CN114331349A - Scientific research project management method and system based on Internet of things technology - Google Patents
Scientific research project management method and system based on Internet of things technology Download PDFInfo
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Abstract
The invention discloses a scientific research project management method and system based on the technology of the Internet of things, wherein the method comprises the following steps: obtaining first target scientific research project information; acquiring data of the first target scientific research project information to obtain a first project data set; constructing a two-dimensional rectangular coordinate model; inputting the first project data set into a two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions to generate first quadrant distribution information; generating a first monitoring configuration parameter; obtaining first real-time monitoring data; transmitting the first real-time monitoring data to a cloud server for project quality prediction to obtain a first prediction quality coefficient; and obtaining a first optimization monitoring item to carry out optimization management on the target scientific research project. The technical problems that in the prior art, complete-cycle and multi-directional comprehensive management cannot be carried out on scientific research projects, so that project progress cannot be mastered in real time, and unknown risks of the projects are improved are solved.
Description
Technical Field
The invention relates to the field of project management, in particular to a scientific research project management method and system based on the technology of the Internet of things.
Background
Scientific research project management refers to the whole-course management of projects from project application, project establishment and demonstration, organization and implementation, examination and evaluation, acceptance and verification, result declaration, scientific and technological popularization and file volume entering. The aim is to implement institutionalized and scientific management of scientific research projects, ensure the scientific research plans to be completed satisfactorily, produce achievements, talents and benefits and improve competitiveness.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the technical problems that complete-cycle and multi-azimuth comprehensive management cannot be carried out on scientific research projects, project progress cannot be mastered in real time, and unknown risks of the projects are improved exist in the prior art.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the application aims to solve the technical problems that the complete-cycle and multi-azimuth comprehensive management of scientific research projects cannot be carried out, the project progress cannot be mastered in real time, and the unknown risk of the projects cannot be improved in the prior art by providing the scientific research project management method and system based on the internet of things technology. Based on different characteristic dimensions of the scientific research projects, targeted dimension management is carried out on the scientific research projects, then project target quality coefficients of the cloud server are compared, the scientific research projects are continuously optimized, comprehensive management of the scientific research projects in a full period and in multiple directions is achieved, the project progress is mastered in real time, unknown risks of the projects are reduced, and the scientific research projects are intelligently, plurally and characteristically managed.
On one hand, the embodiment of the application provides a scientific research project management method based on the internet of things technology, wherein the method is applied to a scientific research project management system based on the internet of things technology, the system is in communication connection with a cloud server, and the method comprises the following steps: obtaining first target scientific research project information; acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data; constructing a two-dimensional rectangular coordinate model; inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information; generating a first monitoring configuration parameter according to the first quadrant distribution information; acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters; transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient; obtaining a first optimized monitoring item according to the first prediction quality coefficient; and carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
On the other hand, this application still provides a scientific research project management system based on internet of things, wherein, the system includes: a first obtaining unit: the first obtaining unit is used for obtaining first target scientific research project information; a first acquisition unit: the first acquisition unit is used for acquiring data of the first target scientific research project information based on the technology of internet of things to obtain a first project data set, wherein the first project data set comprises a plurality of groups of project data; a first building unit: the first construction unit is used for constructing a two-dimensional rectangular coordinate model; a first input unit: the first input unit is used for inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information; a first generation unit: the first generating unit is used for generating a first monitoring configuration parameter according to the first quadrant distribution information; a second obtaining unit: the second obtaining unit is used for obtaining first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters; a first transmission unit: the first transmission unit is used for transmitting the first real-time monitoring data to a cloud server for project quality prediction to obtain a first prediction quality coefficient; a third obtaining unit: the third obtaining unit is used for obtaining a first optimized monitoring item according to the first prediction quality coefficient; a first optimization unit: the first optimization unit is used for carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
In a third aspect, an embodiment of the present application provides a scientific research project management apparatus based on an internet of things technology, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
obtaining first target scientific research project information; acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data; constructing a two-dimensional rectangular coordinate model; inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information; generating a first monitoring configuration parameter according to the first quadrant distribution information; acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters; transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient; obtaining a first optimized monitoring item according to the first prediction quality coefficient; and carrying out optimization management on the target scientific research project based on the first optimization monitoring item. Different characteristic dimensions based on scientific research projects are achieved, targeted dimension management is carried out on the scientific research projects, then project target quality coefficients of the cloud server are compared, the scientific research projects are continuously optimized, and comprehensive management of the scientific research projects in a full period and in multiple directions is achieved, so that project progress is mastered in real time, unknown risks of the projects are reduced, and the scientific research projects are intelligently, plurally and characteristically managed.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic flow chart of a scientific research project management method based on the internet of things technology in an embodiment of the application;
fig. 2 is a schematic flow chart illustrating analogy management of the first related scientific research project based on a scientific research project management method of an internet of things technology according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a scientific research project management method based on the internet of things technology according to the first incentive data to generate the first optimized monitoring item in the embodiment of the present application;
fig. 4 is a schematic flow chart illustrating that the first quality mapping data set is updated to obtain a second quality mapping data set according to the scientific research project management method based on the internet of things technology in the embodiment of the present application;
fig. 5 is a schematic structural diagram of a scientific research project management system based on the internet of things technology according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Detailed Description
By providing the scientific research project management method and system based on the internet of things technology, the technical problems that in the prior art, complete-period and multidirectional comprehensive management cannot be performed on scientific research projects, project progress cannot be mastered in real time, and unknown risks of the projects are improved are solved. Based on different characteristic dimensions of the scientific research projects, targeted dimension management is carried out on the scientific research projects, then project target quality coefficients of the cloud server are compared, the scientific research projects are continuously optimized, comprehensive management of the scientific research projects in a full period and in multiple directions is achieved, the project progress is mastered in real time, unknown risks of the projects are reduced, and the scientific research projects are intelligently, plurally and characteristically managed.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Scientific research project management refers to the whole-course management of projects from project application, project establishment and demonstration, organization and implementation, examination and evaluation, acceptance and verification, result declaration, scientific and technological popularization and file volume entering. The aim is to implement institutionalized and scientific management of scientific research projects, ensure the scientific research plans to be completed satisfactorily, produce achievements, talents and benefits and improve competitiveness. The technical problems that complete-cycle and multi-azimuth comprehensive management cannot be carried out on scientific research projects, project progress cannot be mastered in real time, and unknown risks of the projects are improved exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a scientific research project management method based on the internet of things technology, wherein the method is applied to a scientific research project management system based on the internet of things technology, the system is in communication connection with a cloud server, and the method comprises the following steps: obtaining first target scientific research project information; acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data; constructing a two-dimensional rectangular coordinate model; inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information; generating a first monitoring configuration parameter according to the first quadrant distribution information; acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters; transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient; obtaining a first optimized monitoring item according to the first prediction quality coefficient; and carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
For better understanding of the above technical solutions, the following detailed descriptions will be provided in conjunction with the drawings and the detailed description of the embodiments.
Example one
As shown in fig. 1, an embodiment of the present application provides a scientific research project management method based on an internet of things technology, where the method is applied to a scientific research project management system based on an internet of things technology, the system is in communication connection with a cloud server, and the method includes:
step S100: obtaining first target scientific research project information;
step S200: acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data;
specifically, scientific research project management refers to the whole-course management of projects from project application, project establishment and demonstration, organization and implementation, examination and evaluation, acceptance and verification, result declaration, scientific and technological popularization and file volume entry. The aim is to implement institutionalized and scientific management of scientific research projects, ensure the scientific research plans to be completed satisfactorily, produce achievements, talents and benefits and improve competitiveness. However, the prior art has the technical problems that the comprehensive management of the scientific research projects in a full period and multiple directions cannot be carried out, so that the project progress cannot be mastered in real time, and the unknown risks of the projects are improved. In order to solve the problems, the embodiment of the application provides a scientific research project management method based on the internet of things technology, and the internet of things technology is applied to scientific research project management, so that the scientific research projects are intelligently managed. Further, the first target scientific research project information is a scientific research project which needs to be intelligently managed in the application, and may include scientific research projects in any fields such as photoelectricity, chip, medical treatment, environmental protection and the like, and specifically includes project pre-estimation period, human and material input conditions and the like.
Further, the internet of things refers to the fact that any object is connected with a network through information sensing equipment according to an agreed protocol, and the object carries out information exchange and communication through an information transmission medium, so that functions of intelligent identification, positioning, tracking, supervision and the like are achieved. Taking environmental scientific research project management as an example, the first project data set includes a relevant environment monitoring data set obtained based on monitoring of various sensors, wherein the plurality of sets of project data include multi-characteristic data of project input manpower and material resources, scientific research period and the like.
Step S300: constructing a two-dimensional rectangular coordinate model;
step S400: inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information;
further, step S300 includes:
step S310: constructing a project multi-dimensional index according to the first target scientific research project information;
step S320: acquiring a management keyword set;
step S330: carrying out matching degree analysis on the project multi-dimensional indexes according to the management keyword set to obtain first matching degree arrangement information;
step S340: and constructing the two-dimensional rectangular coordinate model by taking the first dimension item and the second dimension item in the first matching degree arrangement information as input dimensions.
Specifically, in order to construct the two-dimensional rectangular coordinate model, further, a project multidimensional index may be constructed according to the first target scientific research project information, where the project multidimensional index includes multidimensional characteristic indexes such as progress control, expense cycle management, quality risk management, project confidentiality, and the like, the management keyword set may be understood as which dimensions the first target scientific research project is to be managed from to obtain a keyword set of target management dimensions, and further, the matching degree analysis is performed from the project multidimensional index according to the management keyword set to obtain first matching degree arrangement information, where the first matching degree arrangement information is a descending arrangement of the target management dimensions, that is, a dimension before arrangement is the target management dimension, and a first dimension item and a second dimension item in the first matching degree arrangement information are used as input dimensions, for example, if the two-dimensional rectangular coordinate model is to be managed from the aspects of progress control and quality risk, the two-dimensional rectangular coordinate model can be constructed by using the progress control as a first dimension item and the quality risk as a second dimension item.
After the two-dimensional rectangular coordinate model is constructed, the first project data set can be input into the two-dimensional rectangular coordinate model, quadrant division is carried out on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model, first quadrant distribution information is generated, if a target project is required to be managed based on progress control and quality risk, the first quadrant distribution information comprises a multi-data set related to the progress control and the quality risk of the environmental protection project.
Step S500: generating a first monitoring configuration parameter according to the first quadrant distribution information;
step S600: acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters;
specifically, it is known that the first quadrant distribution information is obtained, and further, a first monitoring configuration parameter may be generated, where the first monitoring configuration parameter is early warning data for performing threshold monitoring on the multiple data sets, and if a high-quality and low-risk management project is required to be ensured, the parameter may be set according to the requirement to perform data monitoring, and the first real-time monitoring data is a monitored data set.
Step S700: transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient;
step S800: obtaining a first optimized monitoring item according to the first prediction quality coefficient;
step S900: and carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
Specifically, after the first real-time monitoring data is obtained, the first real-time monitoring data may be transmitted to the cloud server for project quality prediction, that is, whether the actual project monitoring data can meet a required project quality standard is predicted, the first prediction quality coefficient is a difference coefficient between the actual monitoring data and target monitoring data, a first optimized monitoring item may be obtained according to the first prediction quality coefficient, that is, the operating project characteristics of the first target scientific research project are optimized through the difference coefficient, and then the target scientific research project is optimized and managed based on the first optimized monitoring item, if required, confidentiality management is performed on the operating project, but the actual data indicates that the confidentiality of the operating project data temporarily does not reach confidentiality data management, and therefore, optimization management may be performed based on the confidentiality project characteristic, the comprehensive management of the scientific research projects in a full period and multiple directions is realized, and the unknown risks of the projects are reduced.
Further, as shown in fig. 2, the embodiment of the present application further includes:
step S1010: obtaining a plurality of sub scientific research project information of the first target scientific research project;
step S1020: obtaining a plurality of sub scientific research project information of a first associated scientific research project;
step S1030: performing sub-project monitoring feature item contact ratio analysis on the plurality of sub-scientific research project information of the first target scientific research project and the plurality of sub-scientific research project information of the first associated scientific research project to obtain a first contact ratio coefficient;
step S1040: obtaining a first analog instruction according to the first contact ratio coefficient;
step S1050: and performing analogy management on the first related scientific research projects on the basis of the first target scientific research projects according to the first analogy command.
Specifically, in order to perform intelligent simplified management on scientific research projects, further, the multiple pieces of sub-scientific research project information are multiple branch sub-projects of the first target scientific research project, for example, data acquisition is performed on the pollutant discharged from different rivers, the multiple pieces of sub-scientific research project information of the first related scientific research project can be understood as data acquisition is performed on the pollutant discharged from different lakes, and then the multiple pieces of sub-scientific research project information of the first target scientific research project and the multiple pieces of sub-scientific research project information of the first related scientific research project are subjected to sub-project monitoring feature item coincidence degree analysis to obtain a first coincidence degree coefficient, which is a coincidence degree coefficient between the pollutant discharged collection data of different rivers and the pollutant discharged collection data of different lakes, if the first coincidence degree coefficient is higher, according to the first class instruction, and performing analogy management on the first related scientific research project on the basis of the first target scientific research project, namely, the coincidence degree of the two data is higher, and one project management mode for collecting data can be used for analogy with the other project management mode for collecting data, so that the project management process for collecting data is simplified, and the intelligent simplified management on the scientific research project is realized.
Further, as shown in fig. 3, the step S800 of obtaining a first optimized monitoring term according to the first prediction quality coefficient includes:
step S810: constructing a first quality mapping data set according to the mapping relation between the data item of the first real-time monitoring data and the first prediction quality coefficient;
step S820: obtaining a preset quality coefficient threshold value;
step S830: taking the first quality mapping data set as a distribution chain, taking the preset quality coefficient threshold value as a recursion target, and constructing a Markov chain model;
step S840: carrying out recursive prediction according to the Markov chain model to obtain first incentive data;
step S850: and generating the first optimized monitoring item according to the first excitation data.
Specifically, to generate the first optimized monitoring item, further, a first quality mapping data set may be constructed according to a mapping relationship between a data item of the first real-time monitoring data and the first predicted quality coefficient, where the first quality mapping data set is data distribution information between actual monitoring data and a difference coefficient, that is, if the actual monitoring data is infinitely close to target detection data, the difference coefficient becomes infinitely smaller, and at the same time, the preset quality coefficient threshold is a quality coefficient threshold in a more expected state, further, the first quality mapping data set may be used as a distribution chain, the preset quality coefficient threshold may be used as a recursive target, and a markov chain model may be constructed, where a markov chain is a set of discrete random variables with markov properties, and in each step, the system can change from one state to another or maintain the current state based on a probability distribution, the change in state being called a transition and the probability associated with the different state change being called a transition probability. The markov decision process is a typical series of decision processes, and can be constructed based on the mapping relationship between the current state and the next action. And then carrying out recursive prediction according to the Kefu chain model to obtain first excitation data, wherein the first excitation data can be understood as target monitoring data in an expected state, and further generating the first optimized monitoring item according to the first excitation data, namely the first optimized monitoring item is known to be obtained by obtaining the target monitoring data and real-time monitoring data in the expected state, so that real-time difference information can be obtained, and further generating the first optimized monitoring item based on the real-time difference information to realize accurate obtaining of the optimized monitoring item.
Further, as shown in fig. 4, after the constructing a first quality mapping data set according to the mapping relationship between the data item of the first real-time monitoring data and the first prediction quality coefficient, step S810 includes:
step S811: inputting the first quality mapping data set into a self-test calculation unit according to a first self-test instruction, wherein the self-test calculation unit comprises a first partition and a second partition;
step S812: the self-checking calculation unit performs checking calculation on the first quality mapping data set in the first partition to obtain a first statistic;
step S813: the self-checking calculation unit performs significant calculation on the first quality mapping data in the second partition to obtain a second statistic;
step S814: and if the first statistic is less than or equal to the second statistic, updating the first quality mapping data set to obtain a second quality mapping data set.
Specifically, in order to make the constructed first quality mapping data set more accurate, further, the first quality mapping data set may be input into the self-checking computing unit according to the first self-checking instruction, that is, a "markov" check is performed on the discrete sequence, specifically, a markov chain of the discrete sequence is usually selected to perform the "markov" check on a sequence with a random variable, and a common χ 2 (chi-square distribution) statistic is checked. Wherein, the self-test calculation unit includes a first partition and a second partition, specifically, the first quality mapping data set can be checked and calculated in the first partition, the first statistic is a result set of the checking and calculation, and at the same time, performing a significant computation on the first quality mapping data in the second partition, where the second statistic is a result set of significant computation, and further determining the first statistic and the second statistic, if the first statistic is less than or equal to the second statistic, indicating that the first quality mapping dataset is not sufficiently accurate, the first quality mapping data set can be updated to obtain a second quality mapping data set, and the second quality mapping data set is the updated data discrete sequence, so that the data discrete sequence of the Markov chain is constructed more accurately.
Further, if the first statistic is less than or equal to the second statistic, the first quality mapping data set is updated to obtain a second quality mapping data set, and step S814 includes:
step S8141: obtaining a first statistical difference value between the first statistical quantity and the second statistical quantity;
step S8142: if the first statistical difference value is in a preset statistical difference value, a first updating instruction is obtained;
step S8143: performing data item feature optimization on the first quality mapping data set according to the first updating instruction to obtain a first optimized data item;
step S8144: generating the second quality mapping dataset from the first optimization data item.
In particular, for generating the second quality mapping data set, further, a difference calculation may be performed on the first statistical quantity and the second statistical quantity, obtaining the first statistical quantity difference, further judging the first statistic difference value, judging whether the first statistic difference value is in a preset statistic difference value, if the first statistical difference is within the predetermined statistical difference, the first update instruction may be executed, according to the first update instruction, performing a data item feature optimization on the first quality mapping dataset to obtain a first optimized data item, the first optimized data item is a sub item specifically needing data optimization, and then the second quality mapping data set is generated according to the first optimized data item, so that the data discrete sequence is optimized more accurately, and the second quality mapping data set is generated accurately.
Further, the embodiment of the present application further includes:
step S831: acquiring cost control information and project cycle information according to the first real-time monitoring data;
step S832: constructing a first cost mapping data set according to the cost control information and the project period information;
step S833: generating a first optimized mapping data set by data fitting of a data sequence of the first cost mapping data set with a data sequence of the first quality mapping data set;
step S834: and performing model optimization on the Markov chain model according to the first optimization mapping data set.
Specifically, in order to ensure the data diversity and model optimization of the markov chain model, further, cost control information and project cycle information may be obtained according to the first real-time monitoring data, where the cost control information is management control on the input cost of the first target scientific research project information, the project cycle information is time control on the operation cycle of the first target scientific research project information, and further, according to the cost control information and the project cycle information, a first cost mapping data set is constructed, where the first cost mapping data set is a mapping data set constructed by influence of the cost control and operation cycle of the project on the project quality, and further, the cost control and project cycle information of the project is obtained by performing data fitting on the data sequence of the first cost mapping data set and the data sequence of the first quality mapping data set, so as to perform data fitting on the data sequence of the first cost control and project, And performing data fitting on discrete data such as the operation period, the project quality and the like to obtain denser discrete data, wherein the first optimization mapping data set is a data fitting result, and then performing model optimization on the Markov chain model according to the first optimization mapping data set, so that the data diversity and the model optimization of the Markov chain model are ensured.
Compared with the prior art, the invention has the following beneficial effects:
1. obtaining first target scientific research project information; acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data; constructing a two-dimensional rectangular coordinate model; inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information; generating a first monitoring configuration parameter according to the first quadrant distribution information; acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters; transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient; obtaining a first optimized monitoring item according to the first prediction quality coefficient; and carrying out optimization management on the target scientific research project based on the first optimization monitoring item. Different characteristic dimensions based on scientific research projects are achieved, targeted dimension management is carried out on the scientific research projects, then project target quality coefficients of the cloud server are compared, the scientific research projects are continuously optimized, and comprehensive management of the scientific research projects in a full period and in multiple directions is achieved, so that project progress is mastered in real time, unknown risks of the projects are reduced, and the scientific research projects are intelligently, plurally and characteristically managed.
2. And performing sub-project monitoring characteristic item contact ratio analysis on the plurality of sub-scientific research project information of the first target scientific research project and the plurality of sub-scientific research project information of the first related scientific research project, performing analogy management on the first related scientific research project, and if the contact ratio of the two data is higher, using one project management mode for collecting data to simulate the other project management mode for collecting data, so as to simplify the project management process for collecting data and realize intelligent simplified management on the scientific research projects.
Example two
Based on the same inventive concept as the scientific research project management method based on the internet of things technology in the foregoing embodiment, the present invention further provides a scientific research project management system based on the internet of things technology, as shown in fig. 5, the system includes:
the first obtaining unit 11: the first obtaining unit 11 is configured to obtain first target scientific research project information;
the first acquisition unit 12: the first acquisition unit 12 is configured to perform data acquisition on the first target scientific research project information based on an internet of things technology to obtain a first project data set, where the first project data set includes multiple sets of project data;
the first building element 13: the first construction unit 13 is configured to construct a two-dimensional rectangular coordinate model;
first input unit 14: the first input unit 14 is configured to input the first item data set into the two-dimensional rectangular coordinate model, perform quadrant division on the first item data set according to different dimensions of the two-dimensional rectangular coordinate model, and generate first quadrant distribution information;
the first generation unit 15: the first generating unit 15 is configured to generate a first monitoring configuration parameter according to the first quadrant distribution information;
the second obtaining unit 16: the second obtaining unit 16 is configured to obtain first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameter;
the first transmission unit 17: the first transmission unit 17 is configured to transmit the first real-time monitoring data to a cloud server to perform project quality prediction, so as to obtain a first prediction quality coefficient;
the third obtaining unit 18: the third obtaining unit 18 is configured to obtain a first optimized monitoring term according to the first prediction quality coefficient;
the first optimization unit 19: the first optimization unit 19 is configured to perform optimization management on the target scientific research project based on the first optimization monitoring item.
Further, the system further comprises:
a fourth obtaining unit: the fourth obtaining unit is used for obtaining a plurality of pieces of sub scientific research project information of the first target scientific research project;
a fifth obtaining unit: the fifth obtaining unit is used for obtaining a plurality of pieces of sub scientific research project information of the first related scientific research project;
a first analysis unit: the first analysis unit is used for performing sub-project monitoring feature item contact ratio analysis on the plurality of sub scientific research project information of the first target scientific research project and the plurality of sub scientific research project information of the first related scientific research project to obtain a first contact ratio coefficient;
a sixth obtaining unit: the sixth obtaining unit is configured to obtain a first analog instruction according to the first contact ratio coefficient;
a first management unit: the first management unit is used for conducting analogy management on the first related scientific research projects on the basis of the first target scientific research projects according to the first analogy instructions.
Further, the system further comprises:
a second building element: the second construction unit is used for constructing a first quality mapping data set according to the mapping relation between the data item of the first real-time monitoring data and the first prediction quality coefficient;
a seventh obtaining unit: the seventh obtaining unit is used for obtaining a preset quality coefficient threshold value;
a third building element: the third construction unit is used for constructing a Markov chain model by taking the first quality mapping data set as a distribution chain and the preset quality coefficient threshold value as a recursion target;
an eighth obtaining unit: the eighth obtaining unit is configured to perform recursive prediction according to the markov chain model to obtain first excitation data;
a second generation unit: the second generating unit is used for generating the first optimization monitoring item according to the first excitation data.
Further, the system further comprises:
a second input unit: the second input unit is used for inputting the first quality mapping data set into a self-test calculation unit according to a first self-test instruction, wherein the self-test calculation unit comprises a first partition and a second partition;
the first calculation unit: the first computing unit is used for the self-test computing unit to perform test computation on the first quality mapping data set in the first partition to obtain a first statistic;
a second calculation unit: the second calculating unit is used for the self-checking calculating unit to perform significant calculation on the first quality mapping data in the second partition to obtain a second statistic;
a first update unit: the first updating unit is configured to update the first quality mapping data set if the first statistic is less than or equal to the second statistic, so as to obtain a second quality mapping data set.
Further, the system further comprises:
a ninth obtaining unit: the ninth obtaining unit is configured to obtain a first statistical difference between the first statistical quantity and the second statistical quantity;
a tenth obtaining unit: the tenth obtaining unit is configured to obtain a first update instruction if the first statistical difference is in a preset statistical difference;
a second optimization unit: the second optimization unit is used for performing data item feature optimization on the first quality mapping data set according to the first updating instruction to obtain a first optimized data item;
a third generation unit: the third generation unit is configured to generate the second quality mapping data set based on the first optimization data item.
Further, the system further comprises:
a fourth construction unit: the fourth construction unit is used for constructing project multi-dimensional indexes according to the first target scientific research project information;
an eleventh obtaining unit: the eleventh obtaining unit is configured to obtain a set of management keywords;
a twelfth obtaining unit: the twelfth obtaining unit is configured to perform matching degree analysis on the project multidimensional indexes according to the management keyword set to obtain first matching degree arrangement information;
a fifth construction unit: the fifth construction unit is configured to construct the two-dimensional rectangular coordinate model by using the first dimension item and the second dimension item in the first matching degree arrangement information as input dimensions.
Further, the system further comprises:
a thirteenth obtaining unit: the thirteenth obtaining unit is configured to obtain cost control information and project cycle information according to the first real-time monitoring data;
a sixth construction unit: the sixth construction unit is configured to construct a first cost mapping data set according to the cost management and control information and the project cycle information;
a first fitting unit: the first fitting unit is used for performing data fitting on the data sequence of the first cost mapping data set and the data sequence of the first quality mapping data set to generate a first optimized mapping data set;
a third optimization unit: the third optimization unit is used for carrying out model optimization on the Markov chain model according to the first optimization mapping data set.
Various changes and specific examples of the scientific research project management method based on the internet of things technology in the first embodiment of fig. 1 are also applicable to the scientific research project management system based on the internet of things technology in the present embodiment, and through the foregoing detailed description of the scientific research project management method based on the internet of things technology, a skilled person can clearly know the implementation method of the scientific research project management system based on the internet of things technology in the present embodiment, so for the sake of brevity of the description, detailed description is not repeated again.
EXAMPLE III
The electronic apparatus of the embodiment of the present application is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the scientific research project management method based on the internet of things technology in the embodiment, the invention also provides a scientific research project management system based on the internet of things technology, wherein a computer program is stored on the scientific research project management system, and when the computer program is executed by a processor, the steps of any method of the scientific research project management system based on the internet of things technology are realized.
Where in fig. 6 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the application provides a scientific research project management method based on the internet of things technology, wherein the method is applied to a scientific research project management system based on the internet of things technology, the system is in communication connection with a cloud server, and the method comprises the following steps: obtaining first target scientific research project information; acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data; constructing a two-dimensional rectangular coordinate model; inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information; generating a first monitoring configuration parameter according to the first quadrant distribution information; acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters; transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient; obtaining a first optimized monitoring item according to the first prediction quality coefficient; and carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A scientific research project management method based on Internet of things technology is applied to a scientific research project management system based on Internet of things technology, the system is in communication connection with a cloud server, and the method comprises the following steps:
obtaining first target scientific research project information;
acquiring data of the first target scientific research project information based on the technology of the Internet of things to obtain a first project data set, wherein the first project data set comprises multiple groups of project data;
constructing a two-dimensional rectangular coordinate model;
inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information;
generating a first monitoring configuration parameter according to the first quadrant distribution information;
acquiring first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters;
transmitting the first real-time monitoring data to the cloud server for project quality prediction to obtain a first prediction quality coefficient;
obtaining a first optimized monitoring item according to the first prediction quality coefficient;
and carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
2. The method of claim 1, wherein the method further comprises:
obtaining a plurality of sub scientific research project information of the first target scientific research project;
obtaining a plurality of sub scientific research project information of a first associated scientific research project;
performing sub-project monitoring feature item contact ratio analysis on the plurality of sub-scientific research project information of the first target scientific research project and the plurality of sub-scientific research project information of the first associated scientific research project to obtain a first contact ratio coefficient;
obtaining a first analog instruction according to the first contact ratio coefficient;
and performing analogy management on the first related scientific research projects on the basis of the first target scientific research projects according to the first analogy command.
3. The method of claim 1, wherein the obtaining a first optimized monitoring term is based on the first prediction quality coefficient, the method further comprising:
constructing a first quality mapping data set according to the mapping relation between the data item of the first real-time monitoring data and the first prediction quality coefficient;
obtaining a preset quality coefficient threshold value;
taking the first quality mapping data set as a distribution chain, taking the preset quality coefficient threshold value as a recursion target, and constructing a Markov chain model;
carrying out recursive prediction according to the Markov chain model to obtain first incentive data;
and generating the first optimized monitoring item according to the first excitation data.
4. The method of claim 3, wherein after constructing the first quality mapping data set based on the mapping between the data items of the first real-time monitoring data and the first prediction quality coefficient, the method further comprises:
inputting the first quality mapping data set into a self-test calculation unit according to a first self-test instruction, wherein the self-test calculation unit comprises a first partition and a second partition;
the self-checking calculation unit performs checking calculation on the first quality mapping data set in the first partition to obtain a first statistic;
the self-checking calculation unit performs significant calculation on the first quality mapping data in the second partition to obtain a second statistic;
and if the first statistic is less than or equal to the second statistic, updating the first quality mapping data set to obtain a second quality mapping data set.
5. The method of claim 4, wherein the updating the first quality mapping dataset if the first statistic is less than or equal to the second statistic to obtain a second quality mapping dataset, the method further comprising:
obtaining a first statistical difference value between the first statistical quantity and the second statistical quantity;
if the first statistical difference value is in a preset statistical difference value, a first updating instruction is obtained;
performing data item feature optimization on the first quality mapping data set according to the first updating instruction to obtain a first optimized data item;
generating the second quality mapping dataset from the first optimization data item.
6. The method of claim 1, wherein the constructing a two-dimensional rectangular coordinate model, the method further comprises:
constructing a project multi-dimensional index according to the first target scientific research project information;
acquiring a management keyword set;
carrying out matching degree analysis on the project multi-dimensional indexes according to the management keyword set to obtain first matching degree arrangement information;
and constructing the two-dimensional rectangular coordinate model by taking the first dimension item and the second dimension item in the first matching degree arrangement information as input dimensions.
7. The method of claim 3, wherein the method further comprises:
acquiring cost control information and project cycle information according to the first real-time monitoring data;
constructing a first cost mapping data set according to the cost control information and the project period information;
generating a first optimized mapping data set by data fitting of a data sequence of the first cost mapping data set with a data sequence of the first quality mapping data set;
and performing model optimization on the Markov chain model according to the first optimization mapping data set.
8. A scientific research project management system based on Internet of things technology, wherein the system comprises:
a first obtaining unit: the first obtaining unit is used for obtaining first target scientific research project information;
a first acquisition unit: the first acquisition unit is used for acquiring data of the first target scientific research project information based on the technology of internet of things to obtain a first project data set, wherein the first project data set comprises a plurality of groups of project data;
a first building unit: the first construction unit is used for constructing a two-dimensional rectangular coordinate model;
a first input unit: the first input unit is used for inputting the first project data set into the two-dimensional rectangular coordinate model, and performing quadrant division on the first project data set according to different dimensions of the two-dimensional rectangular coordinate model to generate first quadrant distribution information;
a first generation unit: the first generating unit is used for generating a first monitoring configuration parameter according to the first quadrant distribution information;
a second obtaining unit: the second obtaining unit is used for obtaining first real-time monitoring data of the first target scientific research project information according to the first monitoring configuration parameters;
a first transmission unit: the first transmission unit is used for transmitting the first real-time monitoring data to a cloud server for project quality prediction to obtain a first prediction quality coefficient;
a third obtaining unit: the third obtaining unit is used for obtaining a first optimized monitoring item according to the first prediction quality coefficient;
a first optimization unit: the first optimization unit is used for carrying out optimization management on the target scientific research project based on the first optimization monitoring item.
9. An internet of things technology-based research project management system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-7 when executing the program.
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