CN-121787288-B - Public facility layout optimization method based on artificial intelligence

CN121787288BCN 121787288 BCN121787288 BCN 121787288BCN-121787288-B

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based public facility layout optimization method, which comprises the steps of collecting basic data of a public facility layout, sequentially carrying out cleaning, denoising, reformatting reduction and feature extraction on the basic data to obtain layout basic features, mapping the public facility layout into a layout state vector according to the layout basic features, determining the utilization rate of the public facility in unit time based on the actual service quantity of the public facility and the design capacity of the public facility to determine whether the space-requirement adaptation degree of the public facility layout meets the requirement, determining whether the increment step length of the public facility site selection coordinate needs to be reduced according to the space-basis coefficient of the public facility layout, and determining the error feedback gain of layout optimization based on the service requirement coverage rate of the public facility. The invention improves the space-demand adaptation of the public facility layout.

Inventors

Shang Zhuang
ZHANG LU

Assignees

大连理工大学

Dates

Publication Date: 20260508
Application Date: 20260305

Claims (10)

1. A method for optimizing a public facility layout based on artificial intelligence, comprising: collecting basic data of public facility layout, sequentially performing cleaning, denoising, reformatting reduction and feature extraction on the basic data to obtain layout basic features, and mapping the public facility layout into a layout state vector according to the layout basic features; Training an initial model by combining user requirements and the layout state vector to obtain an intelligent layout optimization model, and analyzing basic data of the public facility layout according to the intelligent layout optimization model to output a layout optimization scheme; Acquiring actual service quantity of public facilities and design capacity of the public facilities, and determining utilization rate of the public facilities in unit time based on the actual service quantity of the public facilities and the design capacity of the public facilities so as to determine whether space-demand adaptation degree of public facilities layout meets requirements; if the space-requirement adaptation degree of the public facility layout does not meet the requirements, acquiring a space-based coefficient of the public facility layout to determine whether the space connectivity of the public facility layout meets the requirements; if the space connectivity of the public facility layout does not meet the requirements, determining whether the increment step length of the public facility site selection coordinates needs to be reduced according to the space coefficient of the public facility layout; If the increment step length of the public facility site selection coordinates does not need to be reduced, determining error feedback gain of layout optimization based on service demand coverage rate of the public facilities; The error feedback gain of the layout optimization is a key parameter for adjusting the correction strength of the updating amplitude of each round of optimization iteration of the public facility layout to the facility coordinate error or the position deviation.
2. The artificial intelligence based utility layout optimization method of claim 1, wherein determining utility utilization of a utility per unit time based on an actual service volume of the utility and a design capacity of the utility to determine whether a space-demand fit of a utility layout meets a requirement comprises: Obtaining the utilization rate of the public facilities in unit time based on the ratio of the actual service quantity of the public facilities to the designed capacity of the public facilities; Comparing the utilization rate of the public facilities in the unit time with a preset utilization rate; If the utilization rate of the public facilities in the unit time is larger than the preset utilization rate, determining that the space-requirement adaptation degree of the public facility layout meets the requirement; and if the utilization rate of the public facilities in the unit time is smaller than or equal to the preset utilization rate, determining that the space-requirement adaptation degree of the public facility layout does not meet the requirement.
3. The method for optimizing a public facility layout based on artificial intelligence according to claim 2, wherein if the space-demand adaptation degree of the public facility layout is not satisfactory, determining whether the space connectivity of the public facility layout is satisfactory based on the space-based coefficient of the public facility layout.
4. The artificial intelligence based utility layout optimization method of claim 3, wherein determining whether the spatial connectivity of the utility layout meets the requirements based on the spatial co-efficient of the utility layout comprises: comparing the space coefficient of the public facility layout with a preset first space coefficient of the space coefficient; If the space coefficient of the public facility layout is smaller than or equal to the preset first space coefficient of the space coefficient, determining that the space connectivity of the public facility layout meets the requirement; And if the space coefficient of the public facility layout is larger than the preset first space coefficient of the space coefficient, determining that the space connectivity of the public facility layout is not in accordance with the requirement.
5. The artificial intelligence based utility layout optimization method of claim 4, wherein determining whether an incremental step of utility site coordinates needs to be reduced comprises: comparing the space-based coefficient of the public facility layout with the preset first space-based coefficient and the preset second space-based coefficient respectively; If the space base coefficient of the public facility layout is larger than the preset first space base coefficient and smaller than or equal to the preset second space base coefficient, reducing the increment step length of the public facility site selection coordinates; If the spatial coefficient of the public facility layout is greater than the preset second spatial coefficient, the incremental step of the public facility site selection coordinates does not need to be reduced.
6. The method of optimizing a public facility layout based on artificial intelligence of claim 5, wherein the magnitude of the decrease in the incremental step of the public facility site coordinates is determined by the difference between the spatial co-efficient of the public facility layout and the preset first spatial co-efficient.
7. The method for optimizing a public facility layout based on artificial intelligence according to claim 6, wherein the estimated accuracy of the service demand content is preliminarily determined to be unsatisfactory based on the condition that the space-based coefficient of the public facility layout is larger than the preset second space-based coefficient, and whether the estimated accuracy of the service demand content is satisfactory or not is determined according to the service demand coverage rate of the public facility.
8. The method for optimizing a layout of a public facility based on artificial intelligence according to claim 7, wherein determining whether the estimated accuracy of the service demand content meets the requirement according to the service demand coverage of the public facility comprises: Comparing the service demand coverage rate of the public facilities with a preset coverage rate; If the service demand coverage rate of the public facilities is larger than the preset coverage rate, determining that the estimated accuracy of the service demand content meets the requirements, and determining whether the increment step length of the public facility site selection coordinates meets the requirements; if the service demand coverage rate of the public facilities is smaller than or equal to the preset coverage rate, determining that the estimated accuracy of the service demand content does not meet the requirements, and increasing the error feedback gain of layout optimization.
9. The artificial intelligence based utility layout optimization method of claim 8, wherein the utility's service demand coverage is the ratio of the number of actual completed services of the utility to the total number of user demands in a unit area.
10. The method of claim 9, wherein the magnitude of the increase in error feedback gain for the layout optimization is determined by the difference between the preset coverage rate and the service demand coverage rate of the utility.

Description

Public facility layout optimization method based on artificial intelligence Technical Field The invention relates to the technical field of artificial intelligence, in particular to a public facility layout optimization method based on artificial intelligence. Background Under the background of new urban and smart city construction, the public facility layout optimization method based on artificial intelligence has become a key technology for improving city operation efficiency, promoting resource fair allocation and enhancing public service quality. Because public facility layout relates to cooperation of multidimensional complex elements such as population distribution, traffic network, land utilization, environmental bearing, social fairness and the like, the planning scheme directly influences the living convenience of residents and the sustainable development of cities. Although the existing digital planning tool supports basic geographic information system analysis and simple optimization models, the existing digital planning tool focuses on single targets or limited constraints, and has insufficient fusion capability on multi-source space-time data, dynamic optimization under complex constraints and real-time integration capability of public participation feedback, so that efficient, fair and practical automatic layout recommendation is difficult to realize in actual city planning. Therefore, there is a need for an artificial intelligence-based public facility layout optimization method that can integrate multi-objective reinforcement learning, and achieve accurate prediction of multi-scale requirements, autonomous optimization of multiple constraints, and overall trade-off of social benefits and operation costs. The patent publication No. CN114821816A discloses a gymnastic place layout optimization method based on artificial intelligence, which comprises the steps of carrying out target tracking on each user entering a gymnastic place to obtain an activity track of the user, determining a stay position and stay time of the user according to the activity track to obtain the gymnastic intention of the user, counting the gymnastic intention of all users in a fixed time period, obtaining a reference using time length of the gymnastic facility according to the gymnastic intention of all users and the corresponding theoretical gymnastic time length of different types of gymnastics, obtaining a using time length of each gymnastic facility in the fixed time length, obtaining a first heat of each gymnastic facility through a difference value between the using time length and the corresponding reference using time length, obtaining an interesting area of each track point in the activity track of each user, selecting the gymnastic facility of the same type as the user in each interesting area, obtaining a reference using time length according to the using information, the number of the user waiting nearby, the latest gymnastic facility of different types and the theoretical gymnastic time length of the corresponding gymnastic facility, obtaining a real heat vector through the layout parameter, and optimizing the first heat vector of each gymnastic facility. Therefore, the gymnasium layout optimization method based on the artificial intelligence has the problem that the space-requirement adaptation degree of the public facility layout is insufficient due to the fact that local historical behavior data and a single heat balance target are depended on and multi-dimensional space-time constraint and dynamic requirement change are not fully fused. Disclosure of Invention Therefore, the invention provides an artificial intelligence-based public facility layout optimization method, which is used for solving the problem that the space-requirement adaptation degree of the public facility layout is insufficient due to the fact that local historical behavior data and a single heat balance target are depended on and multi-dimensional space-time constraint and dynamic requirement change are not fully fused in the prior art. In order to achieve the above object, the present invention provides an artificial intelligence based public facility layout optimization method, comprising: collecting basic data of public facility layout, sequentially performing cleaning, denoising, reformatting reduction and feature extraction on the basic data to obtain layout basic features, and mapping the public facility layout into a layout state vector according to the layout basic features; Training an initial model by combining user requirements and the layout state vector to obtain an intelligent layout optimization model, and analyzing basic data of the public facility layout according to the intelligent layout optimization model to output a layout optimization scheme; Acquiring actual service quantity of public facilities and design capacity of the public facilities, and determining utilization rate of the public facilities in unit time ba