a final target and a deliverable result of the project, and dividing according to main stages of informationized project; step three, refining step by step until the decomposed tool kit can clearly define specific work content, expected results and responsible persons; the fourth step creates a WBS dictionary. As shown in fig. 2, taking a mobile office project WBS as an example, the project propulsion stage is decomposed into a planning stage, a demand stage, a design stage, a coding stage, a testing stage and a deployment trial, and then the tasks are further refined, for example, the tasks in the planning stage are subdivided into market research, feasibility study and scheme demonstration.
S1.2, constructing a project schedule by using an MS project tool, presenting the starting time, the finishing time and the task dependency relation of each task, and presenting the overall plan of the project in the form of a cross-track chart, wherein the formed project schedule is a management baseline for project propulsion.
As shown in fig. 3, the progress of an item of 2024 from 2 months to 3 months is shown by a gantt chart example, and the entire item is presented to include tasks such as item planning, demand, design, encoding, testing and deployment, and the work duration of each task and the dependency relationship between tasks. For example, the "design" task can be "coded" after completion, so there is a relationship line pointing to the right between the two tasks; the end date of the "code" in turn determines the start date of the "test", so that there is also a relationship line pointing to the left. "deployment" is the work that begins after all other tasks are completed.
S1.3, allowing a user to define a task level and subtasks by supporting a support custom field, adding task attributes, and realizing a WBS function; integrating the function of Project software, calling WBS data through Project Web service API, presenting Gantt chart, and displaying time line constructed based on WBS and dependency relationship between tasks.
S1.4, real-time data aiming at project tasks are converted into texts in real time through external equipment such as intelligent recording software mobile phones or computer terminals including intelligent recording pens for flying, intelligent recording assistants for hearing and recording and text conversion. The text is subjected to rapid word segmentation, part-of-speech tagging, named entity recognition, noun phrase extraction and the like by using a Python natural language processing tool spaCy, and the designated task name and attribute elements thereof are captured from the text.
Examples: the development team members were engaged in a meeting at day 10, 16, 2023, discussing the user login function development tasks. As the task involves the user's experience and the security of the system. Specific requirements are set for task attributes: the mobile phone is started in 10 months and 16 days, is completed in two weeks, and needs to support two login modes of a mobile phone number and a mailbox.
Python
import spacy
# Loading pre-training model
nlp = spacy.load('en_core_web_sm')
Input conference recording text
Text= "," # the above conference record text content
# Uses spacy to perform word segmentation, named entity recognition and other processes
doc = nlp(text)
# Definition function grabbing task name and attribute element thereof
def extract_task_info(doc):
tasks = []
for ent in doc.ents:
if ent.label_ == 'EVENT' or ent.label_ == 'PRODUCT':
task_name = ent.text
properties = []
for token in ent.sent:
if token.dep_ == 'amod' and token.head.text == task_name:
properties.append(token.text)
elif token.dep_ == 'attr' and token.head.text == task_name:
properties.append(token.text)
tasks.append({"task_name": task_name, "properties": properties})
return tasks
# Call function acquisition task and attribute thereof
tasks_info = extract_task_info(doc)
# Output result
for task in tasks_info:
Print (f "task name: { task [ 'task_name' ] })
Print (f ' attribute element: { ', join (task [ ' properties ') } \n ')
The grabbing result is as follows:
Task name: user login function development
Attribute element: the starting date (for example, the starting date is 10 months and 16 days after the recognition is obtained), the period is completed (for example, the recognition is completed in two weeks), and two login modes of the mobile phone number and the mailbox are supported.
In step S2, the data filling module is configured to automatically fill the collected data into the system, where the data filling module includes a data automatic filling unit and a manual auditing unit. The data automatic filling unit uses Python Selenium automation tools to create WebDriver examples, establishes connection with the browser, controls the Chrome browser behavior by using ChromeDriver, and corresponds to a Firefox browser by FirefoxDriver. After WebDriver examples are acquired, the table element of the automatic data reporting unit is precisely positioned by utilizing the ID positioning of Selenium, the positioning strategies such as Name and the like. If the HTML code of the input box is < input id= "taskKeyword" type= "text" >, this task key input box can be located by driver. And calling a send_keys method, and filling the task keywords and attribute information thereof into the positioned form fields. After the data is filled, the action of clicking the submit button is simulated, so that the data can be uploaded to a background database for storage. After the data is successfully reported and stored, the WebDriver examples are closed, the system resources are released, and the next round of data reporting task is waited.
The accuracy of keyword and attribute grabbing is submitted by establishing a mapping relation between common attributes and keywords and expanding and perfecting the mapping relation according to project pushing. Informationized projects are generally divided into five phases, demand analysis, design, development, testing, and deployment. Taking the requirement analysis stage as an example, the table element of the stage comprises: the keyword and attribute to be extracted are the requirement title, the expected completion date and the priority information. The specific implementation code is as follows:
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import Select
import datetime
# initialize WebDriver instance, here by example of Chrome
driver = webdriver.Chrome()
Form page locating to demand analysis stage
driver.get("http://project-management-system.com/requirement")
# Locate and fill out requirement description
description_field = driver.find_element_by_name("requirement_description")
description_field.clear()
Description_field_key ("extracted keywords: user login function requirement")
# Locate and select expected completion date
due_date_field = driver.find_element_by_name("due_date")
due_date_field.clear()
due_date_field.send_keys(datetime.date(2023, 12, 31))
# Locate and select priority
priority_select = Select(driver.find_element_by_name("priority"))
Priority_select_by_visible_text ("high")
Suppose "high" has been extracted as a priority key
Submit_button=driver.find_element_by_id ("submit-button") # positions the submit button according to actual conditions
Click () # click on submit button
After successful commit of data #, the WebDriver instance is closed
driver.quit()
And distributing auditing permissions according to the task distribution matrix, wherein each person takes charge of verifying and auditing the task progress of the task, team takes charge of auditing the tasks in the group, and so on until the person in charge of the project takes charge of verifying the staged data. And timely giving reminding, warning and the like for the condition that data audit is not timely filled. If the person still does not audit for more than one week, the system takes the filling condition as the reference, and cancels the audit authority of the person for the condition of continuously generating 3 times of non-punctual audit.
In step S3, the data calculation and analysis module is configured to implement progress data calculation, and calculate a project progress deviation index. A fixed time may be selected as the calculation date, such as friday for example. The calculation formula spi=cw/SC, where SPI is project progress deviation, CW is task actual time consumption (task actual end time minus start time, i.e., cw=st-OT), SC is project plan duration. If SPI >1, the description of the project progress leads, whereas SPI <1, the description of the project progress holds off, while spi=1, the description of the project proceeds strictly as planned. Meanwhile, a deviation threshold is set, the SPI is larger than 1.1 to give a prompt, and if the SPI is larger than 1.2, progress intervention is needed, so that the project can be completed in expected time.
Further, to more fully evaluate the completion of the current project, the present invention uses a composite progress deviation index (SPIx) to calculate, taking into account not only the time factor (SPI), but also the quality factor (QPI) and cost factor (CPI) of the completion. Wherein SPI is used as a basic progress deviation index: spi=actual completion workload/planned workload, which is estimated by the ratio of the task actual time consuming CW (Current Work, cw=st-OT) to the planned workload; QPI = actual quality score/target quality score, measured by comparing the actual quality of each stage or task to a predetermined target quality; CPI = actual cost of completed work/budget cost of completing the same workload. SPIx =w1×spi+w2×qpi+w3×cpi, where w1, w2, w3 are weights of three factors in the evaluation system, namely time, quality and cost, respectively. For example, in one embodiment, it is suggested that the progress bias is mainly focused, so that w1, w2, w3 take values of 0.5, 0.3, 0.2, respectively. When CPIx > 1.1: sending out early warning to prompt that the current project has good progress, but the risk of excessive investment or excessive quality is possible; when CPIx < 0.9: immediate intervention is required and the project may suffer from a lag in progress, quality problems or cost.
Illustrating:
Assuming that the current project demand phase just ends, the actual time is 4 weeks, the planning time is 3 weeks, the actual quality score is 85 minutes (full 100), the target quality is 90 minutes, the actual cost is 20,000, and the budget cost is 18,000.
SPI = 4/3 ≈ 1.33
QPI = 85/90 ≈ 0.944
CPI = 20000/18000 ≈ 1.111
SPIx = 0.5 * 1.33 + 0.3 * 0.944 + 0.2 * 1.111 ≈ 1.167
And according to the calculation result, the system sends out early warning to prompt that the current project has good progress but excessive cost investment.
In step S4, the data storage module is configured to store the collected data and the data generated in the project process in time, and adopt a cloud storage manner, so that data management and use are facilitated. Unstructured data such as voice and text are stored under the same project folder in a time naming mode. Selenium the task and attribute data for automated tool crawling is stored using a relational database. And according to the execution frequency of the automatic grabbing data, the key words and the attribute data are updated regularly, so that the instantaneity and the accuracy of data storage are ensured. And setting access, inquiry, modification and deletion authorities of data according to personnel authorities in the responsibility matrix, and performing daily diary storage on all modification records, wherein the modification of the data can be inquired and tracked.
In step S5, the data visualization module provides a visual monitoring interface by adopting a project intelligent data processing method, so as to monitor the project progress in real time and discover and solve the problems in time. The data visualization module plays a key role in project progress management, and the complex project progress data are converted into a visual and understandable chart through Tableau, so that a manager is helped to monitor in real time and respond to deviation quickly. Firstly, connecting databases storing project plans, actual progress, quality scores and cost information by utilizing a Tableau data connector function, preprocessing acquired data such as invalid or repeated data removal, missing value filling and the like, and sequencing the data according to a time dimension (such as date, week or month) so as to generate a SPIx analysis chart of a time sequence; data may also be grouped by project phase, task type, or other relevant fields, showing SPIx trend changes in different dimensions. Finally, a new worksheet is created in Tableau, a 'line' chart type is selected, an X axis is set as a time sequence field, a Y axis is a SPIx value obtained through calculation, and two broken lines are created: one representing the project plan baseline (green), i.e., SPIx is an ideal state of 1, and one representing the actual progress of the project (blue), with Tableau color condition formatting function, a yellow alert is displayed when SPIx is greater than 1.1, and a red alert is displayed when SPI is greater than 1.2, facilitating quick identification of potential problem areas. The influence of each factor on the deviation was analyzed using Tableau interactive analysis functions. Subsequent progress conditions can be adjusted using Tableau interactive functions to view the impact of the adjustment measures on the data in order to adjust project progress at any time.
Carrying out centralized dynamic presentation on data related to project progress, providing various dynamic forms such as a list form, an instrument panel and the like, and presenting the progress deviation of weekly and monthly projects; meanwhile, the click measure can be used for checking the completion condition of the measure and contributing to the whole project. Multidimensional queries are supported, such as execution of a project schedule that can be queried by a specified date.
The invention has the advantages that: by using artificial intelligence technology, automatic identification, processing and analysis of data are realized, the data processing efficiency is greatly improved, the manual operation is reduced, and the error rate is reduced; powerful support can be provided for project decision making, and project success rate is improved. The project intelligent data processing method can realize the operations of automatic data collection, processing, analysis, decision making and the like, and improves the working efficiency and the quality. Meanwhile, through the data visualization module, a user can more intuitively understand and use the data.
Any process or method description in a flowchart of the invention or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, which may be implemented in any computer-readable medium for use by an instruction execution mechanism, apparatus, or device, such computer-readable medium may be any medium that contains a program for storing, communicating, propagating, or transmitting for use by the execution mechanism, apparatus, or device. Including read-only memory, magnetic or optical disks, and the like.
In the description herein, reference to the term "embodiment," "example," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the different embodiments or examples described in this specification and the features therein may be combined or combined by those skilled in the art without creating contradictions.
While embodiments of the present invention have been shown and described, it will be understood that the embodiments are illustrative and not to be construed as limiting the invention, and that various changes, modifications, substitutions and alterations may be made by those skilled in the art without departing from the scope of the invention.
Claims (6)
1. An intelligent data processing method for an informationized project, which is characterized by comprising the following steps:
S1, a data collection module collects data from a data terminal, wherein the data terminal comprises voice equipment, a database and/or a file, and the data is periodically collected according to a set collection time point and collection frequency;
S2, the data filling module matches, grabs and fills the acquired data, and verifies and checks the data in the filling process, including checking the consistency and accuracy of the data;
S3, the data calculation module calculates and analyzes the filled data according to a set calculation model so as to be used for visual presentation of the data;
S4, the data storage module stores the result after calculation and analysis in a storage medium so as to facilitate subsequent inquiry, retrieval and use;
S5, the data visualization module displays the result after data calculation and analysis on the real equipment in a graphic and/or image mode;
Step S1 comprises the following sub-steps:
S1.1, decomposing project tasks by using a project management tool WBS, wherein the project tasks are subjected to factor decomposition according to an implementation process to form a WBS dictionary for describing specific task detailed information, and task attributes comprise task responsible persons, task dependency relations, task starting time, task ending time, task acceptance criteria, task delivery objects and task approvers;
S1.2, constructing a project schedule by using an MS project tool, presenting the starting time, the finishing time and the task dependency relation of each task, and displaying the overall plan of the project in the form of a network diagram and a cross-road diagram, wherein the formed project schedule is a management baseline for project propulsion;
S1.3, embedding functions of WBS and Project tools into a system, editing on line through the system, and establishing a responsibility relationship between Project members and Project tasks;
s1.4, converting real-time data aiming at project tasks into texts through external equipment in real time by using voice of a staged conference and/or communication; using a Python natural language processing tool spaCy to perform quick word segmentation, part-of-speech tagging, named entity recognition and noun phrase extraction on the text, and capturing a designated task name and attribute elements thereof from the text;
In S3, the composite progress deviation index SPIx is used for calculation, which not only considers the time factor SPI, but also combines the finished quality factor QPI and the cost factor CPI, wherein SPI is used as the basic progress deviation index:
SPI = actual completion workload/planned workload, estimated by the ratio of the task actual time consuming CW to the planned workload;
QPI = actual quality score/target quality score, measured by comparing the actual quality of each stage or task to a predetermined target quality;
CPI = actual cost of completed work/budget cost of completing the same workload;
SPIx =w1×spi+w2×qpi+w3×cpi, where w1, w2, w3 are weights of three factors in the evaluation system, namely time, quality and cost, respectively;
When CPIx > 1.1: sending out an early warning to prompt that the current project has good progress, but risks of excessive investment or excessive quality exist; when CPIx < 0.9: immediate intervention is required, and the project has a progress lag, quality problem or cost-off condition.
2. The method for intelligent data processing of informationized projects according to claim 1, wherein in step S1, the data collection module is configured to collect data of informationized project plan data and adjustment data of the plan in the process; secondly, project task completion data.
3. The method for intelligent data processing of informationized items according to claim 1, wherein in step S1.4, the external device comprises an intelligent recording pen, a mobile phone or a computer terminal with intelligent recording software, and a device for converting a recording into a text.
4. The method according to claim 1, wherein in step S2, the automatic data filling unit uses Python Selenium automation tools to create WebDriver instances, locates form elements of the automatic data filling unit, and uses send_keys method to fill the extracted task keywords and attributes into the system form.
5. The method for intelligent data processing of an informationized project according to claim 1, wherein in step S4, the data storage module stores the collected data and the data generated during the project in a cloud storage manner.
6. The method for intelligent data processing of an information item according to claim 1, wherein in step S5, the data visualization module uses Tableau visualization tools to perform data presentation, connects databases through a data connector of Tableau, sorts and groups the data, creates a data presentation dashboard, presents an item SPI line drawing, and presents an item progress situation.
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| CN117130652A (en) * | 2023-06-27 | 2023-11-28 | 中国兵器装备集团上海电控研究所 | Software project management system and method based on rapid development model |
| CN117131034A (en) * | 2023-09-11 | 2023-11-28 | 北京红山信息科技研究院有限公司 | Big data workbench for intelligent operation and maintenance |
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| CN117130652A (en) * | 2023-06-27 | 2023-11-28 | 中国兵器装备集团上海电控研究所 | Software project management system and method based on rapid development model |
| CN117131034A (en) * | 2023-09-11 | 2023-11-28 | 北京红山信息科技研究院有限公司 | Big data workbench for intelligent operation and maintenance |
| CN117350542A (en) * | 2023-10-13 | 2024-01-05 | 北京互时科技股份有限公司 | Digital delivery data visualization system and application method thereof |
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