Method and system for performing decision-making on outdoor environment of residential area on basis of landscape approach motivation

Abstract

The present invention relates to the technical field of quality and efficiency improvement of built environments. Disclosed are a method and system for performing decision-making on the outdoor environment of a residential area on the basis of a landscape approach motivation. The method comprises: collecting engineering drawing data and environment element material test data of the outdoor environment of a residential area, and translating an outdoor-environment element test result of the residential area into landscape approach motivation structure information; delineating landscape nodes in the outdoor environment of the residential area, constructing an initial decision-making matrix of approach motivation change states of environment elements corresponding to the landscape nodes, and decomposing the landscape approach motivation change states into evolutionary states and similar states; geographically mapping position and change state data of the landscape nodes, constructing a distribution adjacency network decision-making matrix on the basis of the landscape approach motivation, and calculating the driving degree of a distribution adjacency network of the outdoor environment of the residential area; and distinguishing between grade patterns of the landscape approach motivation change states, generating a decision-making network for the outdoor environment of the residential area, and determining the priorities for the renewal and governance of the outdoor environment of the residential area.

Description

Residential outdoor environment decision-making method and system based on landscape approach motivation TECHNICAL FIELD

[0001] The present application relates to the technical field of built environment quality improvement, in particular to a residential outdoor environment decision-making method and system based on landscape approach motivation. BACKGROUND

[0002] Landscape approach motivation is one of the main factors affecting the use of residential outdoor environment, and plays a decisive role in the behavior and attitude of residents. The lack of psychological tendency and behavior driving force leads to low use rate and satisfaction of residential landscape environment. Landscape approach motivation specifically represents a "approach-avoidance" cognitive response, which reflects the intuitive decision of attraction and repulsion of environmental elements. Related research shows that the environmental governance decision-making process guided by landscape approach motivation emphasizes data-driven and integrated evaluation to adapt to complex and changing environmental conditions, so as to maximize the satisfaction of residents' expectations and needs. Objective and rational brain cognitive data analysis and decision-making can help technology developers, environmental designers, residential area managers and others to accurately and real-time capture landscape environment characteristics, and provide basic data supply for fine residential outdoor environment renewal governance.

[0003] At present, the analysis methods combining landscape elements and cognitive evaluation are: "Landscape Element Cognitive Evaluation Based on Point Heat Analysis (2022)", which is about landscape preference decision-making method of real scene photo shooting, element point touch rate calculation and heat aggregation statistics; "Emotion Characteristics and Influencing Factors of Urban Park Users (2021)", which is about landscape emotion cognitive path method of park emotion questionnaire survey, structural equation model building and factor influence discrimination; "Comparison of Landscape Preference and Landscape Cognition of Urban Park Users (2020)", which is about landscape cognition evaluation method of landscape preference questionnaire survey, element quadrat combination analysis and park cognitive map generation. When dealing with cognitive data, related methods often rely on users' self-reporting, which is easily affected by memory bias and personal interpretation, affecting the accuracy and reliability of cognitive data analysis. At the same time, these methods are usually static, and cannot fully grasp the spatial flow and temporal evolution of landscape environment and its composition, thereby limiting the application effectiveness of their cognitive decision-making.

[0004] The prior art has: a community public space updating design method through node information collection, daily stop analysis, social network generation and space group comparison (application number: CN202010088995.7); a residential space network construction method through service range acquisition, residential area classification and adjacency matrix relationship analysis (application number: CN202310898451.0); a public space optimization method through material and behavior network model building, "village-district-single point" hierarchical analysis (application number: CN202310061699.1) and the like. Although such technical methods have developed, there are still certain limitations: insufficient attention is paid to resident cognitive feedback information in the outdoor environment of the residential area; the hierarchical scale and analysis accuracy of the outdoor environment elements of the residential area need to be improved; the network decision-making function integration from environmental cognition to environmental change characteristics is low, which makes it difficult for the existing methods to provide efficient guidance for the outdoor environment updating and management of the residential area.

[0005] SUMMARY

[0006] To solve the problems mentioned in the background art, the purpose of the present application is to provide a residential outdoor environment decision-making method and system based on landscape approach motivation

[0007] In the first aspect, the purpose of the present application can be achieved by the following technical solution: a residential outdoor environment decision-making method based on landscape approach motivation, the method comprising the following steps:

[0008] Obtaining engineering drawing data and material test data of the outdoor environment of the residential area, and performing landscape approach motivation inspection on the material test data of the outdoor environment of the residential area to obtain environment element data that passes the inspection, wherein the engineering drawing data of the outdoor environment of the residential area includes outdoor environment plane vector data and GPS positioning data of the residential area, and the material test data includes standard image data of environment elements of the outdoor environment of the residential area;

[0009] Based on the engineering drawing data and the environment element data that passes the inspection of the outdoor environment of the residential area, landscape nodes in the outdoor environment of the residential area are delineated, and the evolution and similar change state of the landscape nodes are determined according to the landscape approach motivation information of the corresponding environment elements of the landscape nodes in the outdoor environment of the residential area;

[0010] Based on the GPS position and change state of the landscape nodes in the outdoor environment of the residential area, an initial decision-making matrix of the landscape nodes and their corresponding environment elements is generated, the discrete variables of the GPS position and change state of the landscape nodes in the outdoor environment of the residential area are encoded, and a distribution adjacency network of the landscape nodes is constructed based on the initial decision-making matrix of the landscape approach motivation of the landscape nodes;

[0011] The distribution adjacency network of the landscape nodes is subjected to environment hierarchical decision-making processing to obtain a residential outdoor environment decision-making result.

[0012] With reference to the first aspect, in some implementations of the first aspect, the method further includes: a process of testing the material test data of the outdoor environment of the residential area according to the landscape access motivation;

[0013] Idle state debugging, Stroop effect test and random playing of residential outdoor environment element standard images;

[0014] Wherein, the Stroop effect test result is subjected to hypothesis test, the fitted landscape access motivation evaluation index is obtained, the fitted landscape access motivation evaluation index is normalized, and finally the environment element data that passes the test is obtained.

[0015] With reference to the first aspect, in some implementations of the first aspect, the method further includes: a formula for normalizing the fitted landscape access motivation evaluation index:

[0016] Wherein, represents the normalized landscape access motivation evaluation index value of the i th residential outdoor environment element in the t th period; represents the original landscape access motivation evaluation index value of the i th environment element in the t th period; represents the mean value of the original landscape access motivation evaluation index of the i th environment element in the t th period; The standard deviation of the original landscape access motivation evaluation index of the i th environment element in the t th period.

[0017] With reference to the first aspect, in some implementations of the first aspect, the method further includes: a process of generating a landscape access motivation initial decision matrix of a landscape node and its corresponding environment element:

[0018] Based on the structural landscape access motivation information R of the residential outdoor environment element in the landscape node id , the landscape access motivation evaluation index of the t th period of w landscape nodes A l ={a w ,w=1,2,3,…,l} forms an initial decision matrix Z wt , the probability matrix P wt of the initial decision matrix Z wt is calculated in turn, and the calculation process is as follows:

[0019] Wherein, i=1,2,3…,m,d=1,2,3…,n

[0020] Wherein, represents the uncertainty of the landscape access motivation information of the m th level of the n th residential outdoor environment element of the w th landscape node in the t th period.

[0021] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: calculating an entropy value E of the outdoor environmental elements of the residential area according to an information entropy principle id and a landscape proximity motivation information utility value O id to obtain a weight T of the environmental elements id Specifically,

[0022] wherein, represents the landscape proximity motivation information of the tth period of the nth outdoor environmental element of the residential area; w represents the number of landscape nodes in the outdoor environment of the residential area;

[0023] The change state of the landscape proximity motivation information of the landscape nodes corresponding to the environmental elements in the outdoor environment of the residential area is divided into an evolution state EVO wt represents the dominance degree of the outdoor environmental elements of the residential area, reflecting the vector space distance of the environmental elements from the optimal value, and the calculation process is as follows:

[0024] wherein, i=1, 2, 3…, m, d=1, 2, 3…, n

[0025] wherein, B wt represents the landscape proximity motivation comprehensive variable of the outdoor environmental elements of the residential area, represents the landscape proximity motivation evaluation index of the ith level of the dth environmental element corresponding to the wth landscape node in the outdoor environment of the residential area in the tth period; represents the evolution coefficient of the landscape proximity motivation evaluation index.

[0026] wherein the similar state SIM wt represents the correlation tendency of the outdoor environmental elements of the residential area, reflecting the collaborative change trend of the environmental elements, and the calculation process is as follows:

[0027] wherein, i=1, 2, 3…, m, d=1, 2, 3…, n, w=1, 2, 3…, l

[0028] wherein, represents the correlation coefficient of the wth landscape node in the outdoor environment of the residential area to the ideal node with respect to the ith level of the dth environmental element; σ is a resolution coefficient, indicating the importance of the max calculation.

[0029] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: when encoding the GPS positions and change states of the landscape nodes in the outdoor environment of the residential area, integrating the levels of outdoor environmental elements in the residential area, the category names, the change states of the landscape approach motivations, and the GPS position attributes. The organizational structure of the preset geographical concept set of the outdoor environment of the residential area is a K-layer structure tree. When 1 < k ≤ K, the maximum number of branches included in the k-th layer is defined as Then the encoding of this classification hierarchy tree has bits.

[0030] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the process of constructing the distribution adjacency network of the landscape nodes based on the initial decision matrix of the landscape approach motivation of the landscape nodes:

[0031] Based on the evolutionary state and similarity state results of the environmental element landscape approach motivation, construct the distribution adjacency network decision matrix S. The distribution adjacency network C = (w, e) consists of w landscape nodes and e edges, forming an undirected network, which is used as the distribution adjacency network of the landscape nodes.

[0032] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: according to the actual geographical distance of the landscape nodes in the outdoor environment of the residential area, for the local adjacency index of the evolutionary state and similarity state of the environmental element landscape approach motivation of the landscape node q in the t-th period and Calculate, which is:

[0033] Where, and respectively represent the evolutionary state and similarity state attribute values of the landscape node in the q-th outdoor environment of the residential area in the t-th period; represents the spatial weight between the landscape node q and the landscape node h in the t-th period, which is determined by the actual geographical distance between the landscape nodes; and respectively represent the average values of the evolutionary state and similarity state attribute values of all landscape nodes; S _EVO and S _SIM respectively represent the standard deviations of the evolutionary state and similarity state attribute values of all landscape nodes; w represents the number of landscape nodes;

[0034] Load the geographical concept set of the outdoor environment of the residential area and the local adjacency index of the landscape nodes into the distribution adjacency network decision matrix S, and calculate the driving degree of the edge connecting the landscape node q and the landscape node h in the t-th period Where q ≤ w, h ≤ w, which is:

[0035] Where, and Let represent the landscape proximity motivation evolution state and similarity state adjacency distribution network driving degree between landscape nodes q and h in the outdoor environment of the residential area at the t-th period, respectively; T represents the non-normalized symmetric spatial interaction matrix between landscape nodes q and h in period t; _SE and T _SS represents the equidistant weights of the adjacency distribution network driving degree for the evolutionary state and the similar state, respectively.

[0036] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of performing environmental hierarchical decision processing on the distributed adjacency network of landscape nodes to obtain the decision results of the outdoor environment of the residential area:

[0037] This study analyzes the evolutionary and similarity state data of proximity motivation information in the outdoor environment of residential areas. By calculating the within-class sum of squared deviations (WSS) and between-class sum of squared deviations (BSS) of the evolutionary and similarity states, the optimal level is selected based on the minimum WSS and maximum BSS. Therefore, the evolutionary and similarity states are divided into f levels, resulting in a total of f levels. 2 A hierarchical pattern of changes in the outdoor environmental landscape of a residential area as a result of motivational changes.

[0038] Based on the hierarchical model of outdoor environmental change in residential areas, the driving force of landscape proximity motivation changes in landscape nodes and their corresponding environmental elements is used. Using the adjacency of landscape nodes as the basis for connecting them, the driving degree of the outdoor environment nodes in the residential area is mapped by engineering drawing primitives, and a decision network representing the spatial distribution of the adjacent landscape nodes in the residential area is generated.

[0039] The spatial effect intensity of the adjacency network of the landscape proximity motivation distribution of outdoor landscape nodes in residential areas is analyzed, and the outdoor landscape nodes in residential areas that need to be improved are:

[0040] Among them, SEI t This represents the spatial effect intensity of landscape nodes in the outdoor environment of the residential area during the t-th period. This represents the driving force assignment of landscape node q and node h in the t-th period to indicate the state of landscape proximity motivation change. hour, when hour, This represents the number of edges connecting the q-th landscape node in the t-th period to other nodes;

[0041] The spatial effect intensity values ​​are normalized. The within-class sum of squares (WSS) and between-class sum of squares (BSS) of the normalized spatial effect intensity are calculated. The rank with the smallest WSS and the largest BSS is selected as the optimal rank, resulting in u ranks of spatial effect intensity. Each u rank is then assigned a value of u. i =j, for j=1,2,…,u. Simultaneously, the f of the landscape node... 2 The hierarchical model of the outdoor environmental change status of a residential area is assigned the value g. i =i, for i = 1, 2, ..., f 2 Thus, the priority of landscape node update and governance, Pr, is obtained. _nod , is: Pr _nod =u i ·g i .

[0042] Secondly, in order to achieve the above objectives, the present invention discloses a residential outdoor environment decision-making system based on landscape proximity motivation, comprising:

[0043] The data translation module is used to acquire engineering drawing data and material test data of the outdoor environment of the residential area, and to conduct landscape approach motivation test on the material test data of the outdoor environment of the residential area to obtain environmental element data that has passed the test. The engineering drawing data of the outdoor environment of the residential area includes planar vector data and GPS positioning data of the outdoor environment of the residential area, and the material test data includes standard image data of the outdoor environmental elements of the residential area.

[0044] The state recognition module is used to delineate landscape nodes in the outdoor environment of the residential area based on engineering drawing data and verified environmental element data. It determines the evolution and similarity change status of the landscape nodes based on the landscape proximity motivation information of the corresponding environmental elements in the outdoor environment of the residential area.

[0045] The network construction module generates an initial decision matrix of landscape approach motivation for landscape nodes and their corresponding environmental elements based on the GPS location and change status of landscape nodes in the outdoor environment of the residential area. It encodes the discrete variables of GPS location and change status of landscape nodes in the outdoor environment of the residential area and constructs a distributed adjacency network of landscape nodes based on the initial decision matrix of landscape approach motivation.

[0046] The environmental decision module is used to perform environmental hierarchical decision processing on the distributed adjacency network of landscape nodes to obtain the decision results of the outdoor environment of the residential area.

[0047] The beneficial effects of this invention are:

[0048] This invention:

[0049] (1) In view of the problems of subjectivity of the outdoor environment cognition data measurement method of residential areas and complicated processing process, the application combines brain cognition test analysis technology to record the brain electrical signals of instantaneous stimulation of environmental elements of residential areas in real time; the required environmental element landscape approach motivation cognition characteristic information is extracted from the complex and changeable brain electrical signals by setting the brain electrical "approach-avoidance" cognition reaction judgment rule, the objective collection and rational analysis of environmental cognition data are realized, and the precision of the extraction of the outdoor environment cognition characteristics of residential areas is improved.

[0050] (2) In view of the problems of insufficient analysis of the difference, fluidity and evolution of the outdoor environmental elements of residential areas, on the basis of obtaining the environmental element landscape approach motivation index result, the time and space range data of the elements are comprehensively considered to establish the initial decision matrix of the landscape nodes of the outdoor environment of residential areas and the corresponding environmental elements, the evolution and similar change state of the landscape nodes are judged, the time and space change state analysis of the multi-dimensional environmental elements is realized, and the efficiency and accuracy of the matrix operation of the outdoor environmental elements of residential areas are ensured.

[0051] (3) In view of the problems of low integration, low spatial resolution and lack of application docking of environmental update management of the landscape approach motivation network decision of residential areas, the time and space change state of the landscape node approach motivation is mapped to the geographic space, the outdoor environment decision network of residential areas is generated by combining the distributed adjacent network decision matrix, the grade mode and spatial effect intensity of the change state of the landscape nodes are determined, and the priority of the landscape node update management of the outdoor environment of residential areas is provided, and the ability of accurate decision and integrated analysis of the outdoor environment of residential areas is improved. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, a brief introduction will be given to the drawings needed to be used in the embodiments or prior art description, and obviously, other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings;

[0053] Fig. 1 is a flowchart of the method of the application;

[0054] Fig. 2 is a development trend graph of the landscape approach motivation evolution state of the outdoor environment of residential areas in the embodiment of the application;

[0055] Fig. 3 is a development trend graph of the landscape approach motivation similar state of the outdoor environment of residential areas in the embodiment of the application;

[0056] Fig. 4 is a decision network graph of the outdoor environment of residential areas in the summer, autumn and winter periods in the embodiment of the application;

[0057] Fig. 5 is a system structure diagram of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0059] Embodiment one:

[0060] As shown in FIG. 1, the outdoor environment decision-making method of residential area based on landscape approach motivation has the characteristics that the method comprises the following steps:

[0061] S101: Obtain the engineering drawing data and material test data of the outdoor environment of the residential area, and perform landscape approach motivation inspection on the material test data of the outdoor environment of the residential area to obtain the environment element data that passes the inspection, wherein the engineering drawing data of the outdoor environment of the residential area comprises outdoor environment plane vector data and GPS positioning data, and the material test data comprises outdoor environment element standard image data;

[0062] The process of performing landscape approach motivation inspection on the material test data of the outdoor environment of the residential area is as follows:

[0063] Idle state debugging, Stroop effect test and random playing of outdoor environment element standard image of residential area;

[0064] The Stroop effect test result is subjected to hypothesis testing to obtain a fitted landscape approach motivation evaluation index, the fitted landscape approach motivation evaluation index is normalized, and finally the environment element data that passes the inspection is obtained;

[0065] Specifically, the present application will be further described by embodiments as follows:

[0066] Obtain the prefrontal electroencephalogram signal of the outdoor environment element standard image of the residential area, accurately cut the electroencephalogram signal according to the environment element category and the collection period, adopt the data cleaning process of band-pass filtering, noise artifact removal, ICA analysis and frequency spectrum analysis, and perform significance test and internal consistency reliability test, and then calculate the power spectrum density of the environment element prefrontal electroencephalogram alpha, theta and gamma rhythm band that passes the test.

[0067] According to the frontal lobe asymmetry principle of the brain, the landscape approach motivation disturbance index (x1; x2; x3) that can represent the landscape "approach-avoidance" reaction is generated from the ratio of the power spectrum density of the electroencephalogram alpha, theta and gamma rhythm band, and the calculation process is as follows:

[0068] Wherein, n represents the number of electroencephalogram channels of the prefrontal lobe of the brain; PSD(alpha), PSD(theta), and PSD(gamma) represent power spectral densities of alpha, theta, and gamma rhythm wave bands, respectively; left and right represent electroencephalogram channels of the left side of the prefrontal lobe of the brain and electroencephalogram channels of the right side of the prefrontal lobe of the brain, respectively.

[0069] The Stroop effect test results are subjected to statistical checking of hypothesis testing (t value and two-tailed p value), confidence interval, and effect size, and effective landscape proximity motivation disturbance index values are screened. On this basis, the steady electroencephalogram signals obtained in the idle state debugging stage are used to calibrate the change baseline of the landscape proximity motivation, and the fitted landscape proximity motivation evaluation index AW is obtained, which is specifically:

[0070] Wherein, x 1_pre , x 2_pre , x 3_pre , x 1_post , x 2_post , x 3_post represent the average values of the landscape proximity motivation disturbance indexes in the last 1 min of the 30s idle state debugging stage and the random playing stage of the outdoor environmental element materials of the residential area, respectively; a, b, and c represent the coefficients of the respective landscape proximity motivation disturbance indexes in the linear combination, indicates a random error; the larger the value of AW is, the stronger the cognitive motivation to approach the landscape environment is.

[0071] The landscape proximity motivation evaluation index values are normalized to obtain the structural information of the outdoor environmental elements of the residential area in the t period which contains m levels and n elements.

[0072] Wherein, the calculation formula for normalizing the outdoor environmental elements of the residential area is as follows:

[0073] Wherein, represents the normalized landscape proximity motivation evaluation index value of the i-type outdoor environmental element of the residential area in the t period; represents the original landscape proximity motivation evaluation index value of the i-type environmental element in the t period; represents the mean value of the original landscape proximity motivation evaluation index of the i-type environmental element in the t period; represents the standard deviation of the original landscape proximity motivation evaluation index of the i-type environmental element in the t period.

[0074] In this embodiment, the AutoCAD software is used to organize the outdoor environment of residential area plane vector data, including residential area red line range, geographic elevation data, road streamline data, green space planting data, hard space data, building vector data, and GPS positioning data of outdoor environment elements of residential area. At the same time, according to the construction status and landscape renewal management needs of the outdoor environment of residential area, the system collects image data of environmental elements in summer, autumn and winter, the actual pixel percentage of the environmental elements represented by the outdoor environment image of the residential area is > 70%, and the image standard of environmental elements in different periods is consistent. The element hierarchical system including node mode, form structure and landscape monomer is constructed, wherein the functional hierarchical elements include gathering and distributing open space, recreational landscape and functional place; the form hierarchical elements include enclosure, sequence, shape and sign; the monomer hierarchical elements include vegetation, water body, structure and facility.

[0075] The EEGomylab portable EEG acquisition system and the MNE-Python EEG cleaning toolkit are used to process the instantaneous stimulation EEG data of the outdoor environment elements of the residential area, wherein all the environmental elements pass the significance test with P value < 0.05; the internal consistency Cronbach α of each type of element is > 0.65. According to the number of test times of the test population and test materials, the G*Power software is used to verify that the environmental element instantaneous stimulation result meets the significance P value ≤ 0.05, the statistical power is > 0.5, the confidence interval does not contain zero, and the effect size is Cohen's d ≥ 0.2, so that the landscape approach motivation change state recognition of the outdoor environment elements of the residential area can be continued.

[0076] S102: Based on the engineering drawing data of the outdoor environment of the residential area and the environmental element data passing the test, the landscape nodes in the outdoor environment of the residential area are delineated, and the evolution and similar change state of the landscape nodes are determined according to the landscape approach motivation information of the corresponding environmental elements of the landscape nodes in the outdoor environment of the residential area;

[0077] S103: Based on the GPS position and change state of the landscape nodes in the outdoor environment of the residential area, an initial decision matrix of the landscape approach motivation of the landscape nodes and the corresponding environmental elements is generated, the discrete variables of the GPS position and change state of the landscape nodes in the outdoor environment of the residential area are coded, and a distribution adjacency network of the landscape nodes is constructed based on the initial decision matrix of the landscape approach motivation of the landscape nodes.

[0078] The process of generating the initial decision matrix of the landscape approach motivation of the landscape nodes and the corresponding environmental elements is as follows:

[0079] Based on the structural landscape approach motivation information R of the environmental elements in the landscape nodes of the outdoor environment of the residential area id , w landscape nodes A l ={a wThe landscape approach motivation evaluation index of the tth period is an initial decision matrix Z wt The probability matrix P wt of the initial decision matrix Z wt is calculated in sequence, and the calculation process is as follows:

[0080] Wherein, i = 1, 2, 3, …, m, d = 1, 2, 3, …, n

[0081] Wherein, Indicates the uncertainty of the landscape approach motivation information of the wth landscape node in the tth period on the n th outdoor environmental element in the mth level.

[0082] According to the information entropy principle, the entropy value E id and the landscape approach motivation information utility value O id of the outdoor environmental element of the residential area are calculated, and the weight T id of the environmental element is obtained, and the specific process is as follows:

[0083] Wherein, Indicates the landscape approach motivation information of the n th outdoor environmental element in the tth period; w indicates the number of landscape nodes in the outdoor environment of the residential area.

[0084] The change state of the landscape approach motivation information of the landscape node corresponding to the environmental element in the outdoor environment of the residential area is divided into evolution state EVO wt and similar state SIM wt , wherein the evolution state EVO wt indicates the dominance degree of the outdoor environmental element of the residential area, and reflects the vector space distance between the environmental element and its optimal value, and the calculation process is as follows:

[0085] Wherein, i = 1, 2, 3, …, m, d = 1, 2, 3, …, n

[0086] Wherein, B wt indicates the landscape approach motivation comprehensive variable of the outdoor environmental element of the residential area, Indicates the landscape approach motivation evaluation index of the wth landscape node in the tth period corresponding to the dth environmental element in the ith level in the outdoor environment of the residential area. Indicates the evolution coefficient of the landscape approach motivation evaluation index.

[0087] The similar state SIM wt indicates the correlation trend of the outdoor environmental element of the residential area, and reflects the cooperative change trend of the environmental element, and the calculation process is as follows:

[0088] wherein, i = 1, 2, 3…, m, d = 1, 2, 3…, n, w = 1, 2, 3…, l

[0089] wherein, represents the correlation coefficient of the wth landscape node in the outdoor environment of the residential area to the idth environmental element about the most ideal node in the ith level; σ is a resolution coefficient, indicating the importance of max calculation.

[0090] In this embodiment, the GIS software is used to perform kernel density analysis on the GPS positioning data of the outdoor environmental elements of the residential area, and the high-frequency and multi-type environmental element aggregation areas are identified. The map marking method is used to obtain the use frequency and intensity of the residents in the outdoor environment of the residential area. On the basis of superimposing the high-frequency and multi-type environmental element aggregation areas and the use frequency and intensity range of the residents, and referring to the results of the aforementioned environmental element landscape proximity motivation test, 15 representative landscape nodes are selected.

[0091] Through the initial decision matrix of the environmental element landscape proximity motivation of the outdoor environment of the residential area, the weights of the outdoor environmental elements of the residential area in the three periods of summer, autumn and winter are calculated (Table 1), and the weight coefficients of the functional level in the three periods are 27.52%, 34.45% and 41.42% respectively; the weight coefficients of the form level in the three periods are 36.31%, 30.37% and 24.73% respectively; and the weight coefficients of the monomer level in the three periods are 36.18%, 35.19% and 33.86% respectively. The resolution coefficient σ is taken as 0.5, and then the evolution state and the similar state of the environmental element landscape proximity motivation are identified, and the dynamic development trend of the landscape nodes in the outdoor environment of the residential area is obtained (Figures 2 and 3).

[0092] Table 1 Weight of outdoor environmental elements of residential area

[0093] When encoding the GPS position and change state of the landscape nodes in the outdoor environment of the residential area, the outdoor environmental element level, category name, landscape proximity motivation change state and GPS position attribute are fused. The preset organization structure of the geographical concept set of the outdoor environment of the residential area is a structure tree of K layers, when 1 < k ≤ K, the maximum number of branches contained in the kth layer is defined as Then the encoding of the classification hierarchical structure tree has bits.

[0094] According to the attribute threshold of the outdoor environmental element level and category of the residential area, the element attribute is converted into a visual variable through conditional judgment, and the mapping relationship of the objective reality of the outdoor environment of the residential area is output by drawing parameters. The visual variable is a vector symbol of a graphic primitive, including a straight line, an ellipse, a polygon and the like, and each type of graphic primitive has its own drawing parameters

[0095] In this embodiment, referring to the One-Hot coding method, the structure tree of the geographical concept set of the outdoor environment of the residential area is designed to have four layers. The first layer is a landscape node, the second and third layers are element levels and categories, and the fourth layer is a landscape proximity motivation change state with a time sequence, including two branches of evolution state and similarity state. On this basis, according to the visualization goal of the outdoor environment decision network of the residential area, a drawing parameter and visual variable conversion format comparison table of the outdoor environment element attribute of the residential area (Table 2) is formulated.

[0096] Table 2 Drawing parameter and visual variable conversion format comparison table

[0097] Based on the evolution state and similarity state results of the landscape node and the corresponding environmental element landscape proximity motivation, a distribution adjacency network decision matrix S is built. The distribution adjacency network C=(w, e) is composed of w landscape nodes and e edges, forming an undirected network as the distribution adjacency network of the landscape node.

[0098] According to the actual geographical distance of the landscape node in the outdoor environment of the residential area, the local adjacency index of the evolution state and the similarity state of the environmental element landscape proximity motivation of the landscape node q in the tth period is calculated as follows: and Specifically,

[0099] wherein, and represent the evolution state and the similarity state attribute values of the landscape node in the qth residential outdoor environment in the tth period, respectively; represents the spatial weight between the landscape node q and the landscape node h in the tth period, which is determined by the actual geographical distance between the landscape nodes; and represent the average values of the evolution state and the similarity state attribute values of all landscape nodes, respectively; _EVO and _SIM represent the standard deviations of the evolution state and the similarity state attribute values of all landscape nodes, respectively; and w represents the number of landscape nodes.

[0100] When or , the landscape node adjacent area has a higher aggregation distribution of the evolution state or the similarity state attribute values of the environmental elements; when or , the aggregation of the evolution state or the similarity state attribute values of the environmental elements of the landscape node adjacent area is lower.

[0101] The geographical concept set of the outdoor environment of the residential area and the local adjacency index of the landscape node are loaded into the distribution adjacency network decision matrix S, and the driving degree of the edge connecting the landscape node q and the landscape node h in the tth period is calculated wherein q≤w, h≤w, and

[0102] wherein, and respectively represent the landscape proximity motivation evolution state and the similar state adjacency distribution network driving degree between the landscape nodes q and h in the outdoor environment of the residential area in the tth period; respectively represent the non-standardized symmetric space action matrix between the landscape nodes q and h in the tth period; T _SE and T _SS respectively represent the equal distance weights of the evolution state and the similar state adjacency distribution network driving degree.

[0103] When , the landscape proximity motivation change state distribution network driving degree between the landscape node q and the node h is larger, indicating that the landscape node q and the node h have a larger contribution to the spatial pattern of the landscape proximity motivation of the common adjacent area of the outdoor environment of the residential area; when , the landscape proximity motivation change state distribution network driving degree between the landscape node q and the node h is smaller, indicating that the landscape node q and the node h have a smaller contribution to the spatial pattern of the landscape proximity motivation of the common adjacent area of the outdoor environment of the residential area.

[0104] S104: performing environment classification decision processing on the distribution adjacency network of the landscape node to obtain a decision result of the outdoor environment of the residential area.

[0105] Backtracking the landscape proximity motivation evolution state and the similar state data of the outdoor environment of the residential area, by calculating the within-class sum of squares WSS and the between-class sum of squares BSS of the evolution state and the similar state, selecting the classification with the minimum WSS and the maximum BSS as the optimal classification, thereby dividing the evolution state and the similar state into f levels, and obtaining f 2 level patterns of the change state of the outdoor environment of the residential area.

[0106] On the basis of the level pattern of the change state of the outdoor environment of the residential area, taking the driving degree of the landscape proximity motivation change state of the landscape node and the corresponding environmental element as the edge weight, taking whether the landscape nodes are adjacent as the basis for the connection between the landscape nodes, mapping the driving degree geographical relationship of the outdoor environment node by engineering drawing primitives, and generating a decision network of the outdoor environment of the residential area representing the spatial distribution of the adjacency of the landscape nodes.

[0107] Analyzing the spatial effect intensity of the landscape proximity motivation distribution adjacency network of the landscape nodes of the outdoor environment of the residential area, obtaining the landscape nodes of the outdoor environment of the residential area to be improved, and

[0108] wherein SEI tThis represents the spatial effect intensity of landscape nodes in the outdoor environment of the residential area during the t-th period. This represents the driving force assignment of landscape node q and node h in the t-th period to indicate the state of landscape proximity motivation change. hour, when hour, This represents the number of edges connecting the q-th landscape node in the t-th period to other nodes;

[0109] The spatial effect intensity values ​​are normalized. The within-class sum of squares (WSS) and between-class sum of squares (BSS) of the normalized spatial effect intensity are calculated. The rank with the smallest WSS and the largest BSS is selected as the optimal rank, resulting in u ranks of spatial effect intensity. Each u rank is then assigned a value of u. i =j, for j = 1,2,…,u. Simultaneously, the f of the landscape node... 2 The hierarchical model of the outdoor environmental change status of a residential area is assigned the value g. i =i, for i = 1, 2, ..., f 2 Thus, the priority of landscape node update and governance, Pr, is obtained. _nod , is: Pr _nod =u i ·g i .

[0110] In this embodiment, the changes in the proximity motivation of the outdoor environment landscape in the residential area are divided into three levels: the evolutionary state includes three levels: low [0, 0.332), medium [0.332, 0.523], and high [0.523, 1]; the similar state includes three levels: low [0, 0.566], medium [0.566, 0.702], and high [0.702, 1]. A total of nine levels of changes in the outdoor environment landscape in the residential area and their corresponding five combination patterns are obtained (Table 3). Through the implementation of the decision network, the distribution adjacency network of the outdoor environment landscape nodes in the residential area is drawn (Figure 4), where the solid and dashed lines of the edges represent the driving degree of the distribution adjacency network of the landscape nodes being greater than 1 and less than 1, respectively.

[0111] Table 3. Grading Model of Outdoor Environmental Changes in Residential Areas

[0112] The spatial effect intensity of the distribution adjacency network of outdoor landscape nodes in residential areas was calculated, yielding three level intervals: [0, 0.498), [0.498, 0.811), and [0.811, 1]. Simultaneously, the natural discontinuity method was used to divide the landscape nodes... Data classification yielded a cluster center of approximately 4.871. Therefore, the SEI was set accordingly. t ≥0.8 and The landscape nodes play an important role in the decision-making network and have a great influence on the decision-making; SEI t <0.5 and The landscape nodes need to be improved, thus generating four combination modes and assigning values (Table 4).

[0113] Table 4 Grade mode of spatial effect intensity of landscape nodes in outdoor environment of residential area

[0114] The priority ranking of the update management of the landscape nodes in the outdoor environment of the residential area in summer, autumn and winter is calculated respectively, and the top three priority rankings of the landscape nodes in summer are node 4 (Pr _nod = 15; recession type, update type), node 2 (Pr _nod = 12; antagonistic type, update type), and nodes 7, 9, 10, 11 and 12 (Pr _nod = 10; recession type, promotion type); the top three priority rankings of the landscape nodes in autumn are nodes 2, 4, 5, 7 and 9 (Pr _nod = 15; recession type, update type), node 11 (Pr _nod = 10; recession type, promotion type), and nodes 1, 8, 12 and 14 (Pr _nod = 6; critical type, promotion type); the top three priority rankings of the landscape nodes in winter are node 5 (Pr _nod = 12; antagonistic type, update type), node 7 (Pr _nod = 10; recession type, promotion type), and node 11 (Pr _nod = antagonistic type; recession type, promotion type).

[0115] Embodiment two: the second aspect, as shown in Figure 5, the decision-making system of the outdoor environment of the residential area based on the landscape approach motivation, comprising:

[0116] The data translation module 11 is used to obtain the engineering drawing data and material test data of the outdoor environment of the residential area, and to perform landscape approach motivation inspection on the material test data of the outdoor environment of the residential area, to obtain environment element data that passes the inspection, wherein the engineering drawing data of the outdoor environment of the residential area includes outdoor environment plane vector data and GPS positioning data, and the material test data includes outdoor environment element standard image data;

[0117] The state recognition module 12 is used to delimit the landscape nodes in the outdoor environment of the residential area based on the engineering drawing data and the environment element data that passes the inspection of the outdoor environment of the residential area, and to determine the evolution and similar change state of the landscape nodes according to the landscape approach motivation information of the corresponding environment elements of the landscape nodes in the outdoor environment of the residential area.

[0118] The network building module 13 is configured to generate a landscape access motivation initial decision matrix of the landscape node and the corresponding environmental element based on the GPS position and the change state of the landscape node in the outdoor environment of the residential area, encode the discrete variables of the GPS position and the change state of the landscape node in the outdoor environment of the residential area, and construct a distribution adjacency network of the landscape node based on the landscape access motivation initial decision matrix of the landscape node.

[0119] The environmental decision module 14 is configured to perform environmental hierarchical decision processing on the distribution adjacency network of the landscape node, and obtain a decision result of the outdoor environment of the residential area.

[0120] Based on the same inventive concept, the application further provides a computer device, which comprises one or more processors and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor is the computing core and control core of the terminal, and is configured to implement one or more instructions, and is specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0121] It should be further noted that based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is run by a processor to execute the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus.

[0122] In the description of the present application, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0123] The above shows and describes the basic principles, main features and advantages of the present disclosure. It should be understood by those skilled in the art that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and all these changes and improvements fall within the scope of the present disclosure.

Claims

1. A residential outdoor environment decision-making method based on landscape approach motivation, characterized by, The method includes the following steps: The engineering drawing data and material test data of the outdoor environment of the residential area are obtained. The landscape approach motivation test is performed on the material test data of the outdoor environment of the residential area to obtain the environmental element data that passes the test. The engineering drawing data of the outdoor environment of the residential area includes the planar vector data and GPS positioning data of the outdoor environment of the residential area, and the material test data includes the standard image data of the outdoor environmental elements of the residential area. Based on the engineering drawing data and verified environmental element data of the residential area's outdoor environment, landscape nodes in the residential area's outdoor environment are delineated. The evolution and similarity change status of the landscape nodes are determined based on the landscape proximity motivation information of the corresponding environmental elements in the residential area's outdoor environment. The initial decision matrix of landscape approach motivation for landscape nodes and their corresponding environmental elements is generated based on the GPS location and change status of landscape nodes in the outdoor environment of the residential area. The discrete variables of GPS location and change status of landscape nodes in the outdoor environment of the residential area are encoded, and the distributed adjacency network of landscape nodes is constructed based on the initial decision matrix of landscape approach motivation for landscape nodes. Environmental hierarchical decision processing is performed on the distribution and adjacency network of landscape nodes to obtain the decision results of the outdoor environment of the residential area.

2. The landscape-access-motivation-based outdoor environment decision method for residential districts according to claim 1, wherein, The process of verifying the landscape proximity motivation based on material test data of the outdoor environment of the residential area: Idle state debugging, Stroop effect test and random playback of standard images of outdoor environmental elements in residential areas; Specifically, the Stroop effect test results are used to perform hypothesis testing to obtain a fitted landscape access motivation assessment index. The fitted landscape access motivation assessment index is then normalized to obtain environmental element data that has passed the test. 3.The landscape-access-motivation-based outdoor environment decision method for residential districts according to claim 2, wherein, The formula for normalizing the fitted landscape approach motivation assessment indicator: wherein a normalized landscape accessibility motive evaluation index value of an outdoor environmental element of the i-th type of residential district in the t-th period of time; a raw landscape approximation motivation evaluation index value of an i-th environmental factor in a t-th period of time; a mean value of the original landscape approximation motivation evaluation index of the i-th environmental factor in the t-th period of time; The standard deviation of the original landscape proximity motivation assessment index for the i-th type of environmental element in period t. 4.The landscape-access-motivation-based residential outdoor environment decision method according to claim 1, wherein, The process of generating the initial decision matrix of landscape proximity motivation for landscape nodes and their corresponding environmental elements: R id , information of structural landscape approach motivation of outdoor environment element of residential district in landscape node l , landscape approach motivation evaluation index of the tth period of w landscape nodes A w , w = 1, 2, 3, …, l} constitutes an initial decision matrix Z wt , the probability matrix P wt of the initial decision matrix Z wt is calculated in sequence, and the calculation process is as follows: Where i = 1, 2, 3, ..., m, d = 1, 2, 3, ..., n wherein, This represents the uncertainty of the landscape proximity motivation information of the w-th landscape node at the m-th level and the n-th residential area outdoor environmental element at the t-th period. 5.The landscape-access-motivation-based outdoor environment decision method for residential district according to claim 4, wherein, Refer to the information entropy principle, calculate the entropy value E of outdoor environmental elements in residential areas id And landscape proximity motivation information utility value O id , get the weight T of environmental elements id , specifically: wherein represents the amount of landscape proximity motivation information for the nth residential area's outdoor environmental element at the tth period; w represents the number of landscape nodes in the residential area's outdoor environment. The change state of the landscape node corresponding to the environmental element landscape approach motivation information in the outdoor environment of the residential area is divided into evolution state EVO wt The advantage degree of the environmental element in the outdoor environment of the residential area is represented, which reflects the vector space distance of the environmental element and its optimal value, and the calculation process is as follows: where i = 1, 2, 3..., m, d = 1, 2, 3..., n wherein B wt represents a landscape approach motivation composite variable for residential outdoor environmental elements, a landscape proximity motivation evaluation index of a dth environmental element of an i th level corresponding to a wth landscape node in an outdoor environment of a residential room in a tth period of time; The coefficient representing the evolution of indicators for assessing landscape approach motivation; wherein the similarity state SIM wt The correlation tendency of the outdoor environment elements of the residential area is represented, and the collaborative change tendency of the environment elements is embodied. The calculation process is as follows: Where i = 1, 2, 3, ..., m, d = 1, 2, 3, ..., n, w = 1, 2, 3, ..., l wherein σ represents the correlation coefficient between the w-th landscape node and the most ideal node in the outdoor environment of the residential area with respect to the d-th environmental element at the i-th level; σ is the resolution coefficient, indicating the importance of the max calculation. 6.The landscape-access-motivation-based residential outdoor environment decision method according to claim 1, wherein, The GPS position and the change state of the landscape node in the outdoor environment of the residential area are encoded by fusing the outdoor environment element hierarchy, the category name, the landscape approach motivation change state and the GPS position attribute, the preset organization structure of the geographical concept set of the outdoor environment of the residential area is a K-layer structure tree, when 1 The encoding of the classification hierarchy tree has bits. 7.The landscape-access-motivation-based residential outdoor environment decision method according to claim 1, wherein, The process of constructing a distributed adjacency network of landscape nodes based on the initial decision matrix of landscape proximity motivation of landscape nodes and their corresponding environmental elements: Based on the evolution and similarity results of landscape nodes and their corresponding environmental elements, a distributed adjacency network decision matrix S is constructed. The distributed adjacency network C = (w, e) consists of w landscape nodes and e edges, forming an undirected network, which serves as the distributed adjacency network of landscape nodes. 8.The landscape-access-motivation-based outdoor environment decision method for residential districts according to claim 7, wherein, According to the actual geographical distance of the landscape nodes in the outdoor environment of the residential area, the local adjacency index of the evolution state and the similar state of the landscape nodes q in the tth period of the environmental element landscape approach motivation and The calculation is as follows: wherein, and respectively represent the evolution state and the similar state attribute value of the landscape node in the outdoor environment of the qth residential unit in the tth period; represents the spatial weight between the landscape node q and the landscape node h in the tth period, which is determined by the actual geographical distance between the landscape nodes; and S and S respectively represent the mean value of the evolution state and the similar state attribute value of all landscape nodes; w represents the number of landscape nodes. _EVO S and S respectively represent the mean value of the evolution state and the similar state attribute value of all landscape nodes; w represents the number of landscape nodes. _SIM S and S respectively represent the mean value of the evolution state and the similar state attribute value of all landscape nodes; w represents the number of landscape nodes. loading the local adjacency index of the geographical concept set of the outdoor environment of the residential area and the landscape node into the distribution adjacency network decision matrix S, calculating the driving degree of the edge connecting the landscape node q and the landscape node h in the tthperiod where q < w, h < w, and wherein and respectively represent the landscape proximity motivation evolution state and the similar state adjacency distribution network driving degree between the landscape nodes q and h in the outdoor environment of the residential room in the tth period; denotes the non-normalized symmetric spatial interaction matrix between landscape nodes q and h in the tth period; T _SE and T _SS denote the equidistance weights of the adjacency distribution network driving degree of evolutionary state and similar state, respectively. 9.The landscape-access-motivation-based outdoor environment decision method for residential district according to claim 1, wherein, The process of performing environment hierarchical decision processing on the distribution adjacency network of the landscape nodes to obtain the outdoor environment decision result of the residential area: The evolution state and the similar state data of the outdoor environment landscape access motivation information of the residential area are analyzed, the within-class sum of squares WSS and the between-class sum of squares BSS of the evolution state and the similar state are calculated, the classification with the minimum WSS and the maximum BSS is selected as the optimal classification, the evolution state and the similar state are divided into f levels, and f 2 level patterns of the outdoor environment landscape access motivation change state of the residential area are obtained. On the basis of the outdoor environment change state level mode of residential quarters, the driving degree of landscape node and its corresponding environment element landscape approach motivation change state For edge weight, whether the landscape nodes are adjacent is taken as the basis for the connection between the landscape nodes, the driving degree geographical relationship of the outdoor environment node of the residential area is mapped by engineering drawing primitives, and a decision network of the outdoor environment of the residential area representing the spatial distribution of the adjacent landscape nodes is generated; The space effect intensity of the landscape access motivation distribution adjacent network of the outdoor environment landscape node of the residential quarter is analyzed, and the outdoor environment landscape node to be improved of the residential quarter is obtained, which is: wherein SEI t represents the spatial effect intensity of the landscape node in the outdoor environment of the residential quarter in the tth period; represents the assigned value of the driving degree of the change state of the landscape approaching motivation of the tth period landscape node q and the node h landscape, when Time, When Time, The number of edges representing the connection between the qth landscape node in the tth period and other nodes; The space effect intensity numerical value is normalized, the within-class sum of squares of deviation WSS and the between-class sum of squares of deviation BSS of the normalized space effect intensity are calculated, the classification with the minimum WSS and the maximum BSS is selected as the optimal classification, u levels of the space effect intensity are obtained, and the u levels are assigned as u i for j = 1, 2, …, u, and the f 2 outdoor environment change state level mode of the landscape node is assigned as g i for i = 1, 2, …, f 2 , thereby obtaining the landscape node update management priority Pr _nod , which is Pr _nod = u i · g i .

10. A residential outdoor environment decision system based on landscape approach motivation, characterized by, It comprises: A data translation module configured to obtain engineering drawing data and material test data of the outdoor environment of the residential area, perform landscape proximity motivation inspection on the material test data of the outdoor environment of the residential area, and obtain environment element data that passes the inspection, wherein the engineering drawing data of the outdoor environment of the residential area comprises outdoor environment plane vector data and GPS positioning data, and the material test data comprises outdoor environment element standard image data; A state recognition module configured to delimit landscape nodes in the outdoor environment of the residential area based on the engineering drawing data and the environment element data that passes the inspection, and determine the evolution and similar change state of the landscape nodes according to the landscape proximity motivation information of the corresponding environment elements of the landscape nodes in the outdoor environment of the residential area; A network building module configured to generate a landscape proximity motivation initial decision matrix of the landscape nodes and the corresponding environment elements based on the GPS positions and change states of the landscape nodes in the outdoor environment of the residential area, encode the discrete variables of the GPS positions and change states of the landscape nodes in the outdoor environment of the residential area, and construct a distribution adjacency network of the landscape nodes based on the landscape proximity motivation initial decision matrix of the landscape nodes; An environment decision module configured to perform environment hierarchical decision processing on the distribution adjacency network of the landscape nodes to obtain the outdoor environment decision result of the residential area.

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