CN112115536B - College teaching building plane learning space connection cohesiveness assessment method and system - Google Patents
College teaching building plane learning space connection cohesiveness assessment method and system Download PDFInfo
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Abstract
The invention relates to a college teaching building plane learning space connection cohesiveness assessment method and system, comprising the following steps: s1, acquiring and simplifying a college teaching building plane technical drawing, performing convex space division, performing space region classification coding, establishing connection between space regions, and generating a learning space topology network model; s2, evaluating connection tightness characteristics of the learning space traffic network and the whole network system; and S3, evaluating the connection control characteristics of the learning space traffic network and the whole network system. The beneficial effects of the invention are as follows: the invention evaluates 3 performance characteristics of compactness, controllability and robustness of learning space connection based on complex network analysis, establishes an accurate evaluation method of teaching building plane space connection cohesiveness based on data and evidence-based centering on flow, and breaks through more intelligent and experience-based intelligent analysis means.
Description
Technical Field
The invention relates to the technical field of space network optimization analysis of building design professions, in particular to an evaluation method and system for the connectivity of a learning space of a building plane in colleges and universities.
Background
Contemporary higher education is faced with a tremendous revolution: for the last 20 years, learning and exploration can occur anywhere due to the widespread use of electronic resources and mobile devices; college students as the network generation prefer an active learning mode in a learning mode and participate in the learning mode actively; learning theory such as cognitive construction theory gradually replaces information processing theory, and it is emphasized that learners learn through modes such as active construction, social interaction and social participation; the current learning paradigm not only changes the formal learning in the studio from passive to active, but also makes the informal learning outside the studio another important role of interest. The extensive application of information technology, the characteristic change of a learning main body, the replacement development of a learning theory, the transition fusion of a learning paradigm and the importance of people on informal learning provide serious challenges for the building planning design of the existing college learning space. How to utilize learning space to promote social interaction of learners, thereby promoting active construction and the like is a focus of attention in the field of architectural design.
Users in the learning space of colleges and universities essentially undergo a non-spatially linked learning process in which participants are spatially located in the learning space. There is an action framework in the above process: space material capital is converted into location capital, which in turn creates social capital, thereby achieving learning performance and healthy well-being. Where location capital is a key element, it is determined by the functional distribution and organization of the learning space. The university learning space is composed of a formal learning space and an informal learning space. The informal learning space (e.g., a study space, an exhibition space, a rest space, etc.) has characteristics of lines and nets, compared to the point characteristics possessed by the formal learning space (e.g., a classroom, a laboratory, etc.). The whole spatial network can generate the emerging effect which is not possessed by a single space under the influence of the network effect. Therefore, it is more important to study the entire learning space network structure than to study how individual learning spaces function in isolation. Learning spatial join condensation is an important characterization of zone capital. The learning space connection cohesiveness refers to the degree to which all learning spaces within a educational building are linked together by walking connection. Therefore, the assessment method of the connectivity of the learning space can provide powerful tools for design push and scheme comparison of teaching buildings in colleges and universities, increase the chance of meeting and interaction of learners, and promote the improvement of learning performance and health and welfare of the learners in the teaching building.
In the present building design process of university teaching building, the following shortcomings exist in the structure analysis of the functional arrangement and the structural relationship of the learning space: first, more aesthetic judgments than scientific rationality assessment: more form-centric morphological analysis perspectives based on inspiration and experience, and lack of stream-centric network analysis perspectives based on data and evidence-based. Secondly, the knowledge of the overall connectivity and cohesiveness of the learning space is insufficient: the effect and effect of individual learning spaces are more isolated, and the performance and comparison of the whole learning space network structure are ignored. Third, the lack of tools to quantitatively evaluate learning spatial junction cohesiveness: the connection is a necessary condition for achieving the cohesive force, but the density of the connection is often not a decisive factor, and the structural mode of the connection is more important. At present, an effective mode for describing the learning space function and the connection structure of the teaching building is not formed, performance analysis on the learning space connection cohesiveness is not formed, a scientific quantification method is not formed to measure the comprehensive performance of the learning space connection cohesiveness, and deep structural characteristics of the learning space connection cannot be revealed.
In contemporary higher educational environments, supporting entity learning space is a strategic opportunity to improve student learning performance and health and well-being. Therefore, there is an urgent need for a scientific and efficient assessment method and system for learning spatial connectivity in the building design process.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an evaluation method and an evaluation system for the connectivity of a plane learning space of a college teaching building.
The college teaching building plane learning space connection cohesiveness assessment system, as shown in fig. 2, comprises a plane drawing processing and network model generating module, a connection compactness characteristic assessment module of a learning space network system, a connection control characteristic assessment module of the learning space network system, a connection robustness characteristic assessment module of the learning space network system, an assessment report and comparison suggestion module.
The plane drawing processing and network model generating module is used for acquiring and simplifying a building plane technical drawing, carrying out convex space division, space region classification coding and connection establishment, and generating a learning space network model; the connection closeness characteristic evaluation module of the learning space network system is used for analyzing the network basic characteristics, the network centrality characteristics and the network structure characteristics of the learning space traffic network and the whole network system and evaluating the connection closeness characteristics; the connection control characteristic evaluation module of the learning space network system is used for analyzing the network basic characteristics, the network center characteristics and the network structure characteristics of the learning space traffic network and the whole network system and evaluating the connection control characteristics; the connection robustness characteristic evaluation module of the learning space network system is used for analyzing the network basic characteristics, the network center characteristics and the network structure characteristics of the learning space traffic network and the whole network system and evaluating the connection robustness characteristics; the evaluation report and comparison suggestion module is used for forming a connection cohesiveness characteristic evaluation report of the learning space traffic network system and the whole network system and providing comparison and modification suggestions.
The evaluation method of the college teaching building plane learning space connection cohesiveness evaluation system comprises the following steps:
s1, acquiring and simplifying a college teaching building plane technical drawing, performing convex space division, performing space region classification coding, establishing connection between space regions, and generating a learning space topology network model;
s2, evaluating connection tightness characteristics of the learning space traffic network and the whole network system;
s3, evaluating connection control characteristics of the learning space traffic network and the whole network system;
s4, evaluating the connection stability characteristics of the learning space traffic network and the whole network system;
s5, comprehensively evaluating the connectivity of the learning space traffic network and the whole network system to form an evaluation report, and providing comparison and modification suggestions; based on the evaluation result of the connection cohesiveness characteristic, the layout and connection conditions of space areas such as a channel area, a shared area and the like in the learning space are adjusted, the network structure of the traffic system and the K-core structure of the whole network are improved, the connection compactness and the control performance of the learning space network system are optimized, and finally the improvement of the connection cohesiveness of the learning space is realized.
Preferably, the step S1 specifically includes the following steps:
s1.1, simplifying and drawing the acquired college teaching building plane drawings according to floor arrangement, and removing all movable arrangements in a public streamline area and a room, as shown in FIG. 4 a;
s1.2, convex space division is carried out: the method comprises the steps that a convex space with the largest area and the smallest quantity is used for covering the whole college teaching building area, the convex spaces are not covered, and the whole plane space is converted into a system consisting of the convex spaces; combining on this basis partly immediately adjacent and not affecting the selective area space (e.g. niche space in adjacently located men and women's classrooms and classrooms etc.), facilitates a later stage simplification of the network, as shown in fig. 4 b; the definition of convex space is: assuming that the space inside a plane can be seen mutually between any two points, the space is a convex space;
s1.3, performing space region classification coding: dividing the space region types of all entity spaces in the college teaching building into a learning region type and an auxiliary region type according to the characteristics of formal learning and informal learning places; performing unique value coding on all convex spaces in the plane space according to the space region type of the entity space;
S1.4, establishing connection between space regions; based on the reachability principle, the space region coding units are regarded as nodes of the learning space topology network, the walking reachability relation among the space region coding units is regarded as the side of the learning space topology network, and the learning space topology network model of the teaching building of the layer is constructed, as shown in fig. 4 c.
Preferably, the step S2 specifically includes the following steps:
s2.1, evaluating connection compactness characteristics of a learning space traffic network;
s2.1.1 converting a layer to be evaluated learning space traffic network (T network) into an adjacent matrix model (T model) by using complex network drawing software, and calculating network basic characteristics (network density, average shortcut distance, distance closeness and distance separation) and network centrality characteristics (degree central potential and near central potential) of the adjacent matrix model (T model) of the learning space traffic network by using complex network analysis software; comparing network basic characteristics and centrality characteristic index values of the learning space traffic networks of different layers to be evaluated;
s2.1.2, carrying out characteristic evaluation on the network structure of the learning space traffic network on the basis of the step S2.1.1: extracting a K-core structure model (TK model) from an adjacent matrix model (T model) of the learning space traffic network, and calculating a global centrality index (degree central potential and approximate central potential) of the K-core structure model of the learning space traffic network; comparing the difference of K-core structure models (TK models) of different layers to be evaluated on the global centrality (the larger the global centrality index value is, the stronger the compactness of the K-core structure is shown); the definition of the K-core is: if all points in a sub-graph are connected to at least the other K points in the sub-graph, then such sub-graph is referred to as a K-core;
S2.2, evaluating connection compactness characteristics of the whole learning space network;
s2.2.1 converting the learning space integral network (W network) of the layer to be evaluated into an adjacent matrix model (W model) by using complex network drawing software, and calculating network basic characteristics (network density, average shortest distance, distance closeness and distance separation) and network centrality characteristics (degree central potential, near central potential and near central degree) of the adjacent matrix model (W model) of the learning space integral network by using complex network analysis software; comparing network basic characteristics and centrality characteristic index values of the whole network of the learning space of different layers to be evaluated, and simultaneously comparing distribution differences of the approximate centrality in the whole plane;
s2.2.2, evaluating the network structure characteristics of the whole learning space network on the basis of the step S2.2.1: extracting a K-core structure model (WK model) from an adjacent matrix model (W model) of the learning space integral network, and calculating a global centrality index (degree central potential and approximate central potential) of the K-core structure model of the learning space integral network; and comparing the differences of the K-core structural models (WK models) of the learning space overall networks of different layers to be evaluated on the global centrality.
Preferably, the step S3 specifically includes the following steps:
s3.1, evaluating connection control characteristics of the learning space traffic network;
s3.1.1 calculating network basic characteristics (average punctuation) and network centrality characteristics (middle centrality and network flow centrality) of a learning space traffic network adjacency matrix model (T model) by using complex network analysis software, and then comparing network basic characteristics and network centrality characteristic values of different layers to be evaluated;
s3.1.2, network structural feature evaluation is performed on the basis of step S3.1.1: extracting a K-core structure model (TK model) from an adjacent matrix model (T model) of the learning space traffic network, and calculating a global centrality index (middle central potential and network flow central potential) of the K-core structure model of the learning space traffic network; comparing the difference of the K-core structure models (TK models) of the learning space traffic networks of different layers to be evaluated on the global centrality (the larger the global centrality index value is, the stronger the control of the K-core structure is shown);
s3.2, evaluating connection control characteristics of the whole learning space network;
s3.2.1 calculating network basic characteristics (average dot degree) and network centrality characteristics (middle centrality, network flow centrality and middle centrality) of a learning space overall network adjacency matrix model (W model) by using complex network analysis software; comparing network basic characteristics and centrality characteristic index values of different layers to be evaluated, and simultaneously comparing distribution differences of the central degree of the middle of the layers to be evaluated in the whole college teaching building plane;
S3.2.2, network structural feature evaluation is performed on the basis of step S3.2.1: and extracting a K-core structure model (WK model) from an adjacent matrix model (W model) of the learning space integral network, calculating global centrality indexes (intermediate centrality and network flow centrality) of the K-core structure model (WK model), and comparing differences of the K-core structure models (WK model) of the learning space integral network of different layers to be evaluated on the global centrality.
Preferably, the step S4 specifically includes the following steps:
s4.1, evaluating connection robustness characteristics of the learning space traffic network:
s4.1.1, calculating the clustering coefficient and transitivity of a learning space traffic network adjacency matrix model (T model) by using complex network analysis software;
s4.1.2, evaluating network structural characteristics on the basis of the step S4.1.1: analyzing the K-core structure of the learning space traffic network, identifying the type and the scale of the K-core, analyzing the Lambda set structure of the K-core, generating a maximum flow matrix, and obtaining an edge association index from the maximum flow matrix; the greater the value of the side association between two points, the more robust the two-point relationship; the more the levels of the maximum flow matrix of the learning space traffic network are, the larger the maximum value of the side association degree value is, and the stronger the cohesiveness of the system is; the Lambda set is defined as: any pair of points in the subset is more than any point in the subset and any point outside the subset, and the subset is a Lambda set; the side association is the minimum number of lines that must be removed from the graph so that there is no path between the two points;
S4.2, evaluating connection robustness characteristics of the whole learning space network:
s4.2.1 calculating network basic characteristics of a learning space overall network adjacency matrix model (W model) by using complex network analysis software, wherein the network basic characteristics comprise clustering coefficients and transitivity;
s4.2.2, network structural feature assessment is performed on the basis of step S4.2.1: analyzing the K-core structure of the learning space integral network, identifying the type and the scale of the K-core, analyzing the Lambda set structure of the K-core, generating a maximum flow matrix, and obtaining an edge association index from the maximum flow matrix; the greater the value of the side association between two points, the more robust the two-point relationship; the more levels of the maximum traffic matrix of the network, the greater the edge correlation value maximum, the more cohesive the system.
Preferably, the step S5 specifically includes the following steps:
s5.1, if the compactness of the traffic network of the learning space and the overall network system is insufficient, increasing the path connection among the learning space, the traffic space and the auxiliary space, increasing the network density and reducing the average shortest distance; optimizing the traffic shortcut design, increasing the relation between the important space and other spaces, and increasing the degree center potential and the near center potential;
S5.2, if the controllability of the learning space traffic network and the whole network system is insufficient, the relation between the central space and other functional spaces is increased, and the average click degree is increased; the space level is increased, a multi-center organization mode is promoted, and the center potential of the middle center and the center potential of the network flow are promoted;
s5.3, if the robustness of the traffic network of the learning space and the whole network system is insufficient, improving the traffic network of the learning space, increasing the path connection, improving the K-core type of the traffic network and increasing the number of nodes contained in the K-core; increasing the maximum flow matrix level of the K-core and increasing the maximum value of the side association degree; optimizing the space setting of the learning function, reasonably setting a shared learning area, forming more annular connection networks with the existing traffic network, increasing the maximum flow matrix level of the whole network K-core, and increasing the maximum value of the side association degree.
Preferably, the teaching building plane technical drawing in the step S1 is a building professional drawing manufactured by a building design unit, and is a dwg format graphic file, a vectorization graphic file or a pixelized graphic file.
Preferably, the learning region type in step S1.3 includes: general teaching areas, specialized teaching areas, gathering areas, sharing areas, outdoor areas, and access areas; the general teaching area comprises a common classroom, a ladder classroom, a reporting hall and the like, the special teaching area comprises a professional classroom, an experimental classroom and the like, the gathering area comprises a project space, a group space, a study space and the like, the sharing area comprises a demonstration space, an exhibition space, a rest space, a practice space and the like, the outdoor area comprises an outdoor space, and the channel area comprises a traffic space, a talking and exhibiting place; the auxiliary area types include: through-high areas (overhead portions, not visible), texter areas (teacher office, conference room, etc.), service areas (resource service space, toilet, etc.), vertical traffic (stairs, elevators, etc.), and other areas (transformer and distribution room, piping, etc.).
The beneficial effects of the invention are as follows:
1) The invention evaluates 3 performance characteristics of compactness, controllability and robustness of learning space connection based on complex network analysis, establishes an accurate evaluation method of teaching building plane space connection cohesiveness based on data and evidence-based centering on flow, and breaks through more intelligent and experience-based intelligent analysis means.
2) The invention classifies and divides various areas of formal and informal learning space, codes unique values, and effectively expresses the connection relation between the spaces, thereby being beneficial to clearly showing the function distribution and the organization relation of the learning space and laying an analysis foundation for further evaluation of space connection performance and other performances.
3) According to the invention, all the spaces possibly generating learning behaviors in the teaching building are analyzed based on the overall view angle, the influence of space connection and organization structures on behaviors of learners is emphasized, the analysis and understanding of the learning space connection condensation performance from the system level are facilitated, the design of the contemporary university teaching building learning space which is more suitable for the wide application of the current information technology, the characteristic change of a learning main body and the replacement development of a learning theory and the transition fusion of the learning paradigm is facilitated.
4) The importance of each learning space in the overall system connection cohesiveness is evaluated based on the local view angle, so that the space connection performance and playing roles of each learning space can be mastered, and further building function layout and important space design optimization can be facilitated.
5) The invention analyzes the deep structure characteristics of the traffic network and the whole network system of the learning space respectively, reveals the core structure, the core nodes and the key connection paths in the network and the hierarchical influence mechanism of the connection cohesiveness of the traffic system on the connection cohesiveness of the whole system, is beneficial to more essentially knowing the internal mechanism formed by the connection cohesiveness of the learning space network, and is more effective and convenient for comparing, selecting and optimizing the architectural design scheme.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a system block diagram of the present invention;
FIG. 3 is an exploded view of learning spatial join condensation performance;
FIG. 4 is a schematic diagram of a building plan technical drawing process and a network model generation;
fig. 5 is a schematic diagram of a learning space overall network model of case 1 and case 2;
FIG. 6 is a schematic diagram showing a comparison of K-core network structures of learning spatial traffic networks for case 1 and case 2;
Fig. 7 is a graph showing a comparison of the distribution of intermediate centroids and near centroids of the learning space global networks of case 1 and case 2;
FIG. 8 is a schematic diagram showing a comparison of K-core network structures of the learning space overall networks of case 1 and case 2;
FIG. 9 is a schematic diagram of a comparison of Lambda set maximum flow matrices for K-core network structures of learning spatial traffic networks for case 1 and case 2;
FIG. 10 is a schematic diagram of a comparison of Lambda set maximum flow matrices for the K-core network structure of the learning space overall network of case 1 and case 2;
FIG. 11 is a schematic diagram showing a summary of the connection tightness and control portion indicators of the learning space traffic networks of case 1 and case 2;
FIG. 12 is a schematic diagram showing a summary of partial indicators of connection compactness and controllability of the entire learning space network of case 1 and case 2;
fig. 13 is a network topology model of a teaching building learning space traffic system constructed by case 1 and case 2;
fig. 14 is a topological model of the whole network system of the teaching building learning space constructed by the case 1 and the case 2.
Detailed Description
The invention is further described below with reference to examples. The following examples are presented only to aid in the understanding of the invention. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present invention without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
As shown in fig. 3, the invention can deeply analyze the performance of learning space connection cohesiveness from three aspects of connection compactness, connection controllability and connection robustness respectively aiming at 2 levels of a traffic network and an overall network of a case to be evaluated, and solve the problems of more artistic-oriented perceptual judgment, lack of quantitative evaluation tools and incapability of revealing deep structural features of learning space connection in the prior art. A flow chart of the method of the present invention is shown in fig. 1.
As an example:
1. the acquired building plan is organized according to floors to simplify the drawing, and all movable arrangements in the public streamline area and the room are removed, as shown in fig. 4 a. The method is characterized in that a convex space dividing method is used for converting the whole plane space into a system consisting of convex spaces, wherein the dividing principle is that the convex spaces with the largest area and the smallest quantity cover the whole area, and the convex spaces do not cover each other; on this basis, partial immediately adjacent and non-selective area spaces (e.g. adjacently located men and women's classrooms and niche spaces in the classrooms, etc.) are merged, facilitating a later stage simplification of the network, as shown in fig. 4 b. The concept of convex space is to assume that the space inside a plane can be seen mutually between any two points, and then the space is the convex space. All physical spaces within the teaching building are divided into 11 spatial region types according to formal and informal study site characteristics, as shown in table 1 below:
Table 1 college teaching building space region type table
Wherein the learning area types are 6 and the auxiliary area types are 5. All convex spaces in a plane are uniquely value coded according to the type of the space region to which they belong. Based on the reachability principle, the space region coding units are regarded as nodes of the learning space topology network, the walking reachability relation among the space region coding units is regarded as the side of the learning space topology network, and the layer teaching building space topology network model is constructed, as shown in fig. 4c and 5.
2. And performing connection compactness characteristic evaluation of the learning space traffic network and the whole network system.
Connection compactness characteristic evaluation of traffic network: the traffic network (T-network) of the layer learning space to be evaluated is converted into an adjacency matrix model (T-model) using complex network drawing software, and the corresponding index is calculated using complex network analysis software, as shown in table 2 below. The basic characteristics of the network to be calculated by the T model are 4 indexes: network density, average shortest distance, distance closeness, distance separation. The network centrality characteristics to be calculated by the T model are 2 indexes: the degree center potential, near center potential, is shown in table 3. And then, comparing the network basic characteristics and the centrality characteristic index values of the layers to be evaluated of different comparison cases. And (3) carrying out network structure characteristic evaluation on the basis: the K-core structure model (TK model) is extracted from the traffic network adjacency matrix model (T model) of the learning space, as shown in FIG. 6. Calculating 2 global centrality indexes of the TK model: the degree center potential and the near center potential are compared with the difference of the comparison case on the global centrality. The greater the above 2 global centrality index values, the stronger the compactness of the K-core structure. The definition of a K-core is that if all points in a sub-graph are connected to at least the other K points in the sub-graph, such a sub-graph is referred to as a K-core.
Connection compactness characteristics evaluation of the overall network: the overall network (W-network) of the layer learning space to be evaluated is converted into an adjacency matrix model (W-model) using complex network drawing software, and the corresponding index is calculated using complex network analysis software, as shown in table 2 below. The basic characteristics of the network to be calculated by the W model are 4 indexes: network density, average shortest distance, distance closeness, distance separation. The network centrality characteristics of the W model to be calculated are 3 indexes: the degree center potential, near center potential, and near center index are shown in table 3. Then, the network basic characteristic and the centrality characteristic index values of the layers to be evaluated of different comparison cases are compared, and the distribution difference of the approximate centrality in the whole plane is compared, as shown in fig. 7. And (3) carrying out network structure characteristic evaluation on the basis: the K-kernel structure model (WK model) is extracted from the whole network adjacency matrix model (W model) of the learning space, as shown in fig. 8. Calculating 2 global centrality indexes of the WK model: the degree center potential and the near center potential are compared with the difference of the comparison case on the global centrality.
TABLE 2 learning index correspondence of spatial connected coacervation characteristics
3. And (3) learning connection control characteristic evaluation of the space traffic network and the whole network system.
Connection control feature evaluation of traffic network: the complex network analysis software is used to calculate the corresponding index of the learning space traffic network adjacency matrix model (T model). The basic characteristics of the network to be calculated by the T model are 1 index: average dot degree. The network centrality characteristics to be calculated by the T model are 2 indexes: intermediate central potential, network flow central potential. And then, comparing the network basic characteristics and the centrality characteristic index values of the layers to be evaluated of different comparison cases. And (3) carrying out network structure characteristic evaluation on the basis: 2 global centrality indexes of a T model K-core (TK model) are calculated: the central potential of the middle and the central potential of the network flow are compared with the difference of the comparison case on the global centrality. The greater the above 2 global centrality index values, the greater the controllability of the K-core structure.
Connection control feature evaluation of the overall network: the complex network analysis software is used to calculate the corresponding index of the learning space overall network adjacency matrix model (W model). The basic characteristics of the network to be calculated of the W model are 1 index: average dot degree. The network centrality characteristics to be calculated for the W model are 3 indexes: intermediate centrality, network flow centrality and intermediate centrality index. Then, the network basic feature core centrality characteristic index values of the layers to be evaluated of different comparison cases are compared, and meanwhile, the distribution difference of the centrality among the comparison cases in the whole plane is compared, as shown in fig. 7. And (3) carrying out network structure characteristic evaluation on the basis: 2 global centrality indices of the W model K-kernel (WK model) are calculated: the middle center potential and the network flow center potential are compared with the differences between the selection cases;
4. Connection robustness feature assessment of learning spatial traffic networks and overall network systems:
connection robustness feature evaluation of traffic network: the complex network analysis software is used to calculate the corresponding index of the learning space traffic network adjacency matrix model (T model). The basic characteristics of the network to be calculated by the T model are 2 indexes: clustering coefficients and transitivity. And (3) carrying out network structure characteristic evaluation on the basis: analyzing the K-core structure of the learning space traffic network, identifying the type and the scale of the K-core, analyzing the Lambda set structure of the K-core, generating a maximum flow matrix, and obtaining an edge relevance index from the maximum flow matrix, wherein the complex network tool can calculate the edge relevance index, such as UCINET software: reading the side association degree on the generated maximum flow matrix; as shown in fig. 9. The greater the value of the edge association between two points, the more robust the two-point relationship. The more levels of the maximum traffic matrix of the network, the greater the edge correlation value maximum, the more cohesive the system. The Lambda set is defined as a subset itself having a greater number of independent paths for any pair of points than any point within the subset and any point outside the subset, which may be referred to as a Lambda set. Edge association refers to the minimum number of lines that must be removed from the graph in order for there to be no path between two points.
Connection robustness feature evaluation of the overall network: the complex network analysis software is used to calculate the corresponding index of the learning space overall network adjacency matrix model (W model). The basic characteristics of the network to be calculated by the W model are 2 indexes: clustering coefficients and transitivity. And (3) carrying out network structure characteristic evaluation on the basis: analyzing the K-core structure of the learning space integral network, identifying the type and the scale of the K-core, analyzing the Lambda set structure of the K-core, generating a maximum flow matrix, and obtaining an edge association index from the maximum flow matrix, as shown in fig. 10. The greater the value of the edge association between two points, the more robust the two-point relationship. The more levels of the maximum traffic matrix of the network, the greater the edge correlation value maximum, the more cohesive the system.
The meaning and calculation formula of the main calculation index of the invention are shown in the following table 3:
TABLE 3 meanings and formulas of main calculation indexes
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5. Implementation case:
case 1: college teaching building with 5 floors is selected, wherein the number of floors to be tested is 3, and the area of the floors to be tested (without the through height) is 3889m 2 The method comprises the steps of carrying out a first treatment on the surface of the The teaching building topology network model is shown in fig. 5 a; the constructed teaching building learning space traffic system network topology model T1 is shown in FIG. 13a, and has 9 nodes in total and 13 connection numbers; as shown in FIG. 14a, the constructed building learning space overall network system topology model W1 has 62 nodes in total and the connection number is 76;
Case 2: college teaching building with 6 floors is selected, wherein the number of floors to be tested is 4, and the floor area to be tested (without the through height) is 3836m 2 The method comprises the steps of carrying out a first treatment on the surface of the The teaching building topology network model is shown in fig. 5 b; the constructed teaching building learning space traffic system network topology model T2 is shown in FIG. 13b, and has 10 nodes in total and 12 connection numbers; building learning space integral network system constructedAs shown in fig. 14b, the topology model W1 has 41 nodes in total, and the connection number is 45;
the connection compactness evaluation results of the learning space traffic networks of case 1 and case 2 are shown in the following table 4; the connection compactness evaluation results of the learning space overall network of case 1 and case 2 are shown in table 5 below; a comparison of the distribution of the intermediate centroids and the near centroids of the learning space overall networks of case 1 and case 2 is shown in fig. 7; the results of evaluation of connectivity control of the learning space traffic networks of case 1 and case 2 are shown in table 6 below; the connection control evaluation results of the learning space overall network of case 1 and case 2 are shown in table 7 below;
table 4 results table of connection affinity assessment of learning spatial traffic networks for case 1 and case 2
Table 5 results table of connection affinity evaluation of learning space overall network of case 1 and case 2
Table 6 results table of connection control evaluation of learning spatial traffic network for case 1 and case 2
Table 7 results table of evaluation of connection control of learning space overall network of case 1 and case 2
The maximum flow matrix of Lambda set of the learning space traffic network system of case 1 and case 2 is shown in figure 9; the connection robustness assessment results of the learning spatial traffic networks for case 1 and case 2 are as follows in table 8:
table 8 connection robustness assessment results table for learning spatial traffic network
The connection robustness evaluation results of the learning space overall network of the case 1 and the case 2 are shown in the following table 9, and a K-core network structure comparison schematic diagram of the learning space overall network is shown in fig. 8; the Lambda set maximum flow matrix of the K core of the learning space overall network system is shown in figure 10;
table 9 results of connection robustness assessment of learning space overall network for case 1 and case 2
The traffic system performance summary for case 1 and case 2 is shown in fig. 11, and the overall system performance summary for case 1 and case 2 is shown in fig. 12.
Summarizing:
1. traffic network connection compactness
The floors of the two cases (case 1 and case 2) have similar building areas, and the number of nodes and the number of connections of the traffic flow line network are very similar. Comparison finds that: the network density of the case 1 traffic network is higher than that of case 2 by 35.40%; the average shortcut distance between any two points is shorter, 15.77% less than case 2; the distance compactness index is higher, and the distance separation degree is lower; the value of the case 1 traffic network is far higher than that of the case 2 on the two index degree central potential and the near central potential of the network central characteristic; the advantages of the case 1 traffic network are further expanded in the degree center potential and the near center potential of the two index K kernels of the network structural features. In summary, the traffic network of case 1 is superior to case 2 in terms of connection compactness.
2. Overall network connection compactness
The two case (case 1 and case 2) floors have a large difference in the number of nodes and connections of the overall spatial network, case 1 being 51.22% more nodes and 68.89% more connections than case 2. This shows that case 1 has a smaller average area of the spatial area, resulting in more nodes and connections. Case 1 overall network has 26.78% lower network density than case 2; but the average shortcut distance is shorter, 6.90% less than case 2; the compactness based on the distance is larger, and the separation degree is smaller. The value of the whole network of case 1 is higher than that of case 2 on the central potential and the near central potential of two index degrees of the central characteristic of the network; the advantages of the case 1 traffic network are further expanded in the degree center potential and the near center potential of the two index K kernels of the network structural features.
Meanwhile, among the nodes of the case 1 whole network, the highest value of the approximate centrality index standard value is 52.586 (1 Co-04 node) which is higher than the highest value 47.059 (4 Co-04 node) of the case 2 node; the mean is also slightly higher than case 2 (34.091 and 31.626), indicating that the connection between the nodes of the overall network of case 1 is more tight.
To sum up, case 1's overall network is also superior to case 2 in terms of connection compactness.
3. Traffic network connection control
The number of nodes and the number of connections of the two case traffic streamline networks are very close, but the average point degree of the case 1 traffic streamline network is larger and 20.38% higher than that of the case 2, which indicates that part of the nodes play a stronger role in the junction. The value of the case 1 traffic network is higher than that of the case 2 on the central potential of the middle of the two indexes of the network centrality characteristic and the central potential of the network flow; the advantages of the case 1 traffic network are further expanded in the central potential of the two index K kernels of the network structural features and in the central potential of the network flow. In summary, the traffic network of case 1 is superior to case 2 in connection controllability.
4. Overall network connection control
The average degree of the overall network of case 1 is higher by 11.71% than that of case 2, indicating that part of the nodes play a stronger pivotal role. The value of the whole network of case 1 is higher than that of case 2 on the central potential of the middle of two indexes of the network central characteristic and the central potential of the network flow; the advantages of the case 1 overall network are further expanded in the intermediate central potential and the network flow central potential of the two index K kernels of the network structural features.
Meanwhile, the highest standard value of the intermediate centrality index in each node of the case 1 whole network is 65.355 (1 Co-04 node) which is far higher than the highest value 48.205 (4 Co-04 node) in case 2; however, the median of the center of each node in case 1 is 3.368, which is smaller than the average 5.757 of case 2, and the overall center potential is far greater than that of case 2, which may be caused by that case 1 has a higher maximum value, but the total number of nodes is larger, and the node with the value of 0 has a larger ratio. This indicates that the centrality among the nodes of case 1 is more diverse, and that individual nodes have very strong control.
In summary, the overall traffic network of case 1 is also superior to case 2 in connection control.
5. Traffic network connection robustness
Case 1 traffic network has 1 2-core of 9 nodes; case 2 traffic network has 1 2-core made up of 8 nodes. The K-core Lambda set maximum edge association for case 1 traffic network is 5, the maximum traffic matrix has 3 levels: the set member with the side association degree of 5 comprises 1Co-03 and 1Co-04 points; the set members with the edge association degree of 3 and above comprise 1Co-03, 1Co-04 and 1Co-01 points; the whole structure forms a set with the side association degree of 2 or more; whereas the K-core Lambda set of the case 2 traffic network has a maximum edge association of 3, the maximum traffic matrix has 2 levels. In summary, case 1 traffic network is superior to case 2 in terms of connection robustness.
6. Overall network connection robustness
The K-core of the case 1 whole network is 1 2-core composed of 17 nodes, 9 are channel area nodes, 8 are shared area nodes; case 2 the K-core of the overall network is 1 2-core made up of 9 nodes, 8 of which are channel area nodes and 1 of which are shared area nodes. Case 1 the K-core Lambda set maximum edge association for the overall network is 8, the maximum traffic matrix has 4 levels: the set member with the side association degree of 8 comprises 1Co-03 and 1Co-04 points; the set members with the edge association degree of 7 and above comprise 1Co-03, 1Co-04 and 1Co-01 points; the set members with the side association degree of 3 and above comprise 10 nodes, and a set with the side association degree of 2 and above is integrally formed; whereas the Lambda set maximum edge association of the K-core of the case 2 overall network is 3, the maximum traffic matrix has 2 levels. Overall, the overall network of case 1 is also superior to case 2 in terms of connection robustness.
Combining the performance characteristics of the two cases in the aspects of traffic network and overall network 2 layers, connection compactness, connection control and connection robustness 3, the connection cohesiveness of case 1 is superior to that of case 1.
Claims (7)
1. The method is characterized in that the college teaching building plane learning space connection condensation evaluation system comprises a plane drawing processing and network model generating module, a connection compactness characteristic evaluation module of a learning space network system, a connection control characteristic evaluation module of the learning space network system, a connection robustness characteristic evaluation module of the learning space network system, an evaluation report and comparison suggestion module; the method comprises the following steps:
s1, acquiring and simplifying a college teaching building plane technical drawing, performing convex space division, performing space region classification coding, establishing connection between space regions, and generating a learning space topology network model;
s2, evaluating connection tightness characteristics of the learning space traffic network and the whole network system;
s3, evaluating connection control characteristics of the learning space traffic network and the whole network system;
S4, evaluating the connection stability characteristics of the learning space traffic network and the whole network system;
s5, comprehensively evaluating the connectivity of the learning space traffic network and the whole network system to form an evaluation report, and providing comparison and modification suggestions;
the step S1 specifically comprises the following steps:
s1.1, sorting, simplifying and drawing the acquired college teaching building plane drawings according to floors, and removing all movable arrangements in a public streamline area and a room;
s1.2, convex space division is carried out: the method comprises the steps that a convex space with the largest area and the smallest quantity is used for covering the whole college teaching building area, the convex spaces are not covered, and the whole plane space is converted into a system consisting of the convex spaces; merging the partial area spaces on the basis; the definition of convex space is: assuming that the space inside a plane can be seen mutually between any two points, the space is a convex space;
s1.3, performing space region classification coding: dividing the space region types of all entity spaces in the college teaching building into a learning region type and an auxiliary region type according to the characteristics of formal learning and informal learning places; performing unique value coding on all convex spaces in the plane space according to the space region type of the entity space;
S1.4, establishing connection between space regions; based on the reachability principle, the space region coding units are regarded as nodes of the learning space topology network, the walking reachability relation among the space region coding units is regarded as the side of the learning space topology network, and the learning space topology network model of the teaching building of the layer is constructed.
2. The method for evaluating the college teaching building plane learning space connection cohesiveness evaluating system according to claim 1, wherein the step S2 specifically comprises the steps of:
s2.1, evaluating connection compactness characteristics of a learning space traffic network;
s2.1.1 converting a layer to be evaluated learning space traffic network into an adjacent matrix model by using complex network drawing software, and calculating network basic characteristics and network centrality characteristics of the adjacent matrix model of the learning space traffic network by using complex network analysis software, wherein the network basic characteristics comprise network density, average shortest distance, distance closeness and distance separation degree, and the network centrality characteristics comprise degree central potential and near central potential; comparing network basic characteristics and centrality characteristic index values of the learning space traffic networks of different layers to be evaluated;
S2.1.2, carrying out characteristic evaluation on the network structure of the learning space traffic network on the basis of the step S2.1.1: extracting a K-core structure model from an adjacent matrix model of the learning space traffic network, and calculating a global centrality index of the K-core structure model of the learning space traffic network; the global centrality index comprises the degree central potential of a K-core of a learning space traffic network and the near central potential of the K-core; comparing differences of the K-core structural models of the learning space traffic networks of different layers to be evaluated on the global centrality; the definition of the K-core is: if all points in a sub-graph are connected to at least the other K points in the sub-graph, then such sub-graph is referred to as a K-core;
s2.2, evaluating connection compactness characteristics of the whole learning space network;
s2.2.1 converting the whole learning space network of the layer to be evaluated into an adjacent matrix model by using complex network drawing software, and calculating network basic characteristics and network centrality characteristics of the adjacent matrix model of the whole learning space network by using complex network analysis software, wherein the network basic characteristics comprise network density, average shortest distance, distance closeness and distance separation degree; the network centrality features include a degree centrality, a near centrality and a near centrality; comparing network basic characteristics and centrality characteristic index values of the whole network of the learning space of different layers to be evaluated, and simultaneously comparing distribution differences of the approximate centrality in the whole plane;
S2.2.2, evaluating the network structure characteristics of the whole learning space network on the basis of the step S2.2.1: extracting a K-core structure model from an adjacent matrix model of the whole learning space network, and calculating a global centrality index of the K-core structure model of the whole learning space network; the global centrality index comprises the degree central potential of a K-core and the near central potential of the K-core of the whole learning space network; and comparing the differences of the K-core structural models of the learning space overall networks of different layers to be evaluated on the global centrality.
3. The method for evaluating the college teaching building plane learning space connection cohesiveness evaluating system according to claim 1, wherein the step S3 specifically comprises the steps of:
s3.1, evaluating connection control characteristics of the learning space traffic network;
s3.1.1 calculating network basic characteristics and network centrality characteristics of the learning space traffic network adjacency matrix model by using complex network analysis software, wherein the network basic characteristics are average punctuation, the network centrality characteristics comprise middle centrality potentials and network flow centrality potentials, and then comparing network basic characteristics and network centrality characteristic values of different layers to be evaluated;
S3.1.2, network structural feature evaluation is performed on the basis of step S3.1.1: extracting a K-core structure model from an adjacent matrix model of the learning space traffic network, and calculating a global centrality index of the K-core structure model of the learning space traffic network; the global centrality index comprises the intermediate centrality of the K-core of the learning space traffic network and the network flow centrality of the K-core; comparing differences of the K-core structural models of the learning space traffic networks of different layers to be evaluated on the global centrality;
s3.2, evaluating connection control characteristics of the whole learning space network;
s3.2.1 calculating network basic characteristics and network centrality characteristics of the learning space overall network adjacency matrix model by using complex network analysis software, wherein the network basic characteristics comprise average dot degrees, and the network centrality characteristics comprise middle central potential, network flow central potential and middle centrality; comparing network basic characteristics and centrality characteristic index values of different layers to be evaluated, and simultaneously comparing distribution differences of the central degree of the middle of the layers to be evaluated in the whole college teaching building plane;
s3.2.2, network structural feature evaluation is performed on the basis of step S3.2.1: extracting a K-core structure model from an adjacent matrix model of the whole learning space network, and calculating a global centrality index of the K-core structure model, wherein the global centrality index comprises an intermediate central potential of the K-core and a network flow central potential of the K-core of the whole learning space network; and comparing the differences of the K-core structural models of the learning space overall networks of different layers to be evaluated on the global centrality.
4. The method for evaluating the college teaching building plane learning space connection cohesiveness evaluating system according to claim 1, wherein the step S4 specifically comprises the steps of:
s4.1, evaluating connection robustness characteristics of the learning space traffic network:
s4.1.1, calculating network basic characteristics of a learning space traffic network adjacency matrix model by using complex network analysis software, wherein the network basic characteristics comprise clustering coefficients and transitivity;
s4.1.2, evaluating network structural characteristics on the basis of the step S4.1.1: analyzing the K-core structure of the learning space traffic network, identifying the type and the scale of the K-core, analyzing the Lambda set structure of the K-core, generating a maximum flow matrix, and obtaining an edge association index from the maximum flow matrix; the Lambda set is defined as: any pair of points in the subset is more than any point in the subset and any point outside the subset, and the subset is a Lambda set; the side association is the minimum number of lines that must be removed from the graph so that there is no path between the two points;
s4.2, evaluating connection robustness characteristics of the whole learning space network:
S4.2.1 calculating network basic characteristics of the learning space overall network adjacency matrix model by using complex network analysis software, wherein the network basic characteristics comprise clustering coefficients and transitivity;
s4.2.2, network structural feature evaluation is performed on the basis of step S4.2.1: analyzing the K-core structure of the learning space integral network, identifying the type and the scale of the K-core, analyzing the Lambda set structure of the K-core, generating a maximum flow matrix, and obtaining an edge association index from the maximum flow matrix.
5. The method for evaluating the college teaching building plane learning space connection condensation evaluation system according to claim 1, wherein the step S5 specifically comprises the following steps:
s5.1, if the compactness of the traffic network of the learning space and the overall network system is insufficient, increasing the path connection among the learning space, the traffic space and the auxiliary space, increasing the network density and reducing the average shortest distance; optimizing the traffic shortcut design, increasing the relation between the important space and other spaces, and increasing the degree center potential and the near center potential;
s5.2, if the controllability of the learning space traffic network and the whole network system is insufficient, the relation between the central space and other functional spaces is increased, and the average click degree is increased; the space level is increased, a multi-center organization mode is promoted, and the center potential of the middle center and the center potential of the network flow are promoted;
S5.3, if the robustness of the traffic network of the learning space and the whole network system is insufficient, improving the traffic network of the learning space, increasing the path connection, improving the K-core type of the traffic network and increasing the number of nodes contained in the K-core; increasing the maximum flow matrix level of the K-core and increasing the maximum value of the side association degree; optimizing the space setting of the learning function, reasonably setting a shared learning area, forming more annular connection networks with the existing traffic network, increasing the maximum flow matrix level of the whole network K-core, and increasing the maximum value of the side association degree.
6. The method for evaluating the college teaching building plane learning space connection cohesiveness evaluating system according to claim 1, wherein: the teaching building plane technical drawing in the step S1 is a building professional drawing manufactured by a building design unit, and is a dwg format graphic file, a vectorization graphic file or a pixelized graphic file.
7. The method for evaluating the college teaching building plane learning space connection cohesiveness evaluation system according to claim 1, wherein the learning region type in step S1.3 includes: general teaching areas, specialized teaching areas, gathering areas, sharing areas, outdoor areas, and access areas; the auxiliary area types include: high pass areas, textman areas, service areas, vertical traffic and other areas.
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