CN112801821A - Engineering construction team portrait method and system based on big data analysis - Google Patents
Engineering construction team portrait method and system based on big data analysis Download PDFInfo
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Abstract
The invention provides a project construction team portrait method and a system based on big data analysis, wherein the method comprises the following steps: acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel; constructing a data matrix of historical construction project attributes of engineering construction team personnel, and carrying out cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing; and after the data matrix is normalized, performing data segmentation by adopting a network segmentation algorithm, and drawing a personnel portrait of an engineering construction team by combining the characteristic information. The application also provides an engineering construction team portrait system based on big data analysis, the application greatly improves personnel portrait depicting accuracy, provides high-quality and reliable selection guidance for engineering constructors in selection, and avoids the situations of engineering construction progress lag or hidden danger and the like caused by reasons such as the fact that the constructors and engineering are not suitable.
Description
Technical Field
The invention belongs to the technical field of engineering construction, and particularly relates to an engineering construction team portrait method and system based on big data analysis.
Background
Engineering constructors are indispensable pillars in the engineering construction process, however, the personnel basic information, qualification information, historical information and the like of the engineering constructors are all foundation stones for guaranteeing the engineering construction efficiency and quality. How fast, accurate collection these loaded down with trivial details personnel information data to carry out analysis, judgement to these data, thereby reach personnel to the effect of guarantee engineering construction progress, carry out accurate judgement to constructor, whether constructor satisfies the requirement of the steady construction of engineering, all leave personnel to portrait.
In the age of paperization office, personnel information collection depends on mouth-to-mouth transmission and various papery data to a great extent, and a series of problems of slow information collection efficiency, low accuracy, incomplete collection and the like usually exist in the laggard information collection modes, so that a large amount of time and labor cost are consumed, and the obtained products are often unsatisfactory. How to adopt a more efficient, simple and accurate information collection mode is urgent. With the development of company informatization and the continuous maturity of big data technology, a personnel information collection mode through internet and big data analysis becomes possible. Carry out automatic collection, analysis, gather to engineering constructor information through the internet, realize personnel's information collection, it is perfect, promote, constantly promote personnel's information mastery degree, form the personnel portrait to every constructor, thereby be more convenient for look up and to personnel's information analysis, select the constructor who is more suitable for corresponding engineering to get into construction team in, reduce because of not professional, the engineering progress that factors such as inexperienced caused lags behind, relevant problems such as construction risk promotion, thereby further promote engineering efficiency of construction, practice thrift engineering construction cost.
Disclosure of Invention
In order to solve the technical problems, the invention provides a project construction team portrait method and a project construction team portrait system based on big data analysis, which fully utilize the trend of big data analysis to portrait a project construction team, improve project progress construction and reduce construction risks.
In order to achieve the purpose, the invention adopts the following technical scheme:
an engineering construction team portrait method based on big data analysis comprises the following steps:
acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel;
constructing a data matrix of historical construction project attributes of engineering construction team personnel, and carrying out cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing;
and after the data matrix is normalized, performing data segmentation by adopting a network segmentation algorithm, and drawing a personnel portrait of an engineering construction team by combining the characteristic information.
Further, the characteristic information of the personnel of the engineering construction team comprises but is not limited to identity information, qualification information, face information and voice information.
Further, the historical construction project attribute information of the personnel of the engineering construction team comprises but is not limited to historical construction project participating information, evidence holding conditions, participation time and positions during participation.
Furthermore, the method for building the data matrix of the historical construction project attributes of the engineering construction team personnel is to build the data matrix of n x m orders based on the m historical construction project attributes of the engineering construction team personnel with the number of n.
Further, the method for forming the similarity matrix by calculating the similarity between the objects in the data matrix and performing normalization processing on the similarity matrix comprises the following steps:
calculating the distance between every two objects as dissimilarity;
converting the dissimilarity between every two objects into similarity to form a similarity matrix;
and carrying out normalization processing on the similarity between 0 and 1.
Further, after the normalization processing of the data matrix, the process of performing data segmentation by using a network segmentation algorithm is as follows:
dividing the image by adopting an Ncut algorithm;
and obtaining analysis indexes of engineering construction team personnel after the division, and drawing an engineering construction team personnel portrait by combining the characteristic information.
The invention also provides an engineering construction team portrait system based on big data analysis, which comprises an acquisition module, a construction processing module and a segmentation drawing module;
the acquisition module is used for acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel;
the construction processing module is used for constructing a data matrix of historical construction project attributes of personnel of an engineering construction team and performing cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing;
and the segmentation drawing module is used for carrying out data segmentation by adopting a network segmentation algorithm after the data matrix is subjected to normalization processing, and drawing the personnel portrait of the engineering construction team by combining the characteristic information.
Further, the acquiring module comprises a first acquiring module and a second acquiring module;
the first acquisition module is used for acquiring characteristic information of personnel of a project construction team; the characteristic information of the personnel of the engineering construction team comprises but is not limited to identity information, qualification information, face information and voice information;
the second acquisition module is used for acquiring the attribute information of the historical construction project of the personnel of the construction team; the historical construction undertaking project attribute information of the engineering construction team personnel comprises but is not limited to historical construction participation project information, warranty conditions, participation time and positions during participation.
Further, the building processing module comprises a building module and a processing module;
the construction module is used for constructing a data matrix of historical construction project attributes of personnel of an engineering construction team and performing cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix;
the processing module is used for carrying out normalization processing on the similarity matrix.
Further, the segmentation and drawing module comprises a segmentation module and a drawing module;
the segmentation module is used for carrying out data segmentation by adopting a network segmentation algorithm after the data matrix is subjected to normalization processing;
and the drawing module is used for drawing the personnel portrait of the engineering construction team by combining the characteristic information.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the invention provides a project construction team portrait method and a system based on big data analysis, wherein the method comprises the following steps: acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel; constructing a data matrix of historical construction project attributes of engineering construction team personnel, and carrying out cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing; and after the data matrix is normalized, performing data segmentation by adopting a network segmentation algorithm, and drawing a personnel portrait of an engineering construction team by combining the characteristic information. The application also provides an engineering construction team portrait method based on big data analysis, and the application replaces the traditional paper filling and oral personnel information acquisition mode with a networked information collection and big data analysis mode, reduces the information collection time and labor cost, and simultaneously performs similarity analysis and normalization processing on the collected image data by combining historical data, and finally performs segmentation on the image to obtain corresponding analysis indexes of the personnel of the construction team. The personnel portrait drawing accuracy is greatly improved, high-quality and reliable selection guidance is provided for engineering constructors, and the situations of engineering construction progress lag or hidden danger and the like caused by the reasons that the constructors and the engineering are not suitable are avoided.
Drawings
FIG. 1 is a flow chart of a method for representing an image of a construction team based on big data analysis according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of an image system of a construction team based on big data analysis in embodiment 1 of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Example 1
The embodiment 1 of the invention provides a project construction team image method based on big data analysis, and as shown in fig. 1, a flow chart of the project construction team image method based on big data analysis in the embodiment 1 of the invention is provided.
In step S101, the processing flow is started.
In step S102, characteristic information of the personnel of the engineering construction team is acquired; firstly, the information of the unit to which the personnel belongs is determined through a PC terminal, then basic information of the personnel is registered at an APP terminal of the personnel of a construction team, and identity information, qualification information, face recognition, voice recognition and the like are automatically recognized through an OCR technology. The information to be acquired in the present application is not limited to the types listed in embodiment 1. The acquired information constitutes highly refined characteristic information of the personnel portrait of the construction team.
In step S103, acquiring historical construction project attribute information of personnel of a construction team; the attribute information of the historical underwriting project includes, but is not limited to, historical participating project information, the condition of warranty, participating time, whether the current participating project post is concurrently in duty, and the like.
In step S104, a data matrix of project attributes of historical underwriting personnel of the construction team is constructed. And constructing an n-m-order data matrix on the basis of m historical construction project attributes of n engineering construction team personnel. The data matrix represents n objects by m attributes, such as: the data of n constructors is provided with m attributes such as age, duty certificate, construction condition and the like to represent the attributes. Thus, a matrix of n x m data is obtained.
In step S105, the data matrix is cluster-analyzed; and then, forming a similarity matrix by calculating the similarity among the objects in the data matrix, and carrying out normalization processing on the similarity matrix. Firstly, calculating the distance between every two objects as dissimilarity; converting the dissimilarity between every two objects into similarity to form a similarity matrix; and carrying out normalization processing on the similarity between 0 and 1.
At this stage, we first calculate the similarity between objects according to some features of the data objects, and construct a similarity matrix between these objects. Due to the characteristics of the data, we can calculate the distance between two objects, for example: and calculating the distance between two objects of the age and the risk processing capability of the constructor as the dissimilarity degree. Then, the dissimilarity between the objects is converted into the similarity between the objects, and in order to avoid a large difference in the value ranges of the similarity, the similarity between the objects needs to be normalized so that the similarity is between 0 and 1.
In step S106, after the data matrix is normalized, data is divided by using a network division algorithm, and a person figure of the construction team is drawn by combining the feature information. Dividing the image by adopting an Ncut algorithm; and obtaining analysis indexes of the personnel of the engineering construction team after the division, and drawing a personnel portrait of the engineering construction team by combining the characteristic information.
In the stage of network segmentation, a certain clustering judgment rule is used to generate a cluster represented by the characteristic. And the clusters are taken out from the data set one by one until no sample data exists in the data set, and the algorithm is ended and the clusters are output. Based on the processing result, the corresponding analysis indexes of the construction team are obtained, such as the age of the constructor, the risk processing capability and other factors, and through the visual display of data, the conditions of improving the engineering progress construction, reducing the construction risk, avoiding hidden dangers to the maximum extent and the like are achieved.
In step S107, the flow ends.
Example 2
Embodiment 2 of the present invention further provides an engineering construction team representation system based on big data analysis, and fig. 2 is a schematic diagram of the engineering construction team representation system based on big data analysis. The system comprises an acquisition module, a construction processing module and a segmentation drawing module;
the acquisition module is used for acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel;
the construction processing module is used for constructing a data matrix of historical construction project attributes of personnel of an engineering construction team and carrying out cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing;
and the segmentation drawing module is used for performing data segmentation by adopting a network segmentation algorithm after the data matrix is subjected to normalization processing, and drawing the personnel portrait of the engineering construction team by combining the characteristic information.
The acquisition module comprises a first acquisition module and a second acquisition module; the first acquisition module is used for acquiring characteristic information of personnel of a project construction team; the characteristic information of the personnel of the engineering construction team comprises but is not limited to identity information, qualification information, face information and voice information; the second acquisition module is used for acquiring the attribute information of the historical construction project of the personnel of the construction team; the historical construction undertaking project attribute information of the engineering construction team personnel comprises but is not limited to historical construction participation project information, warranty conditions, participation time and positions during participation.
The construction processing module comprises a construction module and a processing module; the construction module is used for constructing a data matrix of historical construction project attributes of engineering construction team personnel and carrying out cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix; the processing module is used for carrying out normalization processing on the similarity matrix.
The segmentation drawing module comprises a segmentation module and a drawing module; the segmentation module is used for carrying out data segmentation by adopting a network segmentation algorithm after the data matrix is subjected to normalization processing; and the drawing module is used for drawing the personnel portrait of the engineering construction team by combining the characteristic information.
The method makes full use of the trend of big data analysis, constructs high-precision portrait information for basic information of construction team personnel, qualification information, construction bearing project conditions (duty post, grading condition, post arrival condition, risk processing capability and the like) of personnel in the historical construction bearing project process and the like, visually displays characteristic information in different dimensions, improves project progress construction, reduces construction risk, avoids hidden danger and the like to the maximum extent, and assists in supporting the project construction guarantee quantity to be completed on time.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the scope of the present invention is not limited thereto. Various modifications and alterations will occur to those skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or changes which can be made by a person skilled in the art without creative efforts are still within the protection scope of the invention.
Claims (10)
1. An engineering construction team portrait method based on big data analysis is characterized by comprising the following steps:
acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel;
constructing a data matrix of historical construction project attributes of engineering construction team personnel, and carrying out cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing;
and after the data matrix is normalized, performing data segmentation by adopting a network segmentation algorithm, and drawing a personnel portrait of an engineering construction team by combining the characteristic information.
2. The big data analysis-based project construction team representation method according to claim 1, wherein the feature information of project construction team personnel comprises but is not limited to identity information, qualification information, face information and voice information.
3. The big data analysis-based project team representation method of claim 1, wherein the project team personnel historical underwriting project attribute information includes, but is not limited to, historical participating project information, warranty, participating time, and position at participating time.
4. The big data analysis-based project team representation method according to claim 1, wherein the method for constructing the data matrix of project property historical underwriting by project team personnel is to construct a data matrix of n m orders based on m historical underwriting project property of n project construction team personnel.
5. The big data analysis-based engineering construction team representation method according to claim 4, wherein the similarity matrix is formed by calculating the similarity between objects in the data matrix, and the method for normalizing the similarity matrix comprises the following steps:
calculating the distance between every two objects as dissimilarity;
converting the dissimilarity between every two objects into similarity to form a similarity matrix;
and carrying out normalization processing on the similarity between 0 and 1.
6. The big data analysis-based engineering construction team representation method according to claim 1, wherein after the normalization processing of the data matrix, the data segmentation process by using a network segmentation algorithm comprises the following steps:
dividing the image by adopting an Ncut algorithm;
and obtaining analysis indexes of engineering construction team personnel after the division, and drawing an engineering construction team personnel portrait by combining the characteristic information.
7. The engineering construction team portrait system based on big data analysis is characterized by comprising an acquisition module, a construction processing module and a segmentation drawing module;
the acquisition module is used for acquiring characteristic information of engineering construction team personnel and historical construction project attribute information of the construction team personnel;
the construction processing module is used for constructing a data matrix of historical construction project attributes of personnel of an engineering construction team and performing cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix, and the similarity matrix is subjected to normalization processing;
and the segmentation drawing module is used for carrying out data segmentation by adopting a network segmentation algorithm after the data matrix is subjected to normalization processing, and drawing the personnel portrait of the engineering construction team by combining the characteristic information.
8. The big data analysis-based engineering construction team representation system of claim 7, wherein the obtaining module comprises a first obtaining module and a second obtaining module;
the first acquisition module is used for acquiring characteristic information of personnel of a project construction team; the characteristic information of the personnel of the engineering construction team comprises but is not limited to identity information, qualification information, face information and voice information;
the second acquisition module is used for acquiring the attribute information of the historical construction project of the personnel of the construction team; the historical construction undertaking project attribute information of the engineering construction team personnel comprises but is not limited to historical construction participation project information, warranty conditions, participation time and positions during participation.
9. The big data analysis-based engineering construction team representation system of claim 7, wherein the build processing module comprises a build module and a processing module;
the construction module is used for constructing a data matrix of historical construction project attributes of personnel of an engineering construction team and performing cluster analysis on the data matrix; then, a similarity matrix is formed by calculating the similarity between the objects in the data matrix;
the processing module is used for carrying out normalization processing on the similarity matrix.
10. The big data analysis-based engineering construction team representation system of claim 7, wherein the segmentation and rendering module comprises a segmentation module and a rendering module;
the segmentation module is used for carrying out data segmentation by adopting a network segmentation algorithm after the data matrix is subjected to normalization processing;
and the drawing module is used for drawing the personnel portrait of the engineering construction team by combining the characteristic information.
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