RETRACTED ARTICLE: Residential planning and layout design method based on multi-sensor information fusion

Abstract

With the in-depth development of the market economy and the acceleration of the process of urbanization, a large number of people have poured into the city, and a large number of residential communities have been developed and constructed. Design quality in the development process is often replaced by output. Some companies attach importance to quality but do not know how to control quality. Based on this, this paper uses the method of multi-sensor information fusion to study the planning and layout design methods of residential areas, and provides a design basis for solving the problems of people’s living environment deterioration and increasingly scarce land resources. Based on the Rhinoceros and Grasshopper parametric platform, this paper integrates residential information model, performance prediction technology, and multi-sensor information fusion technology, taking residential planning and layout parameters as design variables. A set of intelligent optimization system for residential planning and layout based on multi-performance objective simulation was compiled. The “internal factors” and “external factors” that affect the results of RLIOS are studied, and then the residential planning and layout design methods are studied. Experiments have proved that no matter what algorithm is used, the performance of each target can be improved, the floor area ratio performance can be improved by 35.3–3.3%, and the open space performance can be improved by 12.6–31.36%. It shows that the residential planning and layout design method based on multi-sensor information fusion proposed in this paper improves the accuracy and efficiency of the work.

1 Introduction

As far as my country is concerned, the development of residential planning and layout design is still lacking, and blindly applying foreign development experience cannot meet its own development needs. The owner’s requirements for residential buildings have evolved from a mere residential function to a multifaceted demand integrating versatility, comfort, richness, and luxury. However, the layout scale imbalance and residential room are exposed in the early design. Problems such as mutual influence and disordered spatial organization of residential areas also need to be resolved urgently. The new demands of the public for this type of residence provide a good opportunity for the study of its planning and layout.

With the explosive growth of the world’s population, the land resources available for human construction are increasingly scarce, and the result is that the population density is getting higher and higher. This is a problem that every country in the world must face. With the advancement of construction technology, in order to solve the prominent population density problem, the density of residential buildings has been continuously increased and high-rise buildings have been gradually used in residential construction. Although high-density housing improves the efficiency of land use and solves the housing problem of an increasing number of people to a certain extent, the accompanying deterioration of the living environment, reduced communication activities, indifferent neighborhood relations, changes in lifestyles, and many issues such as loss of complex still pose many problems for human habitation.

Foreign research on the field of multi-sensor information fusion architectural design is relatively early. Although the development of domestic digital generative design started late, it is also very fast. Subsequently, many scholars and architects began to pay attention to the application of performance simulation technology combined with design practice, which reveals the relationship between design requirements and performance simulation. Mirahadi F explained the application of numerical simulation in the study of the thermal environment of the new riverside residential development in summer. The study consisted of more than 100 two-story detached houses built on a river near Tokyo, Japan. According to meteorological data, in terms of surface temperature, air temperature, and wind distribution at pedestrian heights of land and buildings, the prevailing wind direction and the northward river that affect the microclimate of the study area form a certain angle. These factors have been estimated using stepwise CFD (computational fluid dynamics) simulations of radiation, conduction, and convection. This method improves the outdoor thermal environment by manipulating site design and layout planning scenarios. However, the lack of experimental data in his research led to the sample set that has small differences, which leads to inaccurate results [1]. Traditional residential layout design is inadequate because its performance simulation is independent of the main process of design. In order to solve this problem, Belen Sosa M proposed an integrated automatic design method, called MOOD-S (simulation-based multi-objective optimization design), which is driven by physical performance simulation, running multi-objective optimization algorithms, and parameterized modeling. Four different design tests were introduced using MOOD-S. In these tests, parameters such as floor area ratio, summer solar radiation gain, solar satisfaction rate, daylighting factor, and field-of-view factor are selected to assemble the target set. The geometric parameters, relative positions, and combined form characteristics of the single building are used as independent variables to control the design optimization. The results show that this method can respond to different types of needs by providing a highly diverse set of feasible solutions, but its research does not show the quantitative competitive relationship between targets, which is not conducive to scene screening and selection [2]. Liu H proposed an improved method to realize the automatic design of modular houses in mass customization production. Given a set of modular placement rules for design, the form problem can be viewed as a two-dimensional single large object placement problem with fixed dimensions and additional positioning constraints. This formula leads to a search for a floor plan layout constrained by size and location constraints in the combined size search space. The genetic algorithm strategy was implemented and demonstrated for automated floor plan design to provide the required design solution layout. When these layouts are embedded in an appropriate graphical interface system, future owners will be able to purchase houses that meet their exact needs at an affordable price, improve the quality of life, and comply with the design language. However, the overall research lacks data support. More data are needed to support its conclusion [3].

The residential planning layout design method based on multi-sensor information fusion realizes the process of data integration and automatic optimization. It avoids the limitations of traditional manual empirical scheme design and optimization methods and transfers complex artificial design problems that are difficult to determine to the computer to realize. This paper changes the disconnection between the design steps and the performance simulation steps in the previous design process. While greatly improving the efficiency of design generation, the design process solves the core problem of the design adjustment process tending to performance optimization at the internal logic level, provides a residential layout optimization and generation idea based on residential multi-sensor information fusion, transforms abstract indicators such as physical environment quality into concrete architectural layout design problems, and makes the residential layout plan reasonable and well-founded. The design is more humane, providing a design basis for the ecological decision-making of the settlement plan.

2 Multi-sensor information fusion and residential planning and layout design

2.1 Multi-sensor information fusion

2.1.1 Principle of multi-sensor information fusion

In the human body, organs such as eyes, ears, nose, tongue, hands, and skin are like sensors, which are used to obtain the vision, hearing, smell, taste, and touch perception of the target, and these perceptions are aggregated into the brain for comprehensive processing. From that analysis, we can get the understanding and knowledge of the target [4, 5]. Multi-sensor information fusion is the use of multiple sensors to obtain relevant information and perform data preprocessing, correlation, filtering, integration, and other operations to form a framework. This framework can be used to make decisions, so as to achieve the purposes of identification, tracking, and situation assessment. A schematic diagram of multi-sensor information fusion is shown in Fig. 1.

Fig. 1

Schematic diagram of multi-sensor information fusion

In short, multi-sensor information fusion system includes the following three parts: Sensor is the cornerstone of sensor information fusion system. Information cannot be obtained without sensors, and multiple sensors can obtain more comprehensive and reliable data. Data are a multi-sensor information fusion system, and the processing object in fusion is the carrier of fusion. The data quality determines the upper limit of the performance of the fusion system, and the fusion algorithm is only close to this upper limit; when the quality of information cannot be changed, fusion is to mine information to the greatest extent and make decisions according to information [6, 7].

2.1.2 Information categories of multi-sensor information fusion

In the sensor system, relying on a single sensor is often not enough to accurately detect the target, which may lead to larger errors or even errors, so multiple sensors are required. If each sensor makes independent decisions, regardless of the connection between the detection information of each sensor, not only key information will be lost, resulting in a waste of massive data resources, but also a sharp increase in data processing workload [8, 9]. Therefore, it is necessary to comprehensively process and analyze data from multiple sensors, which is the essence of multi-sensor information fusion. In a multi-sensor information fusion system, information comes from multiple sources, such as historical databases, artificial prior information, and sensor detection information. The information in this article is mainly multi-sensor detection information. Multi-sensor detection information is mainly divided into three categories:

• 1. In a multi-sensor system, multiple sensors detect the same characteristics of a target, so as to obtain a large amount of repeated and homogeneous data, which are redundant information. Redundant information is not useless information. Through multiple detections of the target, the contingency of a single sensor is avoided, and the integrity and reliability of the data are improved.

• 2. Complementary information means that multiple sensors detect the target from different aspects, different angles, and different characteristics, so as to obtain multi-dimensional information of the target, making the information more comprehensive and accurate [10, 11]. The complementary information is associated and fused to obtain multi-dimensional information, which helps eliminate the ambiguity of single information on target detection and avoids blind people from touching the image.

• 3. Collaborative information means that one sensor cannot complete the acquisition of information, and multiple sensors are required to work together to complete the acquisition of information. In the passive direction finding cross-positioning system, each sensor can only detect the direction finding angle information of the target, and a single sensor cannot locate the target, so at least two sensors need to work together to complete the target positioning.

2.1.3 Functional model of multi-sensor information fusion

The functional model of multi-sensor information fusion has been widely recognized since it was proposed. More and more systems gradually adopt this functional model. The functional model of multi-sensor information fusion is shown in Fig. 2.

Fig. 2

Functional model diagram of multi-sensor information fusion

Level zero: data preprocessing. The data transmitted to the system by multiple sensors are affected by noise or interference, resulting in a certain degree of inaccuracy, incompleteness, and inconsistency in the data, which affects the performance of subsequent processing tasks. Therefore, preprocessing of multi-sensor data is essential.

Level 1: Target assessment. Target evaluation is mainly to estimate the target state or parameters, and the evaluation result is the basis for subsequent processing tasks. The evaluated state or parameters mainly include target maneuver model parameters, target position, and target feature vector. Target position estimation is to estimate the actual position of the target based on the established motion model and track measurement data. The target feature vector is a vector that can characterize the target attribute extracted from the original data [12, 13].

Level 2: Situation assessment. Situation assessment is to assess the entire environment based on the assessment results of the target assessment [14]. Situation assessment is mainly used in the battlefield environment. On the battlefield, based on the current situation assessed, a map of factors such as combat schedule, time, location, and force is established to organically integrate the detected enemy forces, battlefield environment, and enemy intentions. All linked together and finally formed a situation map on the battlefield.

Level 3: Impact assessment. Impact assessment evaluates the impact caused by the behavior that may be induced by the results of the situation assessment, which is essentially a predictive behavior.

Level 4: Process evaluation. Process evaluation is the optimization of the entire system. Through the establishment of evaluation indicators, the entire system is monitored and evaluated, thereby improving the performance of the entire fusion system [15].

2.2 Data association algorithm

2.2.1 Data association algorithm based on residual

The main idea of the residual-based data association algorithm is to use the spatial geometric relationship in the measurement process to determine the residual of any intersection and then to solve the loss function of the possible association combination based on the residual, and to determine the final associated combination [16, 17].

As shown in Fig. 3, there are two sensors a and B in the same area. At time K, the position coordinates of target Z are $(X_{0} ,Y_{0} ,Z_{0} )$, and $(x_{i1} ,y_{i1} ,z_{i1} )$ and $(x_{{i{2}}} ,y_{{i{2}}} ,z_{{i{2}}} )$ are the position coordinates of sensors A and B. The azimuth and elevation of the two sensors are $(a_{ij} ,\beta_{ij} )$, i is the sensor number, and j is the sensor measurement data number.

Fig. 3

Sensor detection diagram

From the spatial relationship shown in Fig. 3, the azimuth and elevation angles can be expressed as:

$$a_{i1,j1} = \arctan ((Y_{0} - y_{i1} )/(X_{0} - x_{i1} ))$$

(1)

$$\beta_{i1,j1} = \arctan ((Z_{0} - z_{i1} )/\sqrt {(X_{0} - x_{i1} )^{2} + (Y_{0} - y_{i1} )^{2} } )$$

(2)

$$a_{{i{2},j{2}}} = \arctan ((Y_{0} - y_{i2} )/(X_{0} - x_{i2} ))$$

(3)

$$\beta_{{i{2},j{2}}} = \arctan ((Z_{0} - z_{i2} )/\sqrt {(X_{0} - x_{{i{2}}} )^{2} + (Y_{0} - y_{i2} )^{2} } )$$

(4)

Equation 1, Eq. 2, and Eq. 3 can be combined to determine the three-dimensional coordinates of the target:

$$X_{0} = (y_{i2} - y_{i1} + x_{i1} \tan a_{i1,j1} - x_{i2} \tan a_{i2,j2} )/(\cot a_{i1,j1} - \cot a_{i2,j2} )$$

(5)

$$Y_{0} = (x_{i2} - x_{i1} + y_{i1} \cot a_{i1,j1} - y_{i2} \cot a_{i2,j2} )/(\cot a_{i1,j1} - \cot a_{i2,j2} )$$

(6)

$$Z_{0} = \sqrt {(X_{0} - x_{i1} )^{2} + (Y_{0} - y_{i1} )^{2} } \tan \beta_{i1,j1} + z_{{i{1}}}$$

(7)

Incorporating Eq. 5, Eq. 6, and Eq. 7 into Eq. 3, the measurement data can be obtained to satisfy the unique Eq. 8:

$$\begin{gathered} \left| {\frac{{y_{i2} - y_{i1} + (x_{i1} - x_{i2} )\tan a_{i2,j2} }}{{\tan a_{i1,j1} - \tan a_{i2,j2} }}} \right|\sqrt {1 + \tan^{2} a_{i1,j1} } \tan \beta_{i1,j1} \hfill \\ + \left| {\frac{{y_{i2} - y_{i1} + (x_{i1} - x_{i2} )\tan a_{i1,j1} }}{{\tan a_{i1,j1} - \tan a_{i2,j2} }}} \right|\sqrt {1 + \tan^{2} a_{{i{2},j2}} } \tan \beta_{i2,j2} + z_{i1} - z_{i2} = 0 \hfill \\ \end{gathered}$$

(8)

If the measurement data selected by sensor A and sensor B are not from the same target, then the above formula does not hold. Based on this, it can be judged whether the data are from the same target. Define residual $\delta_{i1i2j1j2}$ as:

$$\begin{gathered} \delta_{i1i2j1j2} = \left| {\frac{{y_{i2} - y_{i1} + (x_{i1} - x_{i2} )\tan a_{i2,j2} }}{{\tan a_{i1,j1} - \tan a_{i2,j2} }}} \right|\sqrt {1 + \tan^{2} a_{i1,j1} } \tan \beta_{i1,j1} \hfill \\ + \left| {\frac{{y_{i2} - y_{i1} + (x_{i1} - x_{i2} )\tan a_{i1,j1} }}{{\tan a_{i1,j1} - \tan a_{i2,j2} }}} \right|\sqrt {1 + \tan^{2} a_{{i{2},j2}} } \tan \beta_{i2,j2} + z_{i1} - z_{i2} \hfill \\ \end{gathered}$$

(9)

In actual scenarios, each sensor will have measurement errors, and the residual $\delta_{i1i2j1j2}$ can be used as an evaluation index for association matching [18, 19]. The smaller the residual, the higher the association confidence. In order to measure the correct rate of the association combination, a loss function is defined on the basis of the residual error. For M sensors and N targets, for any set of possible association combinations $T_{k} = \{ 1j^{1} ,2j^{2} , \cdots ,Mj^{N} \}$ in the association set, the loss function is defined as:

$${\text{Cost}}_{1} (T_{k} ) = \sum\limits_{{i1 \ne i2,i1j1i2j2 \in \tau_{k} }} {\delta_{i1i2j1j2} }$$

(10)

It can be seen from Eq. 10 that the loss function is the sum of the residuals between any two measurements in the associated combination. Ideally, when each sensor has no measurement error, the line of sight of each sensor to the same target will intersect at one point, and the loss function is zero. In the actual scene, each sensor has measurement error, and the loss function of measurement data from the same target is the smallest, which is the standard of this method [20, 21].

2.2.2 Data association algorithm based on sight distance

In an ideal situation, there is no measurement error in each sensor, and the line of sight of each sensor to the same target will intersect at a point, and then the distance between the lines of sight is zero. If the distance between the lines of sight is not zero, it means that the line of sight corresponds to the observation that the data do not come from the same goal. In actual engineering, each sensor will have measurement errors, and the sum of the line-of-sight distances from the same target is the smallest, and the data are associated according to this criterion [22, 23]. There are M sensors and N targets in the same area, $(a_{ij} ,\beta_{ij} )$ is the detection information of each sensor for each target, and the position coordinate of each sensor is $(x_{i} ,y_{i} ,z_{i} )(i = 1,2, \ldots ,M)$. The position coordinates of the sensor and the set of $(a_{ij} ,\beta_{ij} )$ detected by the sensor can determine a straight line in the three-dimensional space, and the straight line is the line of sight. The line-of-sight equation can be expressed as:

$$\frac{{X_{0} - x_{i} }}{{l_{ij} }} = \frac{{Y_{0} - y_{i} }}{{m_{ij} }} = \frac{{Z_{0} - z_{i} }}{{n_{ij} }}$$

(11)

In the formula, $(X_{0} ,Y_{0} ,Z_{0} )$ is the current location of the target, $(l_{ij} ,m_{ij} ,n_{ij} )$ is the direction cosine of the line of sight, and the relationship between $(l_{ij} ,m_{ij} ,n_{ij} )$ and $(a_{ij} ,\beta_{ij} )$ is shown in Formulas 12, 13, and 14:

$$l_{ij} = \cos \beta_{ij} \cos a_{ij}$$

(12)

$$m_{ij} = \cos \beta_{ij} \sin a_{ij}$$

(13)

$$n_{ij} = \sin \beta_{ij}$$

(14)

At the same time, M sensors detect N targets, and each sensor can obtain N sets of azimuth and pitch angle data. Combined with the position coordinate $(x_{i} ,y_{i} ,z_{i} )(i = 1,2, \ldots ,M)$ of the M sensors, MN lines of sight are formed. According to the mathematical relationship of space geometry, for any two lines of sight in three-dimensional space, the line-of-sight distance can be solved. Assuming that the two lines of sight are, respectively, determined by the position coordinate $(x_{i1} ,y_{i1} ,z_{i1} )$ of the sensor A and the corresponding direction cosine $(l_{i1j1} ,m_{i1j1} ,n_{i1j1} )$, and the position coordinate $(x_{{i{2}}} ,y_{{i{2}}} ,z_{{i{2}}} )$ of the sensor B and the corresponding direction cosine $(l_{{i{2}j{2}}} ,m_{{i{2}j{2}}} ,n_{{i{2}j{2}}} )$, then the line-of-sight distance can be expressed as:

$$dist_{i1i2j1j2} = \left| {\frac{{(x_{i1} - x_{i2} )\left| {\begin{array}{*{20}c} {m_{i1j1} } & {n_{i1j1} } \\ {m_{i2j2} } & {n_{i2j2} } \\ \end{array} } \right| + (y_{i1} - y_{i2} )\left| {\begin{array}{*{20}c} {n_{i1j1} } & {l_{i1j1} } \\ {n_{i2j2} } & {l_{i2j2} } \\ \end{array} } \right| + (z_{i1} - z_{i2} )\left| {\begin{array}{*{20}c} {l_{i1j1} } & {m_{i1j1} } \\ {l_{i2j2} } & {m_{i2j2} } \\ \end{array} } \right|}}{{\sqrt {\left| {\begin{array}{*{20}c} {m_{i1j1} } & {n_{i1j1} } \\ {m_{i2j2} } & {n_{i2j2} } \\ \end{array} } \right|^{2} + \left| {\begin{array}{*{20}c} {n_{i1j1} } & {l_{i1j1} } \\ {n_{i2j2} } & {l_{i2j2} } \\ \end{array} } \right|^{2} + \left| {\begin{array}{*{20}c} {l_{i1j1} } & {m_{i1j1} } \\ {l_{i2j2} } & {m_{i2j2} } \\ \end{array} } \right|^{2} } }}} \right|$$

(15)

For M sensors and N targets in the same area, let $T_{k} = \{ 1j^{1} ,2j^{2} , \cdots ,Mj^{N} \}$ be a set of possible association combinations of the measuring machine, and define its loss function as:

$${\text{Cost}}_{{2}} (T_{k} ) = \sum\limits_{{i1 \ne i2,i1j1i2j2 \in \tau_{k} }} {{\text{dist}}_{i1i2j1j2} }$$

(16)

From Eq. 16, the loss function is the sum of the line-of-sight distances between any two measurements in the associated combination. In the ideal situation where there is no measurement error in each sensor, if the measurement data corresponding to each line of sight come from the same target, then ${\text{Cost}}_{{2}} (T_{k} ) = {0}$. In actual engineering, each sensor has measurement errors. If the data currently measured by each sensor come from the same target, the loss function ${\text{Cost}}_{{2}} (T_{k} )$ should be the smallest.

2.3 Characteristics of residential planning and design

According to its process, real estate development mainly includes six aspects: acquisition of land, preliminary planning, planning and design, construction, operation and sales, and later service. Planning and design are in the middle. Its characteristic is to transform objective market demand, market data, and land properties into products required by customers. For example, for the development of residential quarters, the customer needs analyzed in the planning and positioning stage must be designed from scratch into the types of houses, gardens, and transportation systems used by the customers and finally delivered for construction to serve the people. Planning and design are creative work, a process of turning the ideas of developers and users into reality. Good products, high quality, and high cost performance are the core competitiveness of an enterprise. Therefore, whether a project can pass the market test is determined by the design stage [24, 25]. From the perspective of product cost performance, reasonable cost control for any enterprise is the guarantee for the healthy development of the enterprise one. Through analysis, once the product is delivered for construction, the scope of cost control is greatly reduced. Generally speaking, the proportion of cost control in the planning and design stage accounts for the entire development process. Therefore, planning and design play a key role in brand establishment, cost control, market recognition and quality determination of real estate companies.

3 Experimental design of housing planning and layout design method based on multi-sensor information fusion

3.1 System components

RLIOS uses the Rhinoceros and Grasshopper parametric modeling and visual programming environment to realize the integration of three modules on the platform, including settlement plan generation module, settlement plan performance simulation evaluation module, and multi-objective optimization algorithm module. The system changes the fixed single mode of the traditional design optimization method and provides users with diversified choices of goals and variables. Users only need to match and combine the goals and variables according to the actual design needs of the residential area, and the system can automatically generate a series of satisfying requirements, including reasonable residential area layout design plan for user needs.

3.2 Experimental method

First of all, establish or guide the residential area site model in Rhino. Through the GH visual programming environment, establish a residential area layout association model that is controlled by the residential area layout morphological variables and restricted by site conditions, sunshine specifications, spacing specifications, and other constraints. We only need to adjust the range of design variable parameters and specific values, and the layout of residential areas will automatically show different forms of changes. Secondly, based on the GH platform, a complete set of residential performance evaluation system is established, and the soft-drop coupling method or formula method is adopted to realize the simulation of multiple performance targets of the residential layout plan. The performance targets mainly include the performance requirements of different stakeholders. Finally, the Octopus multi-objective optimization algorithm plug-in of the GH platform is used to complete the process of solving the multi-objective optimization problem of settlement layout. Through the automatic adjustment of the variable parameters of the settlement layout, the solution of each settlement layout plan with better performance is continuously searched, and finally a series of solution sets with better performance for each target are generated.

3.3 Selection and limitation of design variables

The selection of design variables is related to factors such as the design task, the selection of optimization targets and the method of constructing the settlement model. The value range of the variables is determined according to the design task, design experience, and relevant specifications. Among them, the generation method of the settlement model determines the existence form of the design variables. There are many morphological parameters of residential layout, such as orientation, height, body shape coefficient, plane aspect ratio, and layout shape. The values or ranges of some parameters are proposed by the owner and Party A and do not need to be determined by the architect themselves. They are called “fixed design variables”; some require the architect’s own control and are called “variable design variables.” The fixed or variable of the variable and the determination of its change range are changed according to the design conditions and the changes in the settlement specifications and other conditions, which are the direct causes of the performance of the settlement layout. The effect and degree of influence of various morphological elements of residential planning layout on residential performance are different. For example, some residential planning layout morphological factors have a greater impact on the thermal environment quality of the residential area, while some morphological factors have a greater impact on the residential wind environment quality. Therefore, in the early stage of residential optimization design, it is necessary to effectively select the morphological factors of residential layout planning that affect the performance of the optimized target according to the optimization target, and select the variable parameters with greater influence as variable variables for optimization design. Irrelevant variable parameters are uniformly valued as fixed variables. This approach can reduce the search range of the plan in the optimization process and improve the efficiency of plan optimization generation. As shown in Fig. 4, the fixed or variable attributes of design variables can be adjusted according to the actual design requirements of the project.

Fig. 4

Selection and classification of design variables in the phase of residential district layout plan

3.4 Statistical processing

Statistical analysis was performed with SPSS 13.0 statistical software. The significance test of the difference was performed by a one-way analysis of variance, the difference between the two groups was tested by LSD-t, and the statistics of residential planning and layout design based on multi-sensor information fusion were performed by group t test. P < 0.05 is considered to be significant and statistically significant.

4 Results section

4.1 RLIOS performance test

4.1.1 Experimental design

The experimental plot uses a 200 m × 200 m regular rectangular plot, and the surrounding area contains existing buildings, as shown in Fig. 5; the climate file uses the .epw climate file of a certain area, and the residential area layout generation model uses version 3.0, which is integrated and optimized the platform using Rhinoceros 5.0 version and Grasshopper_0.9.76.0 version. In order to facilitate the comparative study, except for the population size and algorithm mechanism settings, the remaining Octopus parameters are kept at the default settings: Elitism is set to 0.5; Mut.Probability is set to 0.1; MutationRate is set to 0.5; and CrossoverRate is set to 0.8.

Fig. 5

Experimental site

In this paper, six groups of RLIOS generation experiments are selected, and the results are shown in Table 1. The experiment uses the same base conditions, the same climate files, the same software and hardware conditions as mentioned above, and the selection of algorithm mechanism, population parameter setting, and target and variable selection research on the impact of RLIOS settlement layout generation results.

Table 1 RLIOS performance research experiment

4.1.2 Benchmark model

In order to study the effect of RLIOS intelligent optimization design, a benchmark residential area layout model was designed as a reference model. The benchmark model adopts the design method of manual experience. The most common form of misalignment in residential area design is the layout. The result shown in Fig. 6 is used to compare the performance improvement effect of each optimization target of the RLIOS optimization generation scheme. The variable parameters of the benchmark layout model are set as follows: The layout form is arranged in three rows staggered from south to north, the morphological parameters of the residential units are unified, the flat aspect ratio of the residence is 3:2, and the standard floor area of the residence is 600 square meters. The number of residential floors is 18, all of which are point-type high-rise residential buildings.

Fig. 6

Baseline residential layout model

4.1.3 The influence of different algorithm combinations on the results of RLIOS generation

The two convergence mechanisms and three mutation mechanisms provided by Octopus can be combined to produce a total of six (2 × 3) algorithm mechanism combinations. Experiments 1–6 used these six different algorithm combinations provided by Octopus to perform operations. The specific situation is shown in Table 2.

Table 2 Experiment 1–6 algorithm mechanism settings

In addition to the algorithm mechanism, all algorithm parameters are set uniformly. There are three groups of variable design variables: the single-unit plan form SFFn, the number of individual floors BSn, and the location of the layout point BPn. The remaining variables are fixed variables. The specific conditions are shown in Table 3 Two optimization goals are unified: floor area ratio goal and open space goal.

Table 3 Experiment 1–6 unified parameter settings

Extract the optimal solution values of the successive generations of Pareto in Experiments 1–6, perform normalization processing, and compare the processing results with the normalized ideal Pareto frontier. The results are shown in Fig. 7.

Fig. 7

Normalized Pareto optimal solution generation-by-generation

It can be seen from Fig. 7 that choosing the Hype Reduction mutation mechanism (B1, B2, B3) is closer to the ideal Pareto frontier than selecting the SPEA-2 Reduction mutation mechanism (A1, A2, A3); choosing the Hype Reduction mutation mechanism (the distribution of the generation-by-generation Pareto optimal solution of B1, B2, B3) is relatively scattered, and the generation-by-generation optimization effect is obvious, while the optimization results of the SPEA-2 Reduction mutation mechanism (A1, A2, A3) are more concentrated, and the convergence phenomenon is obvious. Premature convergence may occur. In addition, several abnormal solutions appeared in the A2 algorithm combination optimization result, and the other algorithm combination forms appeared.

4.1.4 Analysis of optimization range and convergence

Extract the minimum and maximum values of each optimization target in the six groups of experimental results, and calculate the optimization range of each target as shown in Fig. 8.

Fig. 8

Experiments 1–6, calculation results of the optimization range of each target

It can be seen from Fig. 8 that no matter what algorithm is used, the performance of each target can be improved. The floor area ratio performance can be increased by 35.3–43.3%, and the open space performance can be increased by 12.6–31.36%. Relatively speaking, FAR targets are easier to optimize than open space targets. Algorithm combination A3 has obvious target optimization range from the perspective of floor area ratio target and open space target, followed by algorithm combination B2, B1, A1, and algorithm combination A2. The performance of each target optimization range is not good.

Extract the minimum value minf(X) of each target from the generation-by-generation Pareto optimal solution data of Experiments 1–6, and normalize it. If the space remains unchanged for a long time, it can be considered that the optimization has reached convergence. The convergence performance of the six sets of experiments is analyzed, and the results are shown in Table 4.

Table 4 Convergence analysis

It can be seen from Table 4 that the optimization performance of algorithms A2 and A3 is unstable; especially for A2, the Pareto optimal solution space shows strong instability and does not show obvious convergence characteristics within 100 generations; secondly, except for A2 and A3, the rest of the algorithm combinations all showed a good convergence process of generation-by-generation optimization, and the performance of each target was continuously optimized with the increase in the optimization algebra; finally, A1, B1, B2, and B3 all showed a gradual convergence trends, where the convergence characteristics of A1 are more obvious, the Pareto optimal solution space remains unchanged after 50 generations, while the convergence characteristics of B1, B2, and B3 all appear in the 80 generations or later. Therefore, from the perspective of convergence analysis, the combination of A2 and A3 algorithms shows unstable characteristics of the optimization process and is not suitable for solving the multi-objective optimization problem of settlement layout; the combination of A1 algorithms is prone to early convergence, which may lead to local optimization: The combination of B1, B2, and B3 algorithms is relatively stable. With the increase in the optimization algebra, the Pareto optimal solution space gradually converges, and there is no early convergence.

4.2 Application practice

4.2.1 Intelligent optimization of the generated results

In the application process of RLIOS in a project, the population individuals are optimized toward three target vectors. The Pareto optimal solution space gradually stabilizes around 80 generations with the increase in the optimization algebra. The results are shown in Fig. 9.

Fig. 9

Intelligent optimization results of the project

Figure 9 shows the final optimization result of the project, where the X-axis represents the floor area ratio target: 5-FAR(X), the target value in the optimization result varies from 0.05 to 2.76, and the floor area ratio index in the optimization solution can reach the maximum value of 4.95; the Y-axis represents the solar lighting target: 1-SSR (X), the target value varies from 0.28 to 0.4, indicating that the project's solar satisfaction rate can reach up to 83%.

4.2.2 Analysis of the optimal solution

The target performance of the four groups of plans with better floor area ratio, better daylighting performance, better landscape view, and better overall performance is compared. The results are given in Table 5.

Table 5 Four sets of programs and performance target values

It can be seen from Table 5 that the idea of obtaining the optimal solution for a single performance will reduce the quality of other competing performance targets, but this reduction is only relative, and the targets themselves have been optimized. Plan 1 has the highest floor area ratio among the four groups, but its sunshine satisfaction rate and landscape accessibility rate are the lowest. For Plan 2, although the sunshine satisfaction rate is the highest, its floor area ratio is the lowest. In comparison, Scheme 3 and Scheme 4 did not have extreme phenomena of extremely large target value and extremely small target value. At the same time, Scheme 4 is the solution with the most balanced performance.

5 Results and discussion

The accelerating development of urbanization is pushing China’s residential planning and design toward a highly standardized, multi-targeted, and intelligent development direction. There are certain problems in traditional residential planning and design methods and processes, which can no longer meet the multi-performance target design requirements of residential planning. This research proposes a complete set of residential layout design methods based on multi-sensor information fusion, which integrates the “plan modification-plan simulation-plan optimization” links in the traditional residential design process, and adopts multi-sensor information fusion combining data association algorithms, combined with digital modeling technology and performance simulation technology, which realize the intelligent optimization design of residential area layout under the requirement of multi-sensor information fusion performance. On the basis of this method, a settlement layout intelligent optimization system (RLIOS) was constructed and applied to actual settlement design projects, realizing the transition from method theory to application practice. RLIOS has good universality and can realize the intelligent optimization and generation of residential layout schemes under different site conditions. The generated results have an intuitive scheme decision interface, which is convenient for users to compare different schemes during the scheme selection process. Architect plan decision-making provides strong data support. The shortcomings of this study are as follows: Due to time reasons, the RLIOS performance research experiment has the shortcomings of insufficient quantification, and it is impossible to make a specific quantitative analysis of the influencing factors that affect the performance of RLIOS optimization performance. It can only provide qualitative guidance for the use of RLOS. In the next step of the research, we can continue to enrich and improve the RLIOS target and variable system and try to add roads, landscapes, public service facilities, and other variable parameters in the variable system, so that the settlement generation model is closer to the actual settlement situation.