Project management system based on data integration

Abstract

The invention discloses a project management system based on data integration, relates to the technical field of project management, and solves the technical problems that in actual project management, key properties of all processes of a project are different, when the key process of the project is abnormal, the whole project may be stopped, and the project is overdue. The method comprises the following steps: acquiring full-process data of a project, dividing the project according to the full-process data, and determining an acquisition frequency; acquiring real-time data of the project based on the acquisition frequency; preprocessing the real-time data to obtain project data; performing abnormal early warning on the project according to the project data, and generating an early warning signal; adjusting the project based on the early warning signal; displaying the project data to related project personnel; obtaining adjustment data of project personnel; adjusting the project according to the adjustment data; and normal proceeding of the project can be ensured and the project is completed on schedule.

Description

Technical Field

[0001] The invention belongs to the field of project management, in particular to a project management system based on data integration. Background Art

[0002] Data integration is the process of combining data from multiple different sources to provide users with a single, unified view; project management is the activity of planning, organizing, leading and controlling resources through systematic methods and processes to achieve specific goals, involving stages such as project planning, project execution, project monitoring and project closure, aiming to ensure that the project is completed within the established time, budget and quality range; data integration enables the project team to quickly access and analyze all relevant data to make more informed and timely decisions. Data integration ensures that all people involved in the project can understand the progress, cost, quality and other aspects of the project in real time, which helps to build trust, reduce conflicts and improve the efficiency of team collaboration.

[0003] The prior art (invention patent application with publication number CN104573991A) discloses a project process management system, which includes: a project initialization module for determining project information and storing the information in a database, and then creating a project schedule and resource planning; a project tracking module for recording and updating the project operation log and the status of each task in the project, maintaining project information; a project portfolio module for querying projects, exporting query results, and obtaining relevant statistical results based on query results; a calendar module for filtering information, schedules, and resource plans of projects that match user information, and then displaying the filtering results, and an agile module for providing a dashboard to display the task status and the version to which it belongs in the project based on classified information; the prior art integrates the complete development process of the project and standardizes the project management process; however, in actual project management, the criticality of each project process is different. When an abnormality occurs in a key project process, the entire project may stagnate, causing the project to be overdue.

[0004] The present invention provides a project management system based on data integration to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a project management system based on data integration, which is used to solve the problem that the prior art integrates the complete development process of the project and standardizes the project management process; however, in actual project management, the criticality of each project process is different. When an abnormality occurs in a key project process, it may cause the entire project to stagnate, resulting in technical problems such as the project being overdue.

[0006] To achieve the above object, the first aspect of the present invention provides a project management system based on data integration, including: a project management module, and a collection and division module and a terminal coordination module connected thereto;

[0007] Collection and division module: used to collect the full process data of the project, divide the project according to the full process data, and determine the collection frequency; obtain the real-time data of the project based on the collection frequency;

[0008] Project management module: used to pre-process real-time data to obtain project data; to issue abnormal warnings to projects based on project data and generate warning signals; to handle abnormal situations of projects based on warning signals;

[0009] Terminal collaboration module: used to display project data to relevant project personnel; obtain adjustment data from project personnel; and adjust projects based on the adjustment data.

[0010] Preferably, the dividing of the projects according to the full process data and determining the collection frequency includes:

[0011] Retrieve the full process data of the project; the full process data includes: project type, project process and project deadline; sort the project processes in the full process data according to the order of project progress to obtain a project process table;

[0012] Obtain the historical completion time of the project process and important data of the project process; divide the project deadline into several project processes based on the historical completion time; analyze the key coefficients of the project process based on the important data; determine the collection frequency of the project process based on the key coefficients.

[0013] The present invention divides projects according to project processes and divides project deadlines into project processes according to historical completion times, and can set the progress of projects, laying a foundation for subsequent monitoring and management of projects.

[0014] Preferably, the key coefficients of the project process analyzed based on important data include:

[0015] Retrieve important data of the project process; important data include: importance, project impact coefficient; project impact coefficient is obtained based on historical data;

[0016] The importance of important data and the coefficient of impact on the project are marked as ZC and YX respectively; the key coefficients of several project processes are calculated by the formula GX=α×exp[lg(ZC+1)]+β×YX^2; where: α and β are proportional coefficients greater than 0.

[0017] The present invention calculates the key coefficient of the project process according to the important data of the project process, can evaluate the importance of several project processes, lays a foundation for the setting of subsequent collection frequency, is conducive to improving the attention to key nodes, and promotes the normal progress of the project.

[0018] Preferably, the impact coefficient on the project is obtained based on historical data, including:

[0019] Obtain historical data on the impact of project processes on the project, and integrate the historical data into an impact input sequence in chronological order;

[0020] Call the impact analysis model, input the impact input sequence into the impact analysis model, and obtain the impact coefficient of the corresponding project process on the project; wherein, the impact analysis model is built based on the artificial intelligence model.

[0021] It should be noted that the historical data on the impact of project processes on projects refers to situations in which abnormalities in project processes occurred during the implementation of other historical projects, leading to project delays, overdue projects, etc.

[0022] The present invention analyzes the impact coefficient of the project process on the project based on the historical data of the impact of the project process on the project using a trained impact analysis model; analyzing the impact coefficient based on historical data can make the analysis result more accurate, which is conducive to improving the accuracy of key node analysis.

[0023] Preferably, the impact analysis model is constructed based on an artificial intelligence model, including:

[0024] Acquire a standard data set; wherein the standard data set includes standard input data consistent with the content attributes of the impact input sequence, and standard output data consistent with the content attributes of the impact coefficient on the project;

[0025] Divide the standard data set into a training set, a validation set, and a test set according to a set ratio; use the training set to train the artificial intelligence model; use the validation set to adjust the internal parameters of the artificial intelligence model; use the test set to test the trained and adjusted artificial intelligence model to obtain performance indicators;

[0026] Determine whether the performance indicators of the artificial intelligence model are all greater than the indicator threshold. If yes, mark the artificial intelligence model as an impact analysis model; if not, retrain the artificial intelligence model; wherein the artificial intelligence model includes a convolutional neural network model or a feedforward neural network model.

[0027] It should be noted that the performance indicators of the artificial intelligence model include accuracy, recall rate, F1 score and stability; the division ratio of the data set is assessed by experts. When the training of the artificial intelligence model fails, the division ratio of the data set is adjusted and the artificial intelligence model is retrained.

[0028] Preferably, determining the acquisition frequency of the project process according to the key coefficient includes:

[0029] Retrieve key coefficients of several project processes; obtain coefficient thresholds; compare the key coefficients with the coefficient thresholds to obtain key levels corresponding to several project processes;

[0030] Obtain the level collection library, match the key levels of the project process with the level collection library, and obtain the collection frequency of the corresponding project process.

[0031] The present invention compares the key coefficient of the project process with the coefficient threshold to obtain the key level corresponding to the project process, and determines the collection frequency of the project process according to the key level corresponding to the project process. This can increase the data collection of key project processes, promptly discover abnormal situations that occur in the project, and help ensure the normal progress of the project.

[0032] Preferably, the preprocessing of the real-time data to obtain the project data includes:

[0033] Retrieve real-time data, including real-time process, real-time status, real-time cost and real-time quality;

[0034] Identify outliers, missing values, and duplicate data in real-time data; remove outliers and duplicate data, and supplement missing values; build a data integration library, mark the processed real-time data as project data, and store it in the data integration library.

[0035] The present invention preprocesses the collected real-time data, can eliminate abnormal values ​​and repeated data in the real-time data, and supplement missing values; can ensure the integrity of the data, eliminate invalid data, and is conducive to improving the efficiency of data analysis.

[0036] Preferably, the abnormal warning of the project based on the project data and generating a warning signal includes:

[0037] Retrieve project data; obtain project planning data, where planning data includes: the start and end time of the project process, the cost of the project process, and the quality standard of the project process;

[0038] Compare the real-time progress in the project data with the planned data. When the real-time progress lags behind the planned data, an early warning signal is generated; otherwise, the real-time status in the project data is analyzed;

[0039] Determine whether the real-time status in the project data is abnormal or suspended; if yes, generate an early warning signal; if no, analyze the real-time cost in the project data;

[0040] Determine whether the real-time cost in the project data is greater than the cost of the project process; if yes, generate an early warning signal; if no, analyze the real-time quality in the project data;

[0041] Determine whether the real-time quality of the project data meets the quality standards of the project process; if yes, continue to monitor and manage the project; if not, generate an early warning signal.

[0042] The present invention analyzes and compares the project data with the planned data to determine whether the project process is overdue, over budget, substandard or abnormal in status. When an abnormal situation occurs in the project process, it can promptly issue an early warning of the abnormality, which is conducive to ensuring that the project is carried out on time and with quality.

[0043] Preferably, the processing of abnormal conditions of the project based on the early warning signal includes:

[0044] Retrieve the early warning signal and abnormal sub-data in the project data; retrieve the corresponding exception processing library according to the abnormal sub-data;

[0045] Match the abnormal sub-data with the exception handling library, obtain the corresponding exception handling measures, and handle the exception according to the exception handling measures.

[0046] It should be noted that when an abnormal situation occurs in the project, the customer must be informed immediately and the handling measures must also be informed to the customer at the same time. The abnormal situation will be handled after the customer confirms it; if the customer has other handling requirements, the abnormal situation will be handled according to the customer's handling requirements.

[0047] The present invention handles abnormal situations that occur in the project, can timely ensure the normal operation of the project process, is conducive to ensuring the normal operation of the entire project, and ensures that the project can be delivered on time and with quality.

[0048] Preferably, adjusting the project according to the adjustment data includes:

[0049] Retrieve adjustment data; the adjustment data includes: changes in project deadlines, changes in project status, or adjustments to project costs;

[0050] When the project deadline is extended in the adjustment data, the deadlines of several project processes are increased according to the extended deadline; when the project deadline is shortened in the adjustment data, the deadlines of several project processes are reduced according to the shortened time;

[0051] When the project status is changed to suspended or postponed in the adjustment data, the corresponding project is suspended and the adjustment data of the project is continuously monitored;

[0052] When the project cost is increased in the adjustment data, better quality materials are selected according to the increased amount; when the cost is reduced in the adjustment data, more cost-effective materials are selected according to the reduced amount.

[0053] It should be noted that when adjusting a project, it is necessary to confirm with the customer. Only when the customer confirms that it is correct can the project be adjusted.

[0054] The present invention timely adjusts the project according to the customer's adjustment data, and timely adjusts the project according to the customer's requirements, so that the project can meet the customer's expectations, which is beneficial to improving customer satisfaction and the company's reputation.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention divides projects according to project processes, and divides project deadlines into project processes according to historical completion times, can set the progress of projects, and lays a foundation for subsequent monitoring and management of projects; according to historical data of the impact of project processes on projects, the impact coefficient of project processes on projects is analyzed using a trained impact analysis model; analyzing the impact coefficient according to historical data can make the analysis results more accurate, which is beneficial to improving the accuracy of key node analysis; calculating the key coefficient of the project process according to important data of the project process can evaluate the importance of several project processes, lay a foundation for the subsequent setting of acquisition frequencies, and is beneficial to increasing the attention to key nodes and promoting the normal progress of the project; comparing the key coefficient of the project process with the coefficient threshold to obtain the key level corresponding to the project process, and determining the acquisition frequency of the project process according to the key level corresponding to the project process, can increase data collection of key project processes, timely discover abnormal situations in the project, and help ensure the normal progress of the project.

[0057] 2. The present invention pre-processes the collected real-time data, can eliminate abnormal values ​​and duplicate data in the real-time data, and supplement missing values; can ensure the integrity of the data, eliminate invalid data, and help improve the efficiency of data analysis; analyze and compare the project data with the planned data to determine whether the project process is overdue, over-budget, substandard or abnormal in status. When an abnormal situation occurs in the project process, it can promptly issue an early warning of the abnormality, which is conducive to ensuring that the project is carried out on time and with quality; handle the abnormal situation in the project, and can promptly ensure the normal operation of the project process, which is conducive to ensuring the normal operation of the entire project and ensuring that the project can be delivered on time and with quality; timely adjust the project according to the customer's adjustment data and timely adjust the project according to the customer's requirements, so that the project can meet the customer's expectations, which is conducive to improving customer satisfaction and the company's reputation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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.

[0059] Figure 1 A schematic diagram of the overall steps of the system of the present invention;

[0060] Figure 2 A schematic diagram of the acquisition frequency setting steps of the present invention;

[0061] Figure 3 This is a schematic diagram of the abnormal monitoring and processing steps of the project of the present invention;

[0062] Figure 4 It is a schematic diagram of the project adjustment steps of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0064] See also Figure 1 , the first aspect of the present invention provides a project management system based on data integration, including: a project management module, and a collection and division module and a terminal collaboration module connected thereto;

[0065] Collection and division module: used to collect the full process data of the project, divide the project according to the full process data, and determine the collection frequency; obtain the real-time data of the project based on the collection frequency;

[0066] Project management module: used to pre-process real-time data to obtain project data; to issue abnormal warnings to projects based on project data and generate warning signals; to adjust projects based on warning signals;

[0067] Terminal collaboration module: used to display project data to relevant project personnel; obtain adjustment data from project personnel; and adjust projects based on the adjustment data.

[0068] See also Figure 2, collect the full process data of the project, where the full process data includes: project type, project process and project deadline; sort the project processes in the full process data according to the order of project progress to obtain a project process table;

[0069] Obtain the historical completion time of the project process; divide the project deadline into several project processes based on the historical completion time; obtain the historical data of the impact of the project process on the project, and integrate the historical data into an impact input sequence in chronological order; call the impact analysis model, input the impact input sequence into the impact analysis model, and obtain the impact coefficient of the corresponding project process on the project; wherein, the impact analysis model is built based on the artificial intelligence model.

[0070] For example, suppose that project A is managed and the full process data of project A is collected; project A is divided into bidding stage, procurement stage, early project stage, mid-project stage, late project stage and project liquidation according to the process;

[0071] The total duration of the project is 230 days, which is divided into the following stages: 30 days for the bidding stage, 20 days for the procurement stage, 30 days for the early stage of the project, 70 days for the middle stage of the project, 50 days for the late stage of the project, and 30 days for the project liquidation;

[0072] Obtain historical data on the impact of project processes on the project, and integrate the historical data into an impact input sequence in chronological order; call the impact analysis model, input the impact input sequence into the impact analysis model, and obtain the impact coefficient of the project process on the project.

[0073] It should be noted that the maximum value of the project impact coefficient is set to 5.

[0074] Obtain important data of the project process; the important data include: importance, project impact coefficient; the project impact coefficient is obtained based on historical data; the importance and project impact coefficient in the important data are marked as ZC and YX respectively; the key coefficients of several project processes are calculated by the formula GX = α × exp [lg (ZC + 1)] + β × YX^2; where α and β are proportional coefficients greater than 0; the settings of α and β are set based on actual experience, and the value range is (0,100];

[0075] Obtain coefficient thresholds; compare key coefficients with coefficient thresholds to obtain key levels corresponding to several project processes; obtain a level collection library, match the key levels of project processes with the level collection library, and obtain collection frequencies of corresponding project processes; obtain real-time data of projects based on collection frequencies.

[0076] For example, suppose that the key coefficients of the early, mid and late stages of the project process of Project A are calculated, α = 10; β = 1. The specific steps are as follows:

[0077] Early stage of the project: importance ZC = 78%; project impact coefficient YX = 3; the key coefficient is GX = 75.66;

[0078] Mid-term of the project: Importance ZC = 92%, Project Impact Coefficient YX = 5; The Key Coefficient is GX = 96.59;

[0079] Late stage of the project: importance ZC = 65%, project impact coefficient YX = 4; the key coefficient is GX = 77.69;

[0080] If the key coefficient of the early and late stages of the project is greater than the coefficient threshold of 70 and less than the coefficient threshold of 90, the key levels of the early and late stages of the project are intermediate, and the corresponding collection frequency is once every three days. If the key coefficient of the mid-stage project is greater than the coefficient threshold of 90, the key level of the late stage of the project is advanced, and the corresponding collection frequency is once a day.

[0081] It is worth noting that the impact analysis model is built based on the artificial intelligence model, including:

[0082] Acquire a standard data set; wherein the standard data set includes standard input data consistent with the content attributes of the impact input sequence, and standard output data consistent with the content attributes of the impact coefficient on the project;

[0083] Divide the standard data set into a training set, a validation set, and a test set according to a set ratio; use the training set to train the artificial intelligence model; use the validation set to adjust the internal parameters of the artificial intelligence model; use the test set to test the trained and adjusted artificial intelligence model to obtain performance indicators;

[0084] Determine whether the performance indicators of the artificial intelligence model are all greater than the indicator threshold. If yes, mark the artificial intelligence model as an impact analysis model; if not, retrain the artificial intelligence model; wherein the artificial intelligence model includes a convolutional neural network model or a feedforward neural network model.

[0085] It should be noted that the performance indicators of the artificial intelligence model include accuracy, recall rate, F1 score and stability; the division ratio of the data set is assessed by experts. When the training of the artificial intelligence model fails, the division ratio of the data set is adjusted and the artificial intelligence model is retrained.

[0086] See also Figure 3, retrieve real-time data; real-time data includes: real-time process, real-time status, real-time cost and real-time quality; identify abnormal values, missing values ​​and duplicate data of real-time data; remove abnormal values ​​and duplicate data, and supplement missing values; build a data integration library, mark the processed real-time data as project data, and store it in the data integration library;

[0087] Obtain the planned data of the project, where: the planned data includes: the start and end time of the project process, the cost of the project process, and the quality standard of the project process; compare the real-time process in the project data with the planned data, and generate a warning signal when the real-time process lags behind the planned data; otherwise, analyze the real-time status in the project data; determine whether the real-time status in the project data is abnormal or suspended; if yes, generate a warning signal; if no, analyze the real-time cost in the project data; determine whether the real-time cost in the project data is greater than the cost of the project process; if yes, generate a warning signal; if no, analyze the real-time quality in the project data; determine whether the real-time quality in the project data meets the quality standard of the project process; if yes, continue to monitor and manage the project; if no, generate a warning signal.

[0088] Retrieve the early warning signal and abnormal sub-data in the project data; retrieve the corresponding exception handling library based on the abnormal sub-data; match the abnormal sub-data with the exception handling library to obtain the corresponding exception handling measures, and handle the exception according to the exception handling measures.

[0089] For example, if Project A is still in the bidding stage on the 34th day, according to the planned data, Project A should have reached the procurement stage on the 34th day, generating an early warning signal. The bidding stage must be completed on the 40th day, and the procurement stage must be carried out during the bidding period; on the 47th day, the procurement is completed and the project enters the early stage;

[0090] On the 110th day of the project, it was found that the real-time quality of the project did not meet the quality standards of the project process. On the 111th day, the work of the 110th day was redone to ensure that the real-time quality of the project met the quality standards of the project process; there were no abnormalities in the real-time status and real-time cost of the project.

[0091] See also Figure 4, display the project data to relevant project personnel; obtain the adjustment data of the project personnel; wherein the adjustment data includes: changes in project deadlines, changes in project status or adjustments to project costs; when the project deadlines are extended in the adjustment data, the deadlines of several project processes are increased according to the extended deadlines; when the project deadlines are shortened in the adjustment data, the deadlines of several project processes are reduced according to the shortened time; when the project status is changed to suspension or postponement in the adjustment data, the corresponding project is suspended, and the adjustment data of the project is continuously monitored; when the project cost is increased in the adjustment data, better quality materials are selected according to the increased amount; when the cost is reduced in the adjustment data, cost-effective materials are selected according to the reduced amount.

[0092] For example, suppose that the adjustment data of Project A is collected. When the project is 132 days in progress, the information of shortening the project period is received, requiring the project to be completed in 60 days. Then the tasks of the mid-term project are completed in 10 days, the tasks of the late stage project are completed in 30 days, and the liquidation of the project is completed in 20 days.

[0093] If no other adjustment data is received, the project deadline will be adjusted and the real-time data of the project will continue to be monitored.

[0094] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0095] The working principle of the present invention is as follows: the present invention collects the full-process data of the project, divides the project according to the full-process data, and determines the collection frequency; obtains the real-time data of the project based on the collection frequency; pre-processes the real-time data to obtain the project data; performs abnormal warning on the project according to the project data, and generates a warning signal; adjusts the project based on the warning signal; displays the project data to relevant project personnel; obtains the adjustment data of the project personnel; and adjusts the project according to the adjustment data.

[0096] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A project management system based on data integration, characterized in that: include: Project management module, and the collection and division module and terminal coordination module connected to it; Collection and division module: used to collect the full process data of the project, divide the project according to the full process data, and determine the collection frequency; Get real-time data of the project based on the collection frequency; Project management module: used to pre-process real-time data to obtain project data; to issue abnormal warnings to projects based on project data and generate warning signals; to handle abnormal situations of projects based on warning signals; Terminal collaboration module: used to display project data to relevant project personnel; obtain adjustment data from project personnel; and adjust projects based on the adjustment data.

2. A project management system based on data integration according to claim 1, characterized in that: The project is divided according to the full process data, and the collection frequency is determined, including: Retrieve the full process data of the project; the full process data includes: project type, project process and project deadline; sort the project processes in the full process data according to the order of project progress to obtain a project process table; Obtain the historical completion time of the project process and important data of the project process; divide the project deadline into several project processes based on the historical completion time; analyze the key coefficients of the project process based on the important data; determine the collection frequency of the project process based on the key coefficients.

3. A project management system based on data integration according to claim 2, characterized in that: The key factors of the project process based on important data analysis include: Retrieve important data of the project process; important data include: importance, project impact coefficient; project impact coefficient is obtained based on historical data; The importance of important data and the coefficient of impact on the project are marked as ZC and YX respectively; the key coefficients of several project processes are calculated by the formula GX=α×exp[lg(ZC+1)]+β×YX^2; where: α and β are proportional coefficients greater than 0.

4. A project management system based on data integration according to claim 3, characterized in that: The impact coefficient on the project is obtained based on historical data, including: Obtain historical data on the impact of project processes on the project, and integrate the historical data into an impact input sequence in chronological order; Call the impact analysis model, input the impact input sequence into the impact analysis model, and obtain the impact coefficient of the corresponding project process on the project; wherein the impact analysis model is built based on the artificial intelligence model.

5. A project management system based on data integration according to claim 4, characterized in that: The impact analysis model is constructed based on an artificial intelligence model and includes: Acquire a standard data set; wherein the standard data set includes standard input data consistent with the content attributes of the impact input sequence, and standard output data consistent with the content attributes of the impact coefficient on the project; Divide the standard data set into a training set, a validation set, and a test set according to a set ratio; use the training set to train the artificial intelligence model; use the validation set to adjust the internal parameters of the artificial intelligence model; use the test set to test the trained and adjusted artificial intelligence model to obtain performance indicators; Determine whether the performance indicators of the artificial intelligence model are all greater than the indicator threshold. If yes, mark the artificial intelligence model as an impact analysis model; if not, retrain the artificial intelligence model; wherein the artificial intelligence model includes a convolutional neural network model or a feedforward neural network model.

6. A project management system based on data integration according to claim 2, characterized in that: Determining the collection frequency of the project process based on the key coefficients includes: Retrieve key coefficients of several project processes; obtain coefficient thresholds; compare the key coefficients with the coefficient thresholds to obtain key levels corresponding to several project processes; Obtain the level collection library, match the key levels of the project process with the level collection library, and obtain the collection frequency of the corresponding project process.

7. A project management system based on data integration according to claim 1, characterized in that: The preprocessing of the real-time data to obtain the project data includes: Retrieve real-time data, including real-time process, real-time status, real-time cost and real-time quality; Identify outliers, missing values, and duplicate data in real-time data; remove outliers and duplicate data, and supplement missing values; build a data integration library, mark the processed real-time data as project data, and store it in the data integration library.

8. A project management system based on data integration according to claim 1, characterized in that: The abnormal warning of the project according to the project data and the generation of the warning signal include: Retrieve project data; obtain project planning data, where planning data includes: the start and end time of the project process, the cost of the project process, and the quality standard of the project process; Compare the real-time progress in the project data with the planned data. When the real-time progress lags behind the planned data, an early warning signal is generated; otherwise, the real-time status in the project data is analyzed; Determine whether the real-time status in the project data is abnormal or suspended; if yes, generate an early warning signal; if no, analyze the real-time cost in the project data; Determine whether the real-time cost in the project data is greater than the cost of the project process; if yes, generate an early warning signal; if no, analyze the real-time quality in the project data; Determine whether the real-time quality of the project data meets the quality standards of the project process; if yes, continue to monitor and manage the project; if not, generate an early warning signal.

9. A project management system based on data integration according to claim 1 or 8, characterized in that: The processing of abnormal situations of the project based on the early warning signal includes: Retrieve the early warning signal and abnormal sub-data in the project data; retrieve the corresponding exception processing library according to the abnormal sub-data; Match the abnormal sub-data with the exception handling library, obtain the corresponding exception handling measures, and handle the exception according to the exception handling measures.

10. A project management system based on data integration according to claim 1, characterized in that: The adjusting of the project according to the adjustment data includes: Retrieve adjustment data; the adjustment data includes: changes in project deadlines, changes in project status, or adjustments to project costs; When the project deadline is extended in the adjustment data, the deadlines of several project processes are increased according to the extended deadline; when the project deadline is shortened in the adjustment data, the deadlines of several project processes are reduced according to the shortened time; When the project status is changed to suspended or postponed in the adjustment data, the corresponding project is suspended and the adjustment data of the project is continuously monitored; When the project cost is increased in the adjustment data, better quality materials are selected according to the increased amount; when the cost is reduced in the adjustment data, more cost-effective materials are selected according to the reduced amount.

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