CN114782030A - Intelligent management system and method based on big data project - Google Patents
Intelligent management system and method based on big data project Download PDFInfo
- Publication number
- CN114782030A CN114782030A CN202210721795.XA CN202210721795A CN114782030A CN 114782030 A CN114782030 A CN 114782030A CN 202210721795 A CN202210721795 A CN 202210721795A CN 114782030 A CN114782030 A CN 114782030A
- Authority
- CN
- China
- Prior art keywords
- data
- project
- idle
- stage
- temporary
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Data Mining & Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a big data project-based intelligent management system and a method, wherein the system comprises the following steps: the project management system comprises a project data acquisition module, a database, a data analysis module, a data transmission management module and a temporary project management module, wherein the project data acquisition module acquires information of the number of stages divided by a project and information of the type of data processed in each stage, the database stores all acquired data, the data analysis module analyzes the idle time of historical data to generate a historical data idle model, the data idle time is predicted through the data transmission management module, managers are reminded to transmit idle data to managers in subsequent stages when the data are idle, the project completion progress is monitored in real time through the temporary project management module, when a temporary project is added in the current project execution process, a proper number of persons are allocated to execute the temporary project, the problem of disordered multi-project management work is solved, and multi-stage project work is synchronously performed, the project propulsion efficiency is improved.
Description
Technical Field
The invention relates to the technical field of project management, in particular to a big data project-based intelligent management system and method.
Background
Project management refers to the application of special knowledge, skills, tools and methods in project activities, so that projects can realize or exceed set requirements and expected processes under limited resource limiting conditions, the project management comprises project range management, project time management, project quality management and the like, under increasingly tense competitive environments, the project management plays a role in enterprise management, and problems in the project advancing process can be effectively reduced;
however, the existing project management method has some problems: firstly, a project work is generally divided into a plurality of stages to be carried out, processed data is generally transmitted to the next stage after the project work of the previous stage is completely finished, and the project work of the next stage is started, however, idle data which is not processed any more may exist in the project work of the previous stage, the corresponding idle data may be data required by the subsequent stage, the idle time of the data cannot be predicted in the existing project management mode to transmit the idle data to the subsequent stage in advance for the next processing, and the function of improving the project propelling efficiency by carrying out the multi-stage project work synchronously cannot be realized; secondly, a situation that a temporary project needs to be processed emergently in the project process often occurs, and for the situation, under the condition that personnel are insufficient, the existing project management mode cannot timely carry out proper personnel allocation and scheduling work so as to ensure that the project progress is controlled within a certain range, and the problem that the project management work is disordered easily occurs.
Therefore, a system and a method for intelligent management based on big data items are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an intelligent management system and method based on big data items to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a big data item based intelligent management system, the system comprising: the system comprises a project data acquisition module, a database, a data analysis module, a data transmission management module and a temporary project management module;
the output end of the project data acquisition module is connected with the input end of the database, the output end of the database is connected with the input ends of the data analysis module and the temporary project management module, and the output end of the data analysis module is connected with the input end of the data transmission management module;
acquiring stage number information of the project and data type information processed in each stage through the project data acquisition module;
storing all collected data through the database;
analyzing the idle time of the historical data before the historical data becomes idle data through the data analysis module to generate a historical data idle model;
predicting data idle time through the data transmission management module, and reminding a manager to transmit idle data to a manager in a subsequent stage when the data are idle;
and monitoring the project completion progress in real time through the temporary project management module, and distributing a proper number of personnel to participate in the temporary project work when a temporary project is added in the current project execution process.
Further, the project data acquisition module comprises a stage information acquisition unit and a data type acquisition unit, wherein the output ends of the stage information acquisition unit and the data type acquisition unit are connected with the input end of the database;
acquiring the number of project stages divided by an enterprise for completing a project and the time for processing data of each stage through the stage information acquisition unit; and acquiring the data types processed when each stage task is executed through the data type acquisition unit, and transmitting all acquired data to the database.
Furthermore, the data analysis module comprises a data type analysis unit and an idle data analysis unit, wherein the input ends of the data type analysis unit and the idle data analysis unit are connected with the output end of the database;
calling a data type processed by the current project through the data type analysis unit, and matching the data type with a historical idle data type; and calling historical project data processing time through the idle data analysis unit, analyzing idle time of historical data before the historical data becomes idle data, generating a historical data idle model, and transmitting the historical data idle model to the data transmission management module.
Furthermore, the data transmission management module comprises a processing time prediction unit and a data transmission reminding unit, wherein the output end of the processing time prediction unit is connected with the input end of the data transmission reminding unit;
predicting the idle time of the processed data according to a historical data idle model by the processing time prediction unit, and transmitting a prediction result to the data transmission reminding unit; and reminding a manager to transmit the data to a manager in a subsequent stage by the data transmission reminding unit when the data idle time of the current project exceeds the predicted idle time.
Further, the temporary project management module comprises a project progress monitoring unit, a project data acquisition unit and a processing personnel distribution unit, wherein the output end of the project progress monitoring unit is connected with the input end of the project data acquisition unit, the input end of the project data acquisition unit is connected with the output end of the database, and the output end of the project data acquisition unit is connected with the input end of the processing personnel distribution unit;
the project completion progress is monitored in real time through the project progress monitoring unit, and when a temporary project needs to be processed in the current project execution process, the project data acquisition unit acquires completion progress data of the current project and allocated personnel number data; and distributing and scheduling personnel to process the temporary project from the current project through the processing personnel distribution unit according to the completion progress of the current project and the distributed personnel data.
An intelligent management method based on big data items comprises the following steps:
s1: acquiring stage information of project division and data type information processed in each stage;
s2: analyzing the idle time of the historical data before the historical data becomes idle data to generate a historical data idle model;
s3: substituting the current project data into a historical data idle model, and predicting the idle time of the current project data;
s4: reminding a manager to transmit idle data to a manager in a subsequent stage when the data are idle;
s5: and monitoring project completion progress in real time, and if a temporary project is added, allocating personnel to execute the temporary project.
Further, in step S1: the collected current project is divided into m stages, the data processed in the first stage has n types, and in step S2: in the items with completed history, the set of idle durations before the random type of data processed in the first stage becomes idle data is counted as T = { T1, T2, …, tk }, and the average idle duration of the corresponding type of data is obtained as T1:
wherein k represents the statistical frequency, the similarity set of the other types of data processed in the first stage and the corresponding types of data is s = { s2, s3, …, sf }, wherein the f types of data are processed in the first stage, and the average idle time duration set before the other types of data become idle data is counted as T = { T2, T3, …, Tf }, so as to generate a data idle model: the data points { (s 1, T1), (s 2, T2), …, (sf, Tf) } were fitted with a straight line: the straight line fitting function is obtained as: y = Ax + B, where a and B represent fitting coefficients, where s1=1, which are calculated according to the following formula, respectively:
wherein si represents the similarity between other random data processed in the first stage and data corresponding to T1, Ti represents the average idle time before the other random data become idle data, the idle time indicates the maintaining time when the data are not processed, the non-processing data in the corresponding stage represents the completion of processing the corresponding data, the processing modes of the similar data have the same identity, the more similar the processing modes of the data have the higher identity, after a large number of similar project works, the differences of the processing modes and the processing time of the similar data of personnel are gradually reduced, and the analysis historical data are called according to the big data: the idle time of random data is analyzed, the idle time of other types is analyzed, data points are formed by combining the similarity degree and the idle time among the data, a fitting function, namely a historical data idle model, is obtained by fitting the data points, the historical data idle model is generated, the purpose of the historical data idle model is to help to prejudge the idle time of the current project data, the generated model can well conclude the idle rule of the historical data, and the accuracy of a prejudgment result is improved.
Further, in step S3: the similarity set of the data obtained by the first stage processing of the current item and the type data corresponding to the T1 is s = { s1=’，s2’，…，sn’Predicting the idle time ti of random data processed in the first stage of the current item according to the following formula’：
Wherein si is’Representing the similarity between the random data processed at the first stage of the current project and the corresponding type data of T1, and obtaining the idle time set T of all types of data processed at the first stage of the current project’={t1’，t2’，…，tn’Predicting that the idle duration of the corresponding type data exceeds the predicted idle duration to become idle data and completing the processing, in step S4: when the idle time of the corresponding type of data exceeds the predicted idle time, reminding a manager to transmit the idle data to the manager needing the corresponding idle data in the subsequent stage, wherein the idle time of the data is related to the similarity degree between the data, matching the current project data with the historical data to obtain the similarity between the current project data and the historical random data, substituting the similarity degree into the historical data idle model, and being capable of predicting when the corresponding type of data becomes the idle data, thereby being beneficial to timely extracting the idle dataAnd the manager checks the corresponding data, and when the manager checks that the corresponding data are not processed any more, namely the data become idle data, the corresponding data are transmitted to a subsequent stage needing the corresponding data in advance, and the personnel in the subsequent stage further process the corresponding data, so that the work handover can be completed in advance to complete the project work synchronously, and the project propulsion efficiency is improved.
Further, in step S5: monitoring project completion progress in real time, if a temporary project needs to be processed in the current project processing process, collecting a set of personnel numbers distributed to each stage of the current project, wherein the set of personnel numbers distributed to each stage of the current project is R = { R1, R2, … and Rm }, the set of project completion progress of each stage is D = { D1, D2, … and Dm }, the set of project completion difficulty coefficients of each stage is N = { N1, N2, … and Nm }, wherein m represents the number of stages into which the current project is divided, the number of stages into which the temporary project is divided is the same as that of the current project, and the number of personnel Mi distributed to one random stage of the temporary project is calculated according to the following formula:
wherein Di represents the completion progress of one random stage of the current project, Ni represents the project completion difficulty coefficient of the corresponding stage,
the minimum value of the person assignment weight coefficients representing all the stages,
the maximum value of the personnel distribution weight coefficients of all the stages is represented, the number set of the personnel distributed to each stage of the temporary project is M = { M1, M2, …, Mm }, a corresponding number of personnel are distributed in the current project to participate in the work of the corresponding stage of the temporary project, when the temporary project needs to be processed and the personnel are insufficient, part of the personnel in the current project need to be scheduled to participate in the work of the temporary project, but due to different work schedules and work difficulties of the projects of all the stages, the number of the personnel in all the stages needs to be scheduled, and the personnel in all the stages need to be scheduled through the distribution of the personnel distribution weight coefficients of all the stages,The normalization processing of the work difficulty data is beneficial to planning the data in a certain range, and the data is used as a weight ratio distributed by personnel to further confirm the number of dispatched personnel, so that the influence on the work of the current project is reduced while the propulsion efficiency of the temporary project is improved.
Compared with the prior art, the invention has the following beneficial effects:
the invention analyzes historical data by big data calling: analyzing the idle time of random data, analyzing the idle time of other data, combining the similarity between data and idle time to form data points, the data points are fitted and a historical data idle model is generated to help to predict the idle time of the current project data, the generated model can well summarize the idle rule of the historical data, the accuracy of the predicted result is improved, the predicted result is substituted into the current project data to the model, predicting when the corresponding data becomes idle data, reminding a manager to check the corresponding data in time, and if the corresponding data is checked, not processing the corresponding data, when the data become idle data, the corresponding data are transmitted to a subsequent stage needing the corresponding data in advance, and the corresponding data are further processed by personnel in the subsequent stage, so that the work handover can be completed in advance to complete project work synchronously, and the project propulsion efficiency is improved; when a temporary project needs to be processed and the staff is insufficient, a proper number of staff in the current project are scheduled to participate in the temporary project work, the pushing efficiency of the temporary project is improved, meanwhile, the influence on the current project work is reduced, and the problem of multi-project and multi-stage management work chaos in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a big data project based intelligent management system of the present invention;
fig. 2 is a flowchart of an intelligent management method based on big data items according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: a big data project based intelligent management system comprises: the system comprises a project data acquisition module, a database, a data analysis module, a data transmission management module and a temporary project management module;
the output end of the project data acquisition module is connected with the input end of the database, the output end of the database is connected with the input ends of the data analysis module and the temporary project management module, and the output end of the data analysis module is connected with the input end of the data transmission management module;
acquiring stage quantity information of the project and data type information processed in each stage through a project data acquisition module;
storing all the collected data through a database;
analyzing the idle time of the historical data before the historical data becomes idle data through a data analysis module to generate a historical data idle model;
predicting data idle time through a data transmission management module, and reminding a manager to transmit idle data to a manager in a subsequent stage when the data are idle;
the project completion progress is monitored in real time through the temporary project management module, and when a temporary project is added in the current project execution process, a proper amount of personnel are allocated to participate in the temporary project work.
The project data acquisition module comprises a stage information acquisition unit and a data type acquisition unit, wherein the output ends of the stage information acquisition unit and the data type acquisition unit are connected with the input end of the database;
the method comprises the steps that a stage information acquisition unit acquires the number of project stages divided by an enterprise for completing a project and the time for processing data of each stage; and acquiring the data types processed when each stage task is executed through a data type acquisition unit, and transmitting all acquired data to a database.
The data analysis module comprises a data type analysis unit and an idle data analysis unit, and the input ends of the data type analysis unit and the idle data analysis unit are connected with the output end of the database;
calling a data type processed by a current project through a data type analysis unit, and matching the data type with a historical idle data type; and calling the historical item data processing time through the idle data analysis unit, analyzing the idle time of the historical data before the historical data becomes idle data, generating a historical data idle model, and transmitting the historical data idle model to the data transmission management module.
The data transmission management module comprises a processing time prediction unit and a data transmission reminding unit, wherein the output end of the processing time prediction unit is connected with the input end of the data transmission reminding unit;
predicting the idle time of the processed data according to the historical data idle model by using a processing time prediction unit, and transmitting a prediction result to a data transmission reminding unit; and when the data idle time of the current project exceeds the predicted idle time, the data transmission reminding unit reminds the manager to transmit the data to the manager in the subsequent stage.
The temporary project management module comprises a project progress monitoring unit, a project data acquisition unit and a processing personnel distribution unit, wherein the output end of the project progress monitoring unit is connected with the input end of the project data acquisition unit, the input end of the project data acquisition unit is connected with the output end of the database, and the output end of the project data acquisition unit is connected with the input end of the processing personnel distribution unit;
the project completion progress is monitored in real time through a project progress monitoring unit, and when a temporary project needs to be processed in the current project execution process, the completion progress data of the current project and the distributed personnel number data are obtained through a project data obtaining unit; and distributing and scheduling the personnel to process the temporary project from the current project through the processing personnel distribution unit according to the completion progress of the current project and the distributed personnel data.
An intelligent management method based on big data items comprises the following steps:
s1: acquiring stage information of project division and data type information processed in each stage;
s2: analyzing the idle time of the historical data before the historical data becomes idle data to generate a historical data idle model;
s3: substituting the current project data into a historical data idle model, and predicting the idle time of the current project data;
s4: reminding a manager to transmit idle data to a manager in a subsequent stage when the data are idle;
s5: and monitoring project completion progress in real time, and if a temporary project is added, allocating personnel to execute the temporary project.
In step S1: the collected current project is divided into m stages, the data processed in the first stage has n types, and in step S2: in the items with completed history, the set of idle durations before the random type of data processed in the first stage becomes idle data is counted as T = { T1, T2, …, tk }, and the average idle duration of the corresponding type of data is obtained as T1:
wherein k represents the statistical frequency, the similarity set of the other types of data processed in the first stage and the corresponding types of data is s = { s2, s3, …, sf }, wherein the f types of data are processed in the first stage, and the average idle time duration set before the other types of data become idle data is counted as T = { T2, T3, …, Tf }, so as to generate a data idle model: the data points { (s 1, T1), (s 2, T2), …, (sf, Tf) } were fitted with a straight line: the straight line fitting function is obtained as: y = Ax + B, where a and B represent fitting coefficients, where s1=1, which are calculated according to the following formula, respectively:
wherein si represents the similarity between other random data processed in the first stage and the corresponding type of data of T1, Ti represents the average idle time before other random data become idle data, and a historical data idle model is generated to help predict the idle time of the current item data, so that the idle rule of the data is well generalized, and the accuracy of the pre-determined result is improved.
In step S3: the similarity set of the data obtained by the first stage processing of the current item and the type data corresponding to the T1 is s = { s1’，s2’，…，sn’Predicting the idle time ti of random data processed in the first stage of the current item according to the following formula’：
Wherein si is’Representing the similarity between the random type data processed in the first stage of the current project and the type data corresponding to the T1, and obtaining the idle time set of all types of data processed in the first stage of the current project as T’={t1’，t2’，…，tn’Predicting that the idle duration of the corresponding type data exceeds the predicted idle duration to become idle data and completing the processing, in step S4: when the idle time length of the corresponding type data exceeds the predicted idle time length, the manager is reminded to transmit the idle data to the manager needing the corresponding idle data in the subsequent stage, the current project data is substituted into the model to obtain when the data becomes the idle data, the manager is reminded to transmit the corresponding data to the stage needing the corresponding data in advance after verification, the staff in the subsequent stage further processes the corresponding data, the work handover is conveniently completed in advance to synchronously complete the project work, and the project propulsion efficiency is improved.
In step S5: monitoring project completion progress in real time, if a temporary project needs to be processed in the current project processing process, collecting a set of personnel numbers distributed to each stage of the current project, wherein the set of personnel numbers distributed to each stage of the current project is R = { R1, R2, … and Rm }, the set of project completion progress of each stage is D = { D1, D2, … and Dm }, the set of project completion difficulty coefficients of each stage is N = { N1, N2, … and Nm }, wherein m represents the number of stages into which the current project is divided, the number of stages into which the temporary project is divided is the same as that of the current project, and the number of personnel Mi distributed to one random stage of the temporary project is calculated according to the following formula:
wherein Di represents the completion progress of a random stage of the current project, Ni represents the project completion difficulty coefficient of the corresponding stage,
the minimum value of the person assignment weight coefficients representing all the stages,
the maximum value of the staff distribution weight coefficients of all the stages is represented, the number set of staff distributed at each stage of the temporary project is M = { M1, M2, …, Mm }, a corresponding number of staff are distributed in the current project to participate in the work of the corresponding stage of the temporary project, the staff with the proper number are dispatched to participate in the work of the temporary project, and the influence on the work of the current project is reduced while the propulsion efficiency of the temporary project is improved.
The first embodiment is as follows: the collected current project is divided into m =3 stages, the data processed in the first stage has n =3 types, and in the project with the completed history, the idle time length set before the random type data processed in the first stage becomes idle data is counted as t = { t1, t2, t3} = {2, 5, 8}, the unit is: and d, obtaining the average idle time length of the corresponding type of data as T1:
the similarity set between the other types of data processed in the first stage and the corresponding types of data is s = { s2, s3, s4} = {0.8, 0.5, 0.3}, and it is counted that the average idle time length before the other types of data become idle data is T = { T2, T3, T4} = {6, 3, 1}, so as to generate a data idle model: data points { (s 1, T1), (s 2, T2), (s 3, T3)) (s 4, T4) } { (1, 5), (0.8, 6), (0.5, 3), (0.3, 1) } straight line fitting: the straight line fitting function is obtained as: y = Ax + B, according to the formula
And
calculating fitting coefficients A and B respectively: a ≈ 6.4, B = -1.4, and the similarity set of the data acquired in the first stage of the current item and the type data corresponding to T1 is s = { s1=’，s2’，s3’} = {0.6, 0.9, 0.7}, according to the formula
Predicting the idle time set t of all types of data processed in the first stage of the current project’={t1’，t2’，t3’The idle time of the first type of data is predicted to exceed 2 days, the idle time of the second type of data exceeds 4 days, the idle time of the third type of data exceeds 3 days, then the data becomes idle data and is processed completely, and when the idle time of the corresponding type of data exceeds the predicted idle time, the management personnel is reminded to transmit the idle data to the management personnel needing the corresponding idle data in the subsequent stage;
example two: monitoring project completion progress in real time, if a temporary project needs to be processed in the current project processing process, collecting a set of personnel numbers distributed by each stage of the current project, wherein the set of personnel numbers distributed by each stage is R = { R1, R2, R3} = {4, 5, 3}, a set of project completion progress of each stage is D = { D1, D2, D3} = {80%, 60%, 10% }, and a set of project completion difficulty coefficients of each stage is N = { N1, N2, N3} = {2, 5, 6}, according to a formula, and processing the project according to the formula
And obtaining a set of the number of persons allocated to each stage of the temporary project, namely M = { M1, M2, M3} = {4, 1, 0}, and allocating a corresponding number of persons to participate in the work of the corresponding stage of the temporary project in the current project.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The utility model provides a based on big data project intelligent management system which characterized in that: the system comprises: the system comprises a project data acquisition module, a database, a data analysis module, a data transmission management module and a temporary project management module; the output end of the project data acquisition module is connected with the input end of the database, the output end of the database is connected with the input ends of the data analysis module and the temporary project management module, and the output end of the data analysis module is connected with the input end of the data transmission management module; acquiring stage quantity information of the project and data type information processed in each stage through the project data acquisition module; storing all collected data through the database; analyzing the idle time of the historical data before the historical data becomes idle data through the data analysis module to generate a historical data idle model; predicting data idle time through the data transmission management module, and reminding a manager to transmit idle data to a manager in a subsequent stage when the data are idle; and monitoring the project completion progress in real time through the temporary project management module, and distributing a proper number of personnel to participate in the temporary project work when a temporary project is added in the current project execution process.
2. The intelligent management system based on big data items according to claim 1, characterized in that: the project data acquisition module comprises a stage information acquisition unit and a data type acquisition unit, and the output ends of the stage information acquisition unit and the data type acquisition unit are connected with the input end of the database; acquiring the number of project stages divided by an enterprise for completing a project and the time for processing data of each stage through the stage information acquisition unit; and acquiring the data types processed when each stage task is executed through the data type acquisition unit, and transmitting all acquired data to the database.
3. The big data item based intelligent management system of claim 1, wherein: the data analysis module comprises a data type analysis unit and an idle data analysis unit, wherein the input ends of the data type analysis unit and the idle data analysis unit are connected with the output end of the database; calling the data type processed by the current project through the data type analysis unit, and matching the data type with the historical idle data type; and calling historical project data processing time through the idle data analysis unit, analyzing idle duration of historical data before the historical data become idle data, generating a historical data idle model, and transmitting the historical data idle model to the data transmission management module.
4. The big data item based intelligent management system of claim 1, wherein: the data transmission management module comprises a processing time prediction unit and a data transmission reminding unit, wherein the output end of the processing time prediction unit is connected with the input end of the data transmission reminding unit; predicting the idle time of the processed data according to a historical data idle model by the processing time prediction unit, and transmitting a prediction result to the data transmission reminding unit; and reminding a manager to transmit the data to a manager in a subsequent stage by the data transmission reminding unit when the data idle time of the current project exceeds the predicted idle time.
5. The intelligent management system based on big data items according to claim 1, characterized in that: the temporary project management module comprises a project progress monitoring unit, a project data acquisition unit and a processing personnel distribution unit, wherein the output end of the project progress monitoring unit is connected with the input end of the project data acquisition unit, the input end of the project data acquisition unit is connected with the output end of the database, and the output end of the project data acquisition unit is connected with the input end of the processing personnel distribution unit; the project completion progress is monitored in real time through the project progress monitoring unit, and when a temporary project needs to be processed in the current project execution process, the project data acquisition unit acquires completion progress data of the current project and allocated personnel number data; and distributing and scheduling personnel to process the temporary project from the current project through the processing personnel distribution unit according to the completion progress of the current project and the distributed personnel data.
6. An intelligent management method based on big data items is characterized in that: the method comprises the following steps:
s1: acquiring stage information of project division and data type information processed in each stage;
s2: analyzing the idle time of the historical data before the historical data becomes idle data to generate a historical data idle model;
s3: substituting the current project data into a historical data idle model, and predicting the idle time of the current project data;
s4: reminding a manager to transmit idle data to a manager in a subsequent stage when the data are idle;
s5: and monitoring project completion progress in real time, and if a temporary project is added, allocating personnel to execute the temporary project.
7. The big data item based intelligent management method according to claim 6, wherein: in step S1: when the current project is collected and divided into m stages, the data processed in the first stage has n types, and in step S2: in the history completed project, the set of idle time durations before the random type of data processed in the first stage becomes idle data is counted as T = { T1, T2, …, tk }, and the average idle time duration of the corresponding type of data is obtained as T1:
wherein k represents the statistical frequency, the similarity set of the other types of data processed in the first stage and the corresponding types of data is s = { s2, s3, …, sf }, wherein the f types of data are processed in the first stage, and the average idle time duration set before the other types of data become idle data is counted as T = { T2, T3, …, Tf }, so as to generate a data idle model: the data points { (s 1, T1), (s 2, T2), …, (sf, Tf) } were fitted with a straight line: the straight line fitting function is obtained as: y = Ax + B, where a and B represent fitting coefficients, where s1=1, which are calculated according to the following formula, respectively:
wherein si represents the similarity between the random data processed in the first stage and the type data corresponding to T1, and Ti represents the average idle time before the random data becomes idle data.
8. The big data item based intelligent management method according to claim 7, wherein: in step S3: the similarity set of the data obtained by the first stage processing of the current item and the type data corresponding to the T1 is s = { s1=’，s2’，…，sn’Predicting the idle time ti of random data processed in the first stage of the current item according to the following formula’：
Wherein si is’Representing the similarity of the random type data processed in the first stage of the current project and the type data corresponding to the T1 to obtain the similarityThe idle time length set of all types of data processed in the first stage of the current project is t’={t1’，t2’，…，tn’Predicting that the idle duration of the corresponding type data exceeds the predicted idle duration to become idle data and completing the processing, in step S4: and when the idle time length of the corresponding type of data exceeds the predicted idle time length, reminding a manager of transmitting the idle data to the manager needing the corresponding idle data in the subsequent stage.
9. The intelligent management method based on big data items according to claim 6, wherein: in step S5: monitoring project completion progress in real time, if a temporary project needs to be processed in the current project processing process, collecting a set of personnel numbers distributed to each stage of the current project, wherein the set of personnel numbers distributed to each stage of the current project is R = { R1, R2, … and Rm }, the set of project completion progress of each stage is D = { D1, D2, … and Dm }, the set of project completion difficulty coefficients of each stage is N = { N1, N2, … and Nm }, wherein m represents the number of stages into which the current project is divided, the number of stages into which the temporary project is divided is the same as that of the current project, and the number of personnel Mi distributed to one random stage of the temporary project is calculated according to the following formula:
wherein Di represents the completion progress of one random stage of the current project, Ni represents the project completion difficulty coefficient of the corresponding stage,
the person who represents all the stages assigns the minimum value of the weight coefficients,
the maximum value of the weight coefficients of the personnel distribution of all the stages is represented, the number of the personnel distributed in each stage of the temporary project is M = { M1, M2, …, Mm }, and the personnel parameters of the corresponding number are distributed in the current projectAnd working in a stage corresponding to the temporary project.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210721795.XA CN114782030B (en) | 2022-06-24 | 2022-06-24 | Intelligent management system and method based on big data project |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210721795.XA CN114782030B (en) | 2022-06-24 | 2022-06-24 | Intelligent management system and method based on big data project |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114782030A true CN114782030A (en) | 2022-07-22 |
| CN114782030B CN114782030B (en) | 2022-08-26 |
Family
ID=82422232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210721795.XA Active CN114782030B (en) | 2022-06-24 | 2022-06-24 | Intelligent management system and method based on big data project |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114782030B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730815A (en) * | 2022-10-10 | 2023-03-03 | 江苏安胜达安全科技有限公司 | Industrial production safety risk early warning system and method based on Internet of things perception |
| CN115796563A (en) * | 2023-02-10 | 2023-03-14 | 中建安装集团有限公司 | Steel structure list checking and verifying management system based on big data |
| CN115858308A (en) * | 2022-12-30 | 2023-03-28 | 珠海市众知科技有限公司 | Automatic processing information intelligent monitoring system and method based on big data |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5521828A (en) * | 1993-01-25 | 1996-05-28 | Mitsubishi Denki Kabushiki Kaisha | Unit and method for setup-change instruction and the production system |
| US20120253864A1 (en) * | 2011-03-31 | 2012-10-04 | Infosys Technologies, Ltd. | Methods for recording and tracking projects and devices thereof |
| CN109388486A (en) * | 2018-10-09 | 2019-02-26 | 北京航空航天大学 | A kind of data placement and moving method for isomery memory with polymorphic type application mixed deployment scene |
| CN113408897A (en) * | 2021-06-19 | 2021-09-17 | 杨福心 | Data resource sharing method applied to big data service and big data server |
| CN113656402A (en) * | 2021-07-13 | 2021-11-16 | 广东省国际工程咨询有限公司 | Engineering project full life cycle data recording and storing method and device based on BIM |
| CN113887933A (en) * | 2021-09-29 | 2022-01-04 | 平安国际智慧城市科技股份有限公司 | Data processing method, device and equipment based on artificial intelligence and storage medium |
| CN114049086A (en) * | 2021-11-15 | 2022-02-15 | 安徽理工大学 | Engineering project management system based on big data |
| CN114626096A (en) * | 2022-03-21 | 2022-06-14 | 长鑫存储技术有限公司 | Wafer processing production data management method and device and electronic equipment |
-
2022
- 2022-06-24 CN CN202210721795.XA patent/CN114782030B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5521828A (en) * | 1993-01-25 | 1996-05-28 | Mitsubishi Denki Kabushiki Kaisha | Unit and method for setup-change instruction and the production system |
| US20120253864A1 (en) * | 2011-03-31 | 2012-10-04 | Infosys Technologies, Ltd. | Methods for recording and tracking projects and devices thereof |
| CN109388486A (en) * | 2018-10-09 | 2019-02-26 | 北京航空航天大学 | A kind of data placement and moving method for isomery memory with polymorphic type application mixed deployment scene |
| CN113408897A (en) * | 2021-06-19 | 2021-09-17 | 杨福心 | Data resource sharing method applied to big data service and big data server |
| CN113656402A (en) * | 2021-07-13 | 2021-11-16 | 广东省国际工程咨询有限公司 | Engineering project full life cycle data recording and storing method and device based on BIM |
| CN113887933A (en) * | 2021-09-29 | 2022-01-04 | 平安国际智慧城市科技股份有限公司 | Data processing method, device and equipment based on artificial intelligence and storage medium |
| CN114049086A (en) * | 2021-11-15 | 2022-02-15 | 安徽理工大学 | Engineering project management system based on big data |
| CN114626096A (en) * | 2022-03-21 | 2022-06-14 | 长鑫存储技术有限公司 | Wafer processing production data management method and device and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 邹森忠: "空间数据共享平台的数据交换与检索及应用模型研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
| 阎思宇: "项目施工阶段大数据信息化管理平台建设模式研究", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730815A (en) * | 2022-10-10 | 2023-03-03 | 江苏安胜达安全科技有限公司 | Industrial production safety risk early warning system and method based on Internet of things perception |
| CN115858308A (en) * | 2022-12-30 | 2023-03-28 | 珠海市众知科技有限公司 | Automatic processing information intelligent monitoring system and method based on big data |
| CN115796563A (en) * | 2023-02-10 | 2023-03-14 | 中建安装集团有限公司 | Steel structure list checking and verifying management system based on big data |
| CN115796563B (en) * | 2023-02-10 | 2023-04-11 | 中建安装集团有限公司 | Steel structure list checking and verifying management system based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114782030B (en) | 2022-08-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114782030B (en) | Intelligent management system and method based on big data project | |
| Gul et al. | A progressive hedging approach for surgery planning under uncertainty | |
| Jørgensen et al. | Software development effort estimation: Formal models or expert judgment? | |
| Keskinocak et al. | Scheduling and reliable lead-time quotation for orders with availability intervals and lead-time sensitive revenues | |
| Zhang et al. | Flexible assembly job-shop scheduling with sequence-dependent setup times and part sharing in a dynamic environment: Constraint programming model, mixed-integer programming model, and dispatching rules | |
| US7212986B1 (en) | Method and apparatus for planning and monitoring multiple tasks based on user defined criteria and predictive ability | |
| Nelson et al. | Centralized scheduling and priority implementation heuristics for a dynamic job shop model | |
| CN111311090B (en) | Intelligent scheduling method and device based on big data calculation and analysis | |
| Ištoković et al. | Simulation framework for determining the order and size of the product batches in the flow shop: A case study | |
| CN117311299B (en) | Factory management system and method based on multi-source heterogeneous data integration | |
| Nie et al. | A cuckoo search algorithm for scheduling multiskilled workforce | |
| CN113344392A (en) | Enterprise project comprehensive management method and system | |
| Myszewski et al. | A model for measuring effectiveness of quality management practices in health care | |
| Selladurai et al. | Dynamic simulation of job shop scheduling for optimal performance | |
| Aksyonov et al. | Application of the hybrid agents technology for control of the construction company | |
| US20090089129A1 (en) | Automated time budgeting | |
| CN115689201A (en) | Multi-criterion intelligent decision optimization method and system for enterprise resource supply and demand allocation | |
| Dragut et al. | A representation model for the solving-time distribution of a set of design tasks in new product development (NPD) | |
| JP7348827B2 (en) | Information processing device and information processing method | |
| Hashemzadeh et al. | Identification and weighting factors influencing the establishment of a single minute exchange of dies in plastic injection industry using VIKOR and Shannon Entropy | |
| Ganji et al. | A new model for optimising simultaneously projects selection and resource-constrained project scheduling problem | |
| CA2832806A1 (en) | Method for optimizing schedule compression in projects using accelerating and overlapping of activities | |
| Mård | Dynamic planning of MTO production | |
| Maprilana¹ | ANALYSIS SCHEDULING PROJECT USING CPM METHOD (Case study: Project of the Surabaya Caspian Tower Apartment) | |
| US20200327501A1 (en) | Transaction Management System and Managing Method Thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |