# An Innovation and Entrepreneurship Management System for Universities Based on Cluster Analysis Theory

Authors: Weijie Zhao; Liang Fang
Journal: Journal of Sensors
 (2022)

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## Abstract

With the development of the market economy and changes in the demand for talents in the market, the cultivation of innovation and entrepreneurship (IAE) among university students has been emphasized, and how to improve the quality of IAE education and promote the overall development of university students has become a key concern of university teachers. Therefore, the design and implementation of the IAE project management system should be explored in the study, starting from the IAE education of university students. The thesis addresses the current situation and characteristics of IAE development of university students and develops a WEB system applicable to the project management of IAE for university students at this stage. Cluster analysis is a mathematical statistical method for grouping and categorising similar data. The design of a classification system based on university innovation and entrepreneurship data proposes a new definition of outlier index for outliers detected in real time, combined with the concept of CBLOF(t). On this basis, the system also implements the monitoring function for abnormal data, thus serving as system optimisation and fault warning. The system implements the main functions such as login, registration, project release, project declaration, and management.


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## Body

## 1. Introduction

To date, the majority of universities do not have a system for the declaration and management of innovative projects [1] for the students, and the declaration of projects mainly relies on manual registration and approval by special personnel, which is inefficient and not easy to manage. Therefore, it is urgent to develop an efficient and stable project declaration system [2]. The development of an innovative project declaration system will gradually move from the original single and simple to diversified and intelligent. The future innovation project declaration system for university students will be an efficient and stable information management system built on the basis of the Internet and supported by a database [3].An analysis of the IAE project practice reveals that it plays a key role in cultivating the innovation, entrepreneurship, and practical ability of university students [4] and that after China proposed to implement IAE education for university students in the field of higher education, many schools have made corresponding practical exploration in this regard [5], creating idealized conditions for the cultivation of IAE ability of university students. However, it must be clearly noted that in order to give play to the important role of innovative and entrepreneurial educational practices of students [6], universities must promote the improvement of the technical level of educational practices with the help of scientific management. It is therefore essential to analyse the project management aspects of student IAE to ensure that scientific management can free student managers from the tedium of management, improve management efficiency and level, and strive to provide diversified services to the student community [7–9].The IAE management system can create certain convenience for the management, and the analysis of the management system shows that the IAE project management platform system for students is mainly an information management system for university students to participate in IAE projects [10]. The system is designed with B/S architecture and MVC model, which can provide support for students to participate in IAE project management and implement information audit and management of relevant information, which has strong practical significance for the management of university students’ IAE projects at present [11]. Therefore, it is necessary to strengthen the exploration of the management platform system in view of the basic situation of the management of the IAE projects of college students in China, so as to the platform system and promote the stable development of the IAE practice activities of college students.The focus of university students’ IAE project management is to analyse the current problems on the basis of a comprehensive understanding of the project management characteristics and then to clarify the problems that exist in it. The work is then combined with different business requirements to complete UML modelling and determine scientific and reasonable measures for authority control [12]. In general, the comprehensive promotion of university students’ IAE project management can enhance the adaptability of university students to society, cultivate their entrepreneurial ability, and cultivate high-level entrepreneurial development talents for the country [13].A systematic study of the construction of the IAE system for students reveals that the B/S architecture is introduced in the design and planning of the system, which can meet the needs of project management and promote the realization of diversified functions of the system [14]. With the support of the system, assessment experts and relevant instructors can have a comprehensive grasp of the project’s operation and provide effective guidance, as well as facilitate students’ access to their own IAE practices [15]. In planning the management platform system, it is necessary to promote the convenience of operation, but also to have the stability of functions, to ensure the accuracy and safety of data, so in the design and planning of the system, it must be analysed from multiple perspectives, to improve the management system, to develop the information backup and statistical analysis functions with the support of modern tools, and to ensure that it can be combined with the participation of students in IAE. The system should be designed and planned from multiple perspectives, and the management system should be improved [16], and the information backup and statistical analysis functions should be developed with the support of modern tools, so as to ensure the effective education and management of university students in the context of their participation in IAE education, to provide a corresponding platform for university students to participate in IAE education and practice, and to ensure that the basic work can be done well and the comprehensive development of university students’ IAE education can be improved. In this way, reform and innovation of IAE education for university students can be carried out [17], and the comprehensive work level will be improved comprehensively, which will play a good role in promoting the implementation of IAE education for university students. Based on the theory of cluster analysis, this paper adopts the weighted K-means algorithm based on Euclidean distance, establishes a clustering model by analyzing a large amount of normal historical data, and then, according to the actual operation, successively proposes the definition of the outlier index of parameter points within the cluster and the definition of the outlier index of abnormal parameter points outside the cluster and proposes a university innovation and entrepreneurship project management system based on this model.


## 2. Analysis of Basic System Requirements

According to the characteristics of the management of entrepreneurship for the Department of Innovation [18, 19], there are four roles involved in the system. The main functions are shown in Figure 1.Figure 1
Functional diagram of system roles.The system adopts an object-oriented approach [20], abandoning the traditional approach the development, instead of making full use database design tables based on the Java code corresponding to the entities written and the mapping files of the entity objects. This approach reduces the need to write cumbersome SQL statements to build the tables directly. The use of reverse engineering to generate the corresponding database tables from the entity objects’ Javabean and their corresponding mapping files (xml files) [21] reduces data redundancy.The supervisor provides guidance to the student leader and analyses the student leader’s project in the context of the actual situation and provides appropriate guidance. In general, the supervisor has the function of reviewing the project declaration, the midterm check, and the closing of the project. The EMS is based on the real needs of the development and management of student IAE projects. The system is used to ensure that students and teachers are involved in the process of each project, to guarantee the effective implementation of student entrepreneurship projects, to give full play to the important role of student IAE projects, and to promote the overall improvement of the quality of personnel training.


### 2.1. Entity Object Design

Based on a comprehensive analysis of the entity objects required by the system and their relationships, eight entity objects were identified, these are the administrator entity, the project application entity, the project applicant entity, the project reviewer entity, the new project entity, the old project entity, the project issuer entity, and the project executor entity.In the proposed management system, there are 26 database tables, for the project declaration, and project evaluation modules are the core of the system, and there is a close relationship between project declaration and project evaluation. In the study, a variety of different database tables are discussed, and in this study, the identity information table and the project information table are selected for specific analysis. The administrator is able to keep up-to-date information about the student innovation and entrepreneurship projects in the management system. In general, their main job responsibility is to follow the realistic needs of student entrepreneurship, the output of the declaration plan and the evaluation plan, the information on the projects, etc., on the platform, thus ensuring that a scientific evaluation plan is developed. It is the focus centre of the whole system and can exchange information on the front page module [22]. The relationship between the various entities is shown in Figure 2.Figure 2
Interentity correlations.


## 2.1. Entity Object Design

Based on a comprehensive analysis of the entity objects required by the system and their relationships, eight entity objects were identified, these are the administrator entity, the project application entity, the project applicant entity, the project reviewer entity, the new project entity, the old project entity, the project issuer entity, and the project executor entity.In the proposed management system, there are 26 database tables, for the project declaration, and project evaluation modules are the core of the system, and there is a close relationship between project declaration and project evaluation. In the study, a variety of different database tables are discussed, and in this study, the identity information table and the project information table are selected for specific analysis. The administrator is able to keep up-to-date information about the student innovation and entrepreneurship projects in the management system. In general, their main job responsibility is to follow the realistic needs of student entrepreneurship, the output of the declaration plan and the evaluation plan, the information on the projects, etc., on the platform, thus ensuring that a scientific evaluation plan is developed. It is the focus centre of the whole system and can exchange information on the front page module [22]. The relationship between the various entities is shown in Figure 2.Figure 2
Interentity correlations.


## 3. Cluster Analysis Theory

The so-called cluster analysis algorithm works on the principle that for all vectors in anm-dimensional space R, the algorithm clusters each vector and achieves a category of vectors by minimising the distance between each vector and the centre of the cluster [23, 24]. That is, the greater the intraclass correlation, the better, and the smaller the interclass correlation, the better. As an unguided learning problem, the purpose of clustering is to obtain some intrinsic data pattern by dividing the original set of objects into similar groups or clusters.


### 3.1. Introduction to Clustering Algorithms

There are many kinds of clustering algorithms, such as hierarchy-based, division-based, and density-based algorithms [25]. In this paper, the most common and reliable division-based K-means algorithm is used for analysis. The K-means algorithm divides n vectors xii=1,2,⋯,n into k classes Gii=1,2,⋯,k and find the cluster centres of each class so that the objective function of the nonsimilarity (or distance) index is minimized. When the metric between the vector xl and the corresponding cluster centre Ci in the i-th class Gi is chosen as the Euclidean distance, the objective function can be defined as
(1)J=∑ki=1Ji=∑ki=1∑l,xl∈Gixl−ci2.Here,Ji=∑l,xl∈Gixl−ci2 is the objective function Gi within the class, the apparent J size of which depends on the Gi shape of the cluster centre ci, and the J smaller the cluster, the better the clustering.The basic idea of the K-means algorithm is as follows.(1)
First,k vectors are randomly selected as the centres of each class(2)
LetU be a 2-dimensional subordination matrix of c∗n. If the jrd vector xj belongs to class i, then the element uij in matrix U is 1; otherwise, it is 0; i.e.,(2)(3)
Baseduij on the value of the calculated objective function, stop if it is less than a threshold or if the difference between two consecutive times is less than a threshold(4)
uij Calculate the centre Gi of each cluster according to Ci:ci=∑l,xl∈Gixl/Gi, Gibeing the number of elements in the cluster. Then, return to (2)The system entity object to database table relationship analysis and implementation entity object to relational table implementation process is shown in Figure3.Figure 3
Generating data tables using control reversal reverse engineering.


### 3.2. Weighted Clustering Algorithms

The K-means algorithm has its limitations in that it takes into account all factors in the clustering analysis and assumes that these factors have an equal effect on distance. Relying equally on similarity measures for all attributes can be misleading [26, 27]; one effective way of overcoming this dimensionality trap can be achieved by assigning weights to each attribute, so that different attributes can achieve optimal results in the clustering process based on their own weights. In terms of the Euclidean space from a data perspective, adding weights is equivalent to lengthening the axes corresponding to the attributes of interest and shortening the axes corresponding to the attributes with smaller weights.To this end, the objective function should bexl−ci2 replaced by ∑ki=1wjxlj−cij2 an objective function that J also depends on the weights wj, the size of which can be determined by experienced staff using expert scoring methods to determine the importance of the equipment parameters for production optimisation and fault warning, thus solving the “dimensional trap” problem.


### 3.3. Concepts and Definitions of Outlier Indices

To date, there is no formal and generally accepted definition of an outlier. More recent findings are given in the relevant literature [28, 29]. To first give an example to illustrate this basic idea, consider the two-dimensional data set in Figure 1, where it can be found that there are four cluster centres, of which C1 and C3 are outliers. Since outliers are defined as data that are not inside any large cluster, here clusters C2 and C4 are to have the vast majority of the data and C1 and C3 are not part of clusters C2 and C4, so they are outliers. In order to show the physical characteristics of outlier data, an index is designed for each identified outlier data object, called CBLOFt, some concepts are given below, and the absolute value of C is used to indicate the number of data points in C.Definition 1.
LetA1,⋯,Am denote a series of data points whose value domain is D1,⋯,Dm such that the result of clustering the database D is denotedC=C1,C2,⋯,Ck; here, Ci∩Cj≠Φ and C1∪C2∪⋯∪Ck=D, by the number of clusters k.Definition 2.
SupposeC=C1,C2,⋯,Ck is the order of the clusters: C1≥C2≥⋯≥Ck, given two numerical parameters: αand β; we let b be the boundary of the size cluster, then
(3)C1+C2+⋯+Cb≥D⋅α,Cb/Cb+1≥β.The first part of equation (3) takes into account that the vast majority of data points in a data set are not outliers, so that a cluster that contains the vast majority of data points is a large cluster. The second part of equation (3) takes into account that the main difference between large and small clusters is the number of data points within the cluster, e.g., set β to 5, and then, any cluster in LC is 5 times larger than in SC [30].Definition 3.
Putting the above analysis together, assuming thatC=C1,C2,⋯,Ck is the order of clustering: C1≥C2≥⋯≥Ck, and consistent in the definitions of pijt, α, β, b, LC, and SC and setting the number of clusters in LC to n, the resulting outlier index for any data point t is
(4)CBLOFt=Ci⋅mindistancet,Cj,
wheret∈Ci,Ci∈SC,Cj∈LC,j=1tob.According to Definition3, the outlier index of a data point is determined by the size of the cluster in which it is located and the distance of this data point from the edge of its nearest large cluster.


## 3.1. Introduction to Clustering Algorithms

There are many kinds of clustering algorithms, such as hierarchy-based, division-based, and density-based algorithms [25]. In this paper, the most common and reliable division-based K-means algorithm is used for analysis. The K-means algorithm divides n vectors xii=1,2,⋯,n into k classes Gii=1,2,⋯,k and find the cluster centres of each class so that the objective function of the nonsimilarity (or distance) index is minimized. When the metric between the vector xl and the corresponding cluster centre Ci in the i-th class Gi is chosen as the Euclidean distance, the objective function can be defined as
(1)J=∑ki=1Ji=∑ki=1∑l,xl∈Gixl−ci2.Here,Ji=∑l,xl∈Gixl−ci2 is the objective function Gi within the class, the apparent J size of which depends on the Gi shape of the cluster centre ci, and the J smaller the cluster, the better the clustering.The basic idea of the K-means algorithm is as follows.(1)
First,k vectors are randomly selected as the centres of each class(2)
LetU be a 2-dimensional subordination matrix of c∗n. If the jrd vector xj belongs to class i, then the element uij in matrix U is 1; otherwise, it is 0; i.e.,(2)(3)
Baseduij on the value of the calculated objective function, stop if it is less than a threshold or if the difference between two consecutive times is less than a threshold(4)
uij Calculate the centre Gi of each cluster according to Ci:ci=∑l,xl∈Gixl/Gi, Gibeing the number of elements in the cluster. Then, return to (2)The system entity object to database table relationship analysis and implementation entity object to relational table implementation process is shown in Figure3.Figure 3
Generating data tables using control reversal reverse engineering.


## 3.2. Weighted Clustering Algorithms

The K-means algorithm has its limitations in that it takes into account all factors in the clustering analysis and assumes that these factors have an equal effect on distance. Relying equally on similarity measures for all attributes can be misleading [26, 27]; one effective way of overcoming this dimensionality trap can be achieved by assigning weights to each attribute, so that different attributes can achieve optimal results in the clustering process based on their own weights. In terms of the Euclidean space from a data perspective, adding weights is equivalent to lengthening the axes corresponding to the attributes of interest and shortening the axes corresponding to the attributes with smaller weights.To this end, the objective function should bexl−ci2 replaced by ∑ki=1wjxlj−cij2 an objective function that J also depends on the weights wj, the size of which can be determined by experienced staff using expert scoring methods to determine the importance of the equipment parameters for production optimisation and fault warning, thus solving the “dimensional trap” problem.


## 3.3. Concepts and Definitions of Outlier Indices

To date, there is no formal and generally accepted definition of an outlier. More recent findings are given in the relevant literature [28, 29]. To first give an example to illustrate this basic idea, consider the two-dimensional data set in Figure 1, where it can be found that there are four cluster centres, of which C1 and C3 are outliers. Since outliers are defined as data that are not inside any large cluster, here clusters C2 and C4 are to have the vast majority of the data and C1 and C3 are not part of clusters C2 and C4, so they are outliers. In order to show the physical characteristics of outlier data, an index is designed for each identified outlier data object, called CBLOFt, some concepts are given below, and the absolute value of C is used to indicate the number of data points in C.Definition 1.
LetA1,⋯,Am denote a series of data points whose value domain is D1,⋯,Dm such that the result of clustering the database D is denotedC=C1,C2,⋯,Ck; here, Ci∩Cj≠Φ and C1∪C2∪⋯∪Ck=D, by the number of clusters k.Definition 2.
SupposeC=C1,C2,⋯,Ck is the order of the clusters: C1≥C2≥⋯≥Ck, given two numerical parameters: αand β; we let b be the boundary of the size cluster, then
(3)C1+C2+⋯+Cb≥D⋅α,Cb/Cb+1≥β.The first part of equation (3) takes into account that the vast majority of data points in a data set are not outliers, so that a cluster that contains the vast majority of data points is a large cluster. The second part of equation (3) takes into account that the main difference between large and small clusters is the number of data points within the cluster, e.g., set β to 5, and then, any cluster in LC is 5 times larger than in SC [30].Definition 3.
Putting the above analysis together, assuming thatC=C1,C2,⋯,Ck is the order of clustering: C1≥C2≥⋯≥Ck, and consistent in the definitions of pijt, α, β, b, LC, and SC and setting the number of clusters in LC to n, the resulting outlier index for any data point t is
(4)CBLOFt=Ci⋅mindistancet,Cj,
wheret∈Ci,Ci∈SC,Cj∈LC,j=1tob.According to Definition3, the outlier index of a data point is determined by the size of the cluster in which it is located and the distance of this data point from the edge of its nearest large cluster.


## 4. System Design and Implementation

The process of using clustering models to guide production optimisation has been implemented in the previous work. The basic process is as follows: the clustering model is trained with a large amount of historical data of normal operation, and then, the corresponding actual data of the field operation is collected and put into the clusters of aggregation; if it is not included in any of the clusters, it means that it is an abnormality, and an alarm should be prompted to the staff to check the equipment, which serves as an early warning function. The detected anomalies belonging to any of the clusters mean that the equipment is operating normally and can be further explored in order to optimise the equipment.


### 4.1. Raw Data Preprocessing

Since the units of the parameters used in the design of the system vary considerably between orders of magnitude, the data must first be processed to make them dimensionless, and the standardised metric used in this paper is(5)xif=zif−hfSf,where zif is an actual parameter, hf is the mean zif of n, and Sf is the mean standard deviation.
(6)Sf=1n∑ni=1zif−hf.


### 4.2. Analysis of the Off-Kernel Indices of the Parameter Points within Clusters

Using IAE at the Shanghai Jiaotong University as the application context, five years of relevant actual production data were collected, the data were processed with standardised metric values and stored in a database, and the corresponding parameters were trained to form a clustering model using a weighted K-mean algorithm. The similarity of the actual parameters within the clusters to the cluster centres was then analysed.The similarity between the other parameters within the cluster and the central parameter is calculated using the correlation coefficient method and the Euclidean distance, respectively; the Euclidean distance has been described earlier, and the following equations are used for the correlation coefficient method.(7)rij=∑mk=1xik−xi¯xjk−xj¯∑mk=1xik−xi¯2×∑mk=1xjk−xj¯2,where xi¯=1/m∑mk=1xik∑mk=1xikandxj¯=1/m∑mk=1xjk. When xj is the cluster centre, rij is the similarity measure between the other parameters and the centre parameter. A total of 20,000 points were collected, and all data were clustered into 4 clusters; the results of which are shown in Table 1.Table 1
Comparison of the parameters of the 4 clusters.
Serial numberCentral valueRadiusPercentCluster 10.52,0.630.320.62Cluster 20.17.0.210.060.04Cluster 30.21,0.560.170.21Cluster 40.32,0.410.230.13A pie chart of the radii and percentages of the four clusters is given in Figure4.Figure 4
Comparison of pie charts of radii and percentages of clusters.
(a)
Radius(b)
PercentThen, the ratio of the number of equipment parameters to the number of parameters in the whole cluster was obtained for different correlation coefficients, and the ratio of the number of parameters in this point to the number of parameters in the whole cluster was obtained for different ratios of the distance from the parameter point to the centre of the cluster to the maximum radius, and the ratio was obtained in Figure2(b). From Figure 2(a), it can be seen that the correlation with the central parameter is greater than 0.8 in more than 90% of the cases, while the proportion below 0.6 is less than 3%. Similar results are obtained for the other equipment parameters; the same analysis is also reflected in Figure 2(b). Examining these four clusters, the data points within 20% of the maximum radius each already account for more than 50% of all data points. This indicates that the equipment has been operating in a performance optimised condition for a long time and that once the equipment parameters have deviated from the optimised condition, the staff immediately call back to bring them back to the optimised condition, so that a large proportion of the parameters are close to the centre of the cluster and a small proportion are far from the centre. The above results show that the magnitude of the dispersion of these data is related not only to the similarity of this point to the central point but also to the proportion of the points occupied, such that the kernel dispersion index of the parameter points within the cluster can be defined as
(8)SCAFt=rt,Cjp/Cj,1−dt,Cj/rjp/Cj,where p represents the number of device parameter points at point t.The curve of where the data points are located and the error detection parameters in the system at this time is shown in Figure5. Cluster 1 represents a normal and good operating condition of the system; cluster 2 is an overadjusted state with a low error rate and a good system; clusters 3 and 4 are the result of the system not being retuned in time, and clusters 3 and 4 also show that the marginal values (points with large dispersion) of the clusters are of poor error quality.Figure 5
Ratio of the number of parameter points to the total number of clusters at different degrees of similarity.
(a)
Similarity factor(b)
Maximum radius ratioWith the above analysis, after training the clustering model with a large amount of historical data of normal operation, when monitoring the operating conditions of the equipment in real time, when a real-time parameter point is within a cluster, this model can not only indicate in which cluster it is but also know the off-kernel index of the parameter point.


### 4.3. Analysis of Outlier Indices for Anomalous Parameter Points Outside Clusters

When the monitored real-time parameter point was outside the cluster, it was an anomaly. In order to be able to analyse the extent of the outliers of the anomaly, the outlier indexCBLOFt was first calculated using equation (3), and the propane content of the dry gas at this point was checked. As shown in Table 2, the outlier index 1 for the outlier point was larger than for the outlier point 2, but the propane content was not high at this time. The analysis shows that if outlier a is closer to a cluster with fewer parameter points and further away from a cluster with more and outlier b is closer to a cluster with more parameter points and further away from a cluster with fewer, CBLOFb>CBLOFa is calculated, but in reality, outlier a may be more outlier than outlier b. The outlier index of an outlier is related not only to the distance of this outlier from the nearest cluster but also to the distance between this cluster and other clusters and the number of clusters within it. Also taking into account the weighted nature of clustering, a new definition of outliers for anomalous parameter points is obtained.
(9)NCFt=∑kj=1cjN⋅mindistancet,cj.Table 2
Relationship between anomalous parameter points and clustering models.
Exceptional parameter pointsCluster 1Cluster 2Cluster 3Cluster 410.650.510.750.5820.620.490.690.6130.590.410.710.5640.630.450.720.52When a parameter point is not in a cluster, a new outlier index can be used to analyse it, which gives insight into the system operation and provides a basis for monitoring the system operation in an optimised state.Point pairs of anomalous parameters for four of the clustering models are shown in Figure6.Figure 6
Comparison effect of anomalous parameter points for the four clustering models.Two sets of experiments were conducted using cluster analysis theory to cluster 20,000 data points from four clusters, and the detailed data distribution of cluster accuracy for the four clusters is given in Table3.Table 3
Clustering accuracy data for the four clusters.
CategoryCluster 1Cluster 2Cluster 3Cluster 4Experiment 189.5695.8693.4285.32Experiment 290.2495.3294.2184.68The comparative effect of the clustering accuracy data for the four clusters in the two sets of experiments is given in Figure7.Figure 7
Comparison of clustering accuracy data for the four clusters in the two sets of experiments.


## 4.1. Raw Data Preprocessing

Since the units of the parameters used in the design of the system vary considerably between orders of magnitude, the data must first be processed to make them dimensionless, and the standardised metric used in this paper is(5)xif=zif−hfSf,where zif is an actual parameter, hf is the mean zif of n, and Sf is the mean standard deviation.
(6)Sf=1n∑ni=1zif−hf.


## 4.2. Analysis of the Off-Kernel Indices of the Parameter Points within Clusters

Using IAE at the Shanghai Jiaotong University as the application context, five years of relevant actual production data were collected, the data were processed with standardised metric values and stored in a database, and the corresponding parameters were trained to form a clustering model using a weighted K-mean algorithm. The similarity of the actual parameters within the clusters to the cluster centres was then analysed.The similarity between the other parameters within the cluster and the central parameter is calculated using the correlation coefficient method and the Euclidean distance, respectively; the Euclidean distance has been described earlier, and the following equations are used for the correlation coefficient method.(7)rij=∑mk=1xik−xi¯xjk−xj¯∑mk=1xik−xi¯2×∑mk=1xjk−xj¯2,where xi¯=1/m∑mk=1xik∑mk=1xikandxj¯=1/m∑mk=1xjk. When xj is the cluster centre, rij is the similarity measure between the other parameters and the centre parameter. A total of 20,000 points were collected, and all data were clustered into 4 clusters; the results of which are shown in Table 1.Table 1
Comparison of the parameters of the 4 clusters.
Serial numberCentral valueRadiusPercentCluster 10.52,0.630.320.62Cluster 20.17.0.210.060.04Cluster 30.21,0.560.170.21Cluster 40.32,0.410.230.13A pie chart of the radii and percentages of the four clusters is given in Figure4.Figure 4
Comparison of pie charts of radii and percentages of clusters.
(a)
Radius(b)
PercentThen, the ratio of the number of equipment parameters to the number of parameters in the whole cluster was obtained for different correlation coefficients, and the ratio of the number of parameters in this point to the number of parameters in the whole cluster was obtained for different ratios of the distance from the parameter point to the centre of the cluster to the maximum radius, and the ratio was obtained in Figure2(b). From Figure 2(a), it can be seen that the correlation with the central parameter is greater than 0.8 in more than 90% of the cases, while the proportion below 0.6 is less than 3%. Similar results are obtained for the other equipment parameters; the same analysis is also reflected in Figure 2(b). Examining these four clusters, the data points within 20% of the maximum radius each already account for more than 50% of all data points. This indicates that the equipment has been operating in a performance optimised condition for a long time and that once the equipment parameters have deviated from the optimised condition, the staff immediately call back to bring them back to the optimised condition, so that a large proportion of the parameters are close to the centre of the cluster and a small proportion are far from the centre. The above results show that the magnitude of the dispersion of these data is related not only to the similarity of this point to the central point but also to the proportion of the points occupied, such that the kernel dispersion index of the parameter points within the cluster can be defined as
(8)SCAFt=rt,Cjp/Cj,1−dt,Cj/rjp/Cj,where p represents the number of device parameter points at point t.The curve of where the data points are located and the error detection parameters in the system at this time is shown in Figure5. Cluster 1 represents a normal and good operating condition of the system; cluster 2 is an overadjusted state with a low error rate and a good system; clusters 3 and 4 are the result of the system not being retuned in time, and clusters 3 and 4 also show that the marginal values (points with large dispersion) of the clusters are of poor error quality.Figure 5
Ratio of the number of parameter points to the total number of clusters at different degrees of similarity.
(a)
Similarity factor(b)
Maximum radius ratioWith the above analysis, after training the clustering model with a large amount of historical data of normal operation, when monitoring the operating conditions of the equipment in real time, when a real-time parameter point is within a cluster, this model can not only indicate in which cluster it is but also know the off-kernel index of the parameter point.


## 4.3. Analysis of Outlier Indices for Anomalous Parameter Points Outside Clusters

When the monitored real-time parameter point was outside the cluster, it was an anomaly. In order to be able to analyse the extent of the outliers of the anomaly, the outlier indexCBLOFt was first calculated using equation (3), and the propane content of the dry gas at this point was checked. As shown in Table 2, the outlier index 1 for the outlier point was larger than for the outlier point 2, but the propane content was not high at this time. The analysis shows that if outlier a is closer to a cluster with fewer parameter points and further away from a cluster with more and outlier b is closer to a cluster with more parameter points and further away from a cluster with fewer, CBLOFb>CBLOFa is calculated, but in reality, outlier a may be more outlier than outlier b. The outlier index of an outlier is related not only to the distance of this outlier from the nearest cluster but also to the distance between this cluster and other clusters and the number of clusters within it. Also taking into account the weighted nature of clustering, a new definition of outliers for anomalous parameter points is obtained.
(9)NCFt=∑kj=1cjN⋅mindistancet,cj.Table 2
Relationship between anomalous parameter points and clustering models.
Exceptional parameter pointsCluster 1Cluster 2Cluster 3Cluster 410.650.510.750.5820.620.490.690.6130.590.410.710.5640.630.450.720.52When a parameter point is not in a cluster, a new outlier index can be used to analyse it, which gives insight into the system operation and provides a basis for monitoring the system operation in an optimised state.Point pairs of anomalous parameters for four of the clustering models are shown in Figure6.Figure 6
Comparison effect of anomalous parameter points for the four clustering models.Two sets of experiments were conducted using cluster analysis theory to cluster 20,000 data points from four clusters, and the detailed data distribution of cluster accuracy for the four clusters is given in Table3.Table 3
Clustering accuracy data for the four clusters.
CategoryCluster 1Cluster 2Cluster 3Cluster 4Experiment 189.5695.8693.4285.32Experiment 290.2495.3294.2184.68The comparative effect of the clustering accuracy data for the four clusters in the two sets of experiments is given in Figure7.Figure 7
Comparison of clustering accuracy data for the four clusters in the two sets of experiments.


## 5. Conclusion

The design and implementation of the IAE project management system is mainly based on the real demand for the development and management of university students’ IAE projects, to achieve coordinated management with the power of the system, thus ensuring that the teacher and student groups can participate in each project process, reducing the management workload and improving the comprehensive effect of management. Cluster analysis itself is a method of grouping data based on a certain similarity measure, and this paper adopts the weighted K-means algorithm based on the Euclidean distance to establish a clustering model by analysing a large amount of normal historical data and then, according to the actual operation, successively proposes the definition of the off-kernel index of parameter points within clusters and the definition of anomalous parameter points outside clusters. The definition of the outlier index of the parameter points within the cluster and the definition of the outlier index of the abnormal parameter points outside the cluster were then proposed according to the actual operation. The management system proposed in this paper can monitor the historical data of the system running in an optimised state, thus enabling the effective implementation of the innovation and entrepreneurship project, but due to the many uncontrollable factors in the actual operation, further development of the system is necessary to make the innovation and entrepreneurship project management system practically useful.



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*Source: 4865716-2022-05-09.xml*