Modularized design method for full-garden multi-storey residence

Abstract

The invention relates to a modular design method for a full-garden multi-storey house. The method comprises the steps that firstly, ground spaces around a multi-storey residential building body are obtained, then the ground spaces on the front side, the rear side and the side faces of the building body are reasonably divided, and exclusive private garden areas are created for residents on all floors. And then, the optimal installation position of the elevator in the private garden area is determined by means of a deep learning position prediction model, the district type shared elevator shared by the two households is constructed, and then an optimal direct path set from the private garden area to the corresponding floor is generated. And then multi-door structure data of the partitioned shared elevator is obtained, and independent channels for users on all floors to enter the elevator are determined. And finally, according to the use condition of the independent channel, mastering the running state of the elevator used by the users on any floor, and realizing a non-interfering running mode of the users on different floors. The use efficiency of the elevator is effectively improved, mutual interference between floors is avoided, and a higher-quality living environment is created for residents.

Description

Technical Field

[0001] The invention belongs to the field of architectural design and planning, and in particular relates to a modular design method for a full-garden multi-story residential building. Background Art

[0002] With the advancement of architectural design and planning technology, modular design technology for multi-story residential buildings with full gardens has emerged. Multi-story residential buildings, as a common residential type, have seen some development in recent years, with the emergence of products such as villas with elevators and villas with gardens on the ground or top floors. However, they still have significant limitations. Currently, standard floors (2nd to 5th floors) of multi-story residential buildings cannot be equipped with private gardens, resulting in low living quality and value on these floors, making it difficult to meet residents' demand for personalized, high-quality living spaces. At the same time, the pursuit of comfort and livability is hindered by the poor living experience of high-rise and high-density residential buildings. Multi-story residential buildings have become a key option for balancing living quality and land resources. Traditional multi-story residential designs have relatively simple functions on each floor, with inadequate distinctions between public and private spaces. In particular, there is a lack of planning for personalized leisure spaces for residents, making it difficult to meet residents' needs for closeness to nature and private outdoor spaces. Furthermore, regarding elevator design and operation, traditional elevator layouts and operating modes suffer from inefficiencies and interference between residents. Furthermore, existing architectural designs lack efficient design methods and technical means to address complex terrain and diverse resident needs, making it difficult to achieve rapid, accurate, and personalized design solutions. Summary of the Invention

[0003] Based on this, it is necessary to address the above technical problems and provide a modular design method for multi-story residential buildings with full gardens, which can provide private garden areas for residents on each floor, effectively improve the efficiency of elevator use and avoid mutual interference between floors.

[0004] In a first aspect, the present application provides a modular design method for a full-garden multi-story residential building, comprising:

[0005] Get the ground space around a multi-story residential building.

[0006] The optimization algorithm based on regional division is used to divide the ground space on the front, back and back sides to obtain the private garden area for residents on each floor; among them, a roof garden is set up on the roof as a private garden area for residents on the top floor.

[0007] A deep learning location prediction model is used to determine the optimal installation location of the elevator in the private garden area and build a partitioned shared elevator for two households, obtaining the optimal direct path set from the private garden area to the corresponding floor.

[0008] Obtain the multi-door structure data of the segmented shared elevator; use the different direction entrances and optimal direct path set of the multi-door structure to determine the independent channels for users on each floor to enter the segmented shared elevator.

[0009] Based on the usage of independent channels, the operating status of users on any floor using the segmented shared elevator is obtained, and the algorithm is used to switch the operating status to obtain an operating mode in which users on different floors do not interfere with each other.

[0010] In one embodiment, the optimization algorithm based on area division is used to divide the ground space on the front, back and back sides to obtain the private garden areas for residents on each floor, including:

[0011] Obtain three-dimensional coordinate data of the ground space on the front, side and rear sides; the three-dimensional coordinate data includes terrain undulations and obstacle distribution.

[0012] An initial area grid is generated based on the three-dimensional coordinate data, and the constraints of the private garden area are extracted. The initial area grid contains the projected boundaries of each floor. The constraints include area thresholds and connectivity requirements.

[0013] Based on the constraints, a multi-objective optimization algorithm is used to iteratively divide the initial regional grid to obtain the set of regional boundary coordinates after division.

[0014] A three-dimensional visualization model is generated based on the set of regional boundary coordinates, and the three-dimensional visualization model is synchronized with the resident terminal to obtain real-time space status update information.

[0015] The deviation value of the area division is corrected based on the real-time space status update information, and the private garden area of the residents on each floor is obtained according to the corrected area division result.

[0016] In one embodiment, a deep learning location prediction model is used to determine the optimal installation location of an elevator in a private garden area and to construct a partitioned shared elevator for two households. The optimal direct path set from the private garden area to the corresponding floor is obtained, including:

[0017] Get user movement trajectory data in the private garden area.

[0018] Generate the visit frequency distribution matrix of each floor based on the user movement trajectory data.

[0019] Obtain the building topology of the private garden area and each floor; the building topology includes floor connection relationships and path weight parameters.

[0020] The access frequency distribution matrix and path weight parameters are input into the path optimization algorithm to generate a set of direct path weight coefficients.

[0021] An elevator candidate location evaluation model is constructed based on the direct path weight coefficient set and the building topology map, and a set of compartmentalized layout solutions that meet the dual-household sharing conditions are obtained.

[0022] A convolutional neural network is used to process the set of compartmentalized layout solutions to obtain the elevator installation coordinates and the corresponding path connection matrix.

[0023] The floor accessibility parameters in the building topology are updated according to the path connectivity matrix to generate an optimal set of direct paths.

[0024] In one embodiment, the access frequency distribution matrix and the path weight parameters are input into a path optimization algorithm to generate a set of direct path weight coefficients, including:

[0025] The access frequency distribution matrix is normalized to obtain the node weight distribution.

[0026] The node weight distribution and path weight parameters are input into the path optimization algorithm, and the link priority score is calculated to obtain the link priority sequence.

[0027] A dynamic adjustment strategy is adopted according to the link priority sequence to generate a transmission delay coefficient; the dynamic adjustment strategy iteratively updates the link weight based on a preset convergence threshold.

[0028] The transmission delay coefficient is combined with the redundancy index in the path weight parameter to obtain a set of direct path weight coefficients.

[0029] In one embodiment, the link priority score is calculated using the following formula:

[0030]

[0031] Where L(p) represents the link priority score, γ represents the adjustment coefficient, and W k represents the weight of the kth path segment, len(P k ) represents the length of the kth path segment, h represents the number of segments the path is divided into, ω n Indicates the maximum weight value in the nth path node.

[0032] In one embodiment, the operating status of a user on any floor using a segmented shared elevator is obtained based on the usage of independent channels, and an algorithm is used to switch the operating status to obtain an operating mode in which users on different floors do not interfere with each other, including:

[0033] Get the real-time occupancy rate of independent channels; the real-time occupancy rate is calculated by the ratio of the length of the user request queue on each floor to the elevator capacity.

[0034] The time slice rotation cycle is determined according to the real-time occupancy rate, and a dynamic scheduling instruction is generated; the dynamic scheduling instruction includes the target floor number and the running direction parameter.

[0035] According to the target floor number, the dynamic scheduling instruction is used to activate the independent channel of the corresponding floor, and the response delay data of the independent channel is monitored.

[0036] When the response delay data exceeds a preset threshold, the time slice rotation period is recalculated to obtain an updated time slice rotation period.

[0037] The dynamic scheduling instructions are updated according to the updated time slice rotation period to execute the motor drive operation, thereby obtaining an operation mode in which users on different floors do not interfere with each other.

[0038] In a second aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0039] Get the ground space around a multi-story residential building.

[0040] The optimization algorithm based on regional division is used to divide the ground space on the front, back and back sides to obtain the private garden area for residents on each floor; among them, a roof garden is set up on the roof as a private garden area for residents on the top floor.

[0041] A deep learning location prediction model is used to determine the optimal installation location of the elevator in the private garden area and build a partitioned shared elevator for two households, obtaining the optimal direct path set from the private garden area to the corresponding floor.

[0042] Obtain the multi-door structure data of the segmented shared elevator; use the different direction entrances and optimal direct path set of the multi-door structure to determine the independent channels for users on each floor to enter the segmented shared elevator.

[0043] Based on the usage of independent channels, the operating status of users on any floor using the segmented shared elevator is obtained, and the algorithm is used to switch the operating status to obtain an operating mode in which users on different floors do not interfere with each other.

[0044] In a third aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0045] Get the ground space around a multi-story residential building.

[0046] The optimization algorithm based on regional division is used to divide the ground space on the front, back and back sides to obtain the private garden area for residents on each floor; among them, a roof garden is set up on the roof as a private garden area for residents on the top floor.

[0047] A deep learning location prediction model is used to determine the optimal installation location of the elevator in the private garden area and build a partitioned shared elevator for two households, obtaining the optimal direct path set from the private garden area to the corresponding floor.

[0048] Obtain the multi-door structure data of the segmented shared elevator; use the different direction entrances and optimal direct path set of the multi-door structure to determine the independent channels for users on each floor to enter the segmented shared elevator.

[0049] Based on the usage of independent channels, the operating status of users on any floor using the segmented shared elevator is obtained, and the algorithm is used to switch the operating status to obtain an operating mode in which users on different floors do not interfere with each other.

[0050] The modular design method, computer equipment, and storage medium for a multi-story residential building with a full garden first obtain the ground space surrounding the multi-story building. Then, using an optimization algorithm based on regional division, the ground space at the front, rear, and sides of the building is rationally divided, creating exclusive private garden areas for residents on each floor. For top-floor residents, a rooftop garden is also provided as a private space. Next, a deep learning-based location prediction model is used to determine the optimal elevator installation location within the private garden area. A segmented shared elevator for two households is constructed, and the optimal set of direct paths from the private garden area to the corresponding floor is generated. Data on the multi-door structure of the segmented shared elevator is then obtained. Combined with the optimal set of direct paths, independent access channels are determined for users on each floor. Finally, the elevator's operating status is determined based on the usage of the independent channels. The algorithm switches between these states to achieve an operation mode where users on different floors do not interfere with each other. It greatly improves the comfort and privacy of living, allowing residents to be closer to nature and enjoy the unique outdoor space, effectively improves the efficiency of elevator use, reduces residents' waiting time, avoids mutual interference between floors, improves the convenience and safety of living, and creates a better living environment for residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A flow chart of a modular design method for a full-garden multi-story residential building provided by an embodiment of the present invention;

[0053] Figure 2 A flowchart of an embodiment of the present invention for obtaining the operating status of a user on any floor using a segmented shared elevator based on the usage of independent channels, and for switching the operating status using an algorithm to obtain an operating mode in which users on different floors do not interfere with each other;

[0054] Figure 3 This is a plan view of the first to seventh floors of a multi-story, all-garden residence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0056] In one embodiment, Figure 1 As shown, the present application provides a modular design method for a full-garden multi-story residential building, which may include the following steps:

[0057] Step S101: Acquire the ground space around a multi-story residential building.

[0058] Specifically, a variety of specialized methods, such as high-precision geographic information surveying and satellite remote sensing mapping, are used to comprehensively and accurately collect data on the building's surrounding terrain, landforms, and actual usable area. This data not only clarifies the boundaries of the ground space but also provides a deeper understanding of key information such as the site's topography and the presence of obstacles.

[0059] Step S102: Divide the front, back and three sides of the ground space based on the area division optimization algorithm to obtain private garden areas for residents on each floor; among them, a rooftop garden is set up on the rooftop as a private garden area for residents on the top floor.

[0060] An optimization algorithm for regional division is used to finely delineate the ground space at the front, back, and sides of the building. The algorithm first generates an initial regional mesh containing the projected boundaries of each floor based on the collected 3D ground space coordinate data. It also extracts constraints for private garden areas, such as area thresholds that must meet the needs of residents on different floors and connectivity requirements for garden areas to ensure convenient access. Next, based on these constraints, a multi-objective optimization algorithm is used to iteratively divide the initial regional mesh, continuously adjusting the regional boundaries to achieve the optimal division. Once the division is complete, a 3D visualization model is generated based on the resulting set of regional boundary coordinates. This data is synchronized with resident terminals, allowing residents to monitor the space status in real time and provide timely feedback on correcting any deviations in the regional division. Ultimately, the private garden areas for residents on each floor are determined. For the top-floor residents, given their unique location, rooftop gardens are installed on the rooftop. This fully utilizes the space and provides them with a dedicated outdoor recreational area, achieving the design goal of ensuring private gardens for all floors.

[0061] Step S103, using a deep learning location prediction model to determine the optimal installation location of the elevator in the private garden area and construct a partitioned shared elevator shared by two households, to obtain the optimal direct path set from the private garden area to the corresponding floor.

[0062] To ensure residents can conveniently access their floors directly from their private gardens, a deep learning location prediction model is used to determine the optimal elevator installation location. First, user trajectory data from the private garden area is collected. This data reflects residents' daily paths and frequent stops within the garden. A building topology map of each floor is also obtained, detailing floor connectivity and path weight parameters, which are used to measure the accessibility of different paths. A floor visit frequency distribution matrix is generated based on the user trajectory data. This, along with the path weight parameters, is input into a path optimization algorithm to calculate a set of direct path weight coefficients. Based on these coefficients and the building topology map, a candidate elevator location evaluation model is constructed to identify a set of compartmentalized layout options that meet the dual-family sharing requirement. These options are then processed using a convolutional neural network to determine the precise elevator installation coordinates and the corresponding path connectivity matrix. The floor accessibility parameters within the building topology map are updated based on the path connectivity matrix, generating an optimal set of direct paths from the private garden area to the corresponding floors, ensuring fast and efficient travel between the garden and the floors.

[0063] Step S104, obtaining multi-door structure data of the segmented shared elevator; using the different direction entrances of the multi-door structure and the optimal direct path set, determine the independent channels for users on each floor to enter the segmented shared elevator.

[0064] Specifically, after determining the elevator's installation location and optimal direct access path, data on the multi-door structure of the segmented shared elevator is obtained. Different elevator doors correspond to different floor entrances. By analyzing the relationship between the different entrances in the multi-door structure and the previously generated optimal direct access path set, combined with the floor layout and resident behavior, precise independent access paths for users on each floor are planned. For example, with a two-door elevator, a user entering from their side of the elevator door or elevator car can only reach their own floor and must exit from the same side. With a three-door elevator, the elevator car has doors on all three sides, except the counterweight side. Users enter and exit from their own side of the elevator car and can only reach their own floor, entering and exiting through the third door. This design effectively prevents interference between residents on different floors while using the elevator, ensuring the independence and privacy of users on each floor.

[0065] Step S105: Based on the usage of the independent channels, the operating status of the users on any floor using the segmented shared elevator is obtained, and the operating status is switched using an algorithm to obtain an operating mode in which users on different floors do not interfere with each other.

[0066] To ensure efficient elevator operation and prevent interference between users on different floors, real-time monitoring of independent channel usage is required. The real-time occupancy rate of each independent channel, calculated by the ratio of the user request queue length to the elevator capacity on each floor, is used to determine the level of elevator usage on each floor. The time-slice rotation period is determined based on the real-time occupancy rate, and a dynamic scheduling instruction is generated, including the target floor number and travel direction parameters. Upon receiving the instruction, the elevator activates the independent channel corresponding to the target floor number and monitors the response delay data for that independent channel in real time. If the response delay exceeds a preset threshold, indicating a potential problem with the current scheduling scheme, the time-slice rotation period is recalculated. The updated period is then used to update the dynamic scheduling instruction, execute motor drive operations, and adjust the elevator's operating status. This continuous monitoring, calculation, and adjustment ensures that users on different floors do not interfere with each other when using the segmented shared elevator, significantly improving elevator efficiency and the resident experience.

[0067] The modular design method for multi-story residential buildings with all-gardens begins by determining the ground space surrounding the multi-story building. Then, using an optimization algorithm based on zone partitioning, the ground space at the front, rear, and sides of the building is rationally divided, creating exclusive private garden areas for residents on each floor. For top-floor residents, a rooftop garden is also provided as a private space. Next, a deep learning-based location prediction model is used to determine the optimal elevator installation location within the private garden area. A separate shared elevator system for two households is constructed, and the optimal set of direct paths from the private garden area to the corresponding floor is generated. Data on the multi-door structure of the separate shared elevator is then obtained. Combined with the optimal set of direct paths, independent access channels are determined for users on each floor. Finally, the elevator's operating status is determined based on the usage of the independent channels. An algorithm switches between these states to ensure that users on different floors do not interfere with each other. It greatly improves the comfort and privacy of living, allowing residents to be closer to nature and enjoy the unique outdoor space, effectively improves the efficiency of elevator use, reduces residents' waiting time, avoids mutual interference between floors, improves the convenience and safety of living, and creates a better living environment for residents.

[0068] In one embodiment, the optimization algorithm for area division is used to divide the ground space on the front, side, and back sides to obtain the private garden areas for residents on each floor. The following steps may be included:

[0069] Step S201 , obtaining three-dimensional coordinate data of the front, rear and three-surface ground space; the three-dimensional coordinate data includes terrain undulations and obstacle distribution.

[0070] Step S202 : Generate an initial area grid based on the three-dimensional coordinate data and extract the constraints of the private garden area; the initial area grid includes the projection boundaries of each floor; the constraints include area thresholds and connectivity requirements.

[0071] Step S203 , iteratively dividing the initial region grid using a multi-objective optimization algorithm based on the constraint conditions to obtain a set of region boundary coordinates after division.

[0072] Step S204: Generate a three-dimensional visualization model based on the area boundary coordinate set, synchronize the three-dimensional visualization model with the resident terminal, and obtain real-time space status update information.

[0073] Step S205 , correcting the deviation value of the area division based on the real-time space status update information, and obtaining the private garden area of the residents on each floor according to the corrected area division result.

[0074] First, three-dimensional coordinate data for the front, back, and sides of a multi-story residential building is obtained. This data accurately captures the topography and the distribution of obstacles. Next, an initial regional mesh containing the projected boundaries of each floor is generated based on this 3D coordinate data. Constraints for private garden areas are also extracted from this data, such as setting thresholds for garden area to meet the needs of residents on different floors and ensuring good connectivity. Subsequently, a multi-objective optimization algorithm is used to iteratively partition the initial regional mesh based on these constraints, ultimately generating a set of coordinates for the partitioned regional boundaries. A 3D visualization model is generated from this set of coordinates and synchronized with resident terminals, allowing residents to receive real-time updates on the spatial status. Finally, deviations in the regional partitioning are corrected based on real-time spatial status updates, and the private garden areas for residents on each floor are determined based on the revised regional partitioning results.

[0075] Precise three-dimensional coordinate data provides accurate terrain and spatial information for garden area division, ensuring that the design fully considers actual site conditions. The multi-objective optimization algorithm combines constraints for iterative division, balancing multiple objectives such as area requirements and connectivity requirements, and improving the rationality of space utilization. The three-dimensional visualization model is synchronized with the data of the resident terminal, allowing residents to participate in the design process, update information feedback based on real-time space status, correct area division deviation values, and improve design accuracy and resident satisfaction. From the perspective of living experience, the reasonably divided private garden area provides residents on each floor with exclusive outdoor space. Good connectivity facilitates residents' access, improves living comfort and convenience, and lays a solid foundation for creating a high-quality living environment.

[0076] In one embodiment, using a deep learning location prediction model to determine the optimal installation location of an elevator in a private garden area and constructing a partitioned shared elevator for two households, thereby obtaining an optimal set of direct paths from the private garden area to the corresponding floor, may include the following steps:

[0077] Step S301: Obtain user movement trajectory data in the private garden area.

[0078] Step S302: Generate a visit frequency distribution matrix of each floor based on the user movement trajectory data.

[0079] Step S303: Obtain a building topology diagram of the private garden area and each floor; the building topology diagram includes floor connection relationships and path weight parameters.

[0080] Step S304: input the access frequency distribution matrix and the path weight parameters into the path optimization algorithm to generate a set of direct path weight coefficients.

[0081] Step S305 , constructing an elevator candidate location evaluation model based on the direct path weight coefficient set and the building topology map, and obtaining a set of compartmentalized layout solutions that meet the dual-household sharing condition.

[0082] Step S306: Use a convolutional neural network to process the compartmentalized layout solution set to obtain the elevator installation coordinates and the corresponding path connection matrix.

[0083] Step S307 : updating the floor accessibility parameters in the building topology map according to the path connection matrix to generate an optimal direct path set.

[0084] Specifically, user movement trajectory data for the private garden area is first collected. This data reflects residents' movement habits and frequent activity areas within the garden. Based on this data, a floor-by-floor visit frequency distribution matrix is generated, visually demonstrating the frequency of visits to different floors. Simultaneously, a building topology map of the private garden area and each floor is obtained. This map shows the connectivity between floors and path weight parameters, which are used to measure the quality of each connection path. Next, the visit frequency distribution matrix and path weight parameters are input into a path optimization algorithm, which generates a set of direct path weight coefficients. Based on this set and the building topology map, a candidate elevator location evaluation model is constructed to identify a set of compartmentalized layout options that meet the dual-household sharing requirement. For further optimization, a convolutional neural network is used to process these layout options, ultimately determining the precise elevator installation coordinates and the corresponding path connectivity matrix. Finally, the floor accessibility parameters within the building topology map are updated based on the path connectivity matrix to generate the optimal set of direct paths.

[0085] From the perspective of enhancing user experience, this embodiment analyzes user movement trajectory data to generate an optimal set of direct paths, allowing residents to quickly and conveniently reach their respective floors from their private gardens. This reduces unnecessary walking distance and waiting time, significantly improving convenience. Furthermore, the compartmentalized layout design, which meets the requirements for dual-family sharing, ensures efficient elevator utilization while maintaining residents' privacy and avoiding mutual interference. From the perspective of architectural design and planning, comprehensive analysis using multiple data and algorithms makes the determination of elevator locations more scientific and reasonable, optimizes the building's spatial layout, improves the overall residential space utilization and functionality, and enhances the market competitiveness of multi-story, all-garden residences.

[0086] In one embodiment, inputting the access frequency distribution matrix and the path weight parameters into the path optimization algorithm to generate a set of direct path weight coefficients may include the following steps:

[0087] Step S401 : normalize the access frequency distribution matrix to obtain node weight distribution.

[0088] Step S402: Input the node weight distribution and path weight parameters into the path optimization algorithm, calculate the link priority score and obtain the link priority sequence.

[0089] Step S403 : generating a transmission delay coefficient using a dynamic adjustment strategy according to the link priority sequence; the dynamic adjustment strategy iteratively updates the link weight based on a preset convergence threshold.

[0090] Step S404: The transmission delay coefficient is integrated with the redundancy index in the path weight parameter to obtain a set of direct path weight coefficients.

[0091] First, the obtained access frequency distribution matrix is normalized. This process standardizes the data in the matrix and eliminates dimensional differences between different data points, thereby obtaining a more comparable and analytically valuable node weight distribution. Once the node weight distribution is obtained, it is input into the path optimization algorithm along with the path weight parameters in the building topology diagram. Based on this input data, the algorithm calculates link priority scores and generates a link priority sequence. This sequence reflects the importance of different links in the overall path planning. Next, based on the link priority sequence, a dynamic adjustment strategy based on a preset convergence threshold is used to generate transmission delay coefficients. During this process, the algorithm iteratively updates the link weights until the convergence threshold is met. Finally, the generated transmission delay coefficients are combined with the redundancy index in the path weight parameters. After comprehensively considering various factors, a set of direct path weight coefficients is obtained.

[0092] By normalizing the data to make it more reasonable, calculating link priority scores and generating priority sequences, we can clearly distinguish the importance of different links, making path planning more targeted. Dynamic adjustment strategies are used to generate transmission delay coefficients, fully considering dynamic changes in actual use to ensure that path planning can adapt to different usage scenarios. The set of direct path weight coefficients, derived by integrating the transmission delay coefficient with the redundancy index, integrates multiple key factors, making the final planned elevator path more scientific and reasonable, effectively improving elevator operating efficiency and reducing residents' waiting time. Reasonable elevator path planning can optimize residents' travel experience between private gardens and floors, enhance the convenience and comfort of the residence, and improve the market appeal and competitiveness of multi-story residences with full gardens.

[0093] In one embodiment, the link priority score can be calculated using the following formula:

[0094]

[0095] Where L(p) represents the link priority score, γ represents the adjustment coefficient, and W k represents the weight of the kth path segment, len(P k ) represents the length of the kth path segment, h represents the number of segments the path is divided into, ω n Indicates the maximum weight value in the nth path node.

[0096] The formula in this embodiment comprehensively considers multiple factors such as path segment weight, length, number of path segments, and maximum node weight value, making the evaluation of link priority more comprehensive and accurate. Compared with the evaluation method of a single factor, it can more accurately reflect the actual value of the link in the entire path planning, and provide a solid data foundation for subsequent path optimization. From a practical application perspective, accurate link priority scoring helps to determine a more reasonable elevator operation path. For example, when planning the optimal path from a private garden to each floor, higher priority links can be quickly screened out based on these scores, reducing unnecessary path selection, improving elevator operation efficiency, and thereby improving residents' convenience in traveling between the garden and the floors, and enhancing the living experience of multi-story residences with full gardens.

[0097] In one embodiment, Figure 2 As shown, based on the usage of independent channels, the operating status of users on any floor using the segmented shared elevator is obtained, and the operating mode in which users on different floors do not interfere with each other is obtained by switching the operating status using an algorithm. The following steps may be included:

[0098] Step S501, obtaining the real-time occupancy rate of the independent channel; the real-time occupancy rate is calculated by the ratio of the length of the user request queue on each floor to the elevator capacity.

[0099] Step S502: determining the time slice rotation period according to the real-time occupancy rate and generating a dynamic scheduling instruction; the dynamic scheduling instruction includes a target floor number and a running direction parameter.

[0100] Step S503: Activate the independent channel of the corresponding floor using the dynamic scheduling instruction according to the target floor number, and monitor and obtain the response delay data of the independent channel.

[0101] When the response delay data exceeds a preset threshold, the time slice rotation period is recalculated to obtain an updated time slice rotation period.

[0102] Step S504 , updating the dynamic scheduling instruction according to the updated time slice rotation period to execute the motor driving operation, so as to obtain an operation mode in which users on different floors do not interfere with each other.

[0103] Specifically, the real-time occupancy rate of the independent channels is first obtained. This real-time occupancy rate is determined by calculating the ratio of the length of the user request queue on each floor to the elevator capacity. It provides a direct reflection of the demand for elevators on each floor. Based on the real-time occupancy rate, the time slice rotation period is further determined, and dynamic scheduling instructions are generated, including the target floor number and travel direction parameters. These instructions activate the independent channels on the corresponding floors based on the target floor number, while monitoring the response delay data of the independent channels. If the response delay data exceeds a preset threshold, it indicates that the current scheduling scheme may have efficiency issues. In this case, the time slice rotation period is recalculated to obtain an updated period. Finally, the dynamic scheduling instructions are adjusted based on the updated time slice rotation period, and motor drive operations are executed, thereby achieving an operation mode in which users on different floors do not interfere with each other when using the elevator.

[0104] This embodiment dynamically adjusts elevator operation scheduling by real-time monitoring of independent channel occupancy rates, better meeting the needs of residents on each floor, reducing wait times, and improving the convenience and comfort of elevator rides. This non-interference operation mode ensures residents' privacy and independence when using the elevator, enhancing the overall living experience. Timely recalculation of time slice rotation cycles based on response delay data optimizes dynamic scheduling instructions, avoiding idle elevator runs or congestion caused by improper scheduling. This improves elevator operating efficiency, reduces energy consumption, and extends elevator service life, providing strong support for the efficient operation of multi-story residential buildings throughout the garden.

[0105] In one embodiment, Figure 3 As shown, the floor plans of the first to seventh floors of the full-garden multi-story residential buildings are provided, including the following:

[0106] The spatial layout and structural form of a full-garden multi-story residential building.

[0107] Through the innovative integration of multi-story residential space layout, each household has its own private garden.

[0108] The ground space around the multi-story residential building is divided into three sides: front, side, and back, to create private gardens for residents on the first to fifth floors. A rooftop garden is also provided for residents on the sixth floor, ensuring that every household in the multi-story residential building has a garden.

[0109] Residents on the 2nd to 5th floors can directly reach their homes through the separate double-household shared elevator set up in the private garden. Adjacent residents sharing the same elevator will not interfere with each other, so that each household can enter the house from its own garden and go directly to the house from the garden elevator, reflecting the independence, exclusivity and convenience of the private garden, thereby greatly improving the living quality of multi-story residential buildings.

[0110] The control principle of segmented shared elevators.

[0111] When using an elevator, users on different floors enter the first floor from different directions of the same elevator and can only reach their respective floors. When users on one floor are going up or down, users on the other floor are temporarily suspended, thus achieving user separation and preventing them from bumping into each other. When one party is not using the elevator, they cannot restrict the other party's use. When using a double-door elevator, users enter from their own side of the elevator door or elevator car and can only reach their own floor and must exit from the same side. When using a three-door elevator, the elevator car has doors on all three sides except the counterweight side. Users enter and exit the elevator from their own side of the elevator door and can only reach their own floor, entering and exiting through the third door.

[0112] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0113] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the modular design method for a full-garden multi-story house as described above are implemented.

[0114] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0115] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0116] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. The modular design method of a full-garden multi-story residential building is characterized by: The method comprises: Obtaining ground space around multi-story residential buildings; Based on the optimization algorithm of area division, the ground space on the front, back and three sides is divided to obtain the private garden area of the residents on each floor; wherein, for the residents on the top floor, a roof garden is set on the roof as the private garden area; Using a deep learning location prediction model, the optimal installation location of an elevator in the private garden area is determined, and a partitioned shared elevator for two households is constructed to obtain an optimal set of direct paths from the private garden area to the corresponding floor. Acquiring multi-door structure data of the segmented shared elevator; determining independent channels for users on each floor to enter the segmented shared elevator using entrances in different directions of the multi-door structure and the optimal direct path set; Based on the usage of the independent channels, the operating status of users on any floor when using the segmented shared elevator is obtained, and the operating status is switched using an algorithm to obtain an operating mode in which users on different floors do not interfere with each other.

2. The method according to claim 1, characterized in that The optimization algorithm based on area division divides the ground space on the front, side and back sides to obtain the private garden area for residents on each floor, including: Obtaining three-dimensional coordinate data of the ground space on the front, side, and rear sides; the three-dimensional coordinate data includes terrain undulations and obstacle distribution; Generating an initial area grid based on the three-dimensional coordinate data and extracting the constraints of the private garden area; the initial area grid includes the projection boundaries of each floor; the constraints include area thresholds and connectivity requirements; Iteratively dividing the initial region grid using a multi-objective optimization algorithm based on the constraint conditions to obtain a set of region boundary coordinates after division; Generate a three-dimensional visualization model based on the area boundary coordinate set, synchronize the three-dimensional visualization model with the resident terminal to obtain real-time space status update information; The deviation value of the area division is corrected based on the real-time space status update information, and the private garden area of the residents on each floor is obtained according to the corrected area division result.

3. The method according to claim 1, characterized in that The method of using a deep learning location prediction model to determine the optimal installation location of an elevator in the private garden area and constructing a partitioned shared elevator for two households, thereby obtaining an optimal set of direct paths from the private garden area to the corresponding floor, includes: Obtaining user movement trajectory data of the private garden area; Generating a visit frequency distribution matrix for each floor according to the user movement trajectory data; Obtaining a building topology diagram of the private garden area and each floor; the building topology diagram includes floor connection relationships and path weight parameters; Inputting the access frequency distribution matrix and the path weight parameters into a path optimization algorithm to generate a set of direct path weight coefficients; Constructing an elevator candidate location evaluation model based on the direct path weight coefficient set and the building topology map to obtain a set of compartmentalized layout solutions that meet the dual-household sharing condition; Using a convolutional neural network to process the set of compartmentalized layout solutions to obtain elevator installation coordinates and corresponding path connection matrices; The floor accessibility parameters in the building topology are updated according to the path connection matrix to generate an optimal direct path set.

4. The method according to claim 3, characterized in that The step of inputting the access frequency distribution matrix and the path weight parameters into a path optimization algorithm to generate a set of direct path weight coefficients includes: Normalizing the access frequency distribution matrix to obtain node weight distribution; Inputting the node weight distribution and the path weight parameter into a path optimization algorithm, calculating a link priority score to obtain a link priority sequence; Generating a transmission delay coefficient using a dynamic adjustment strategy according to the link priority sequence; the dynamic adjustment strategy iteratively updates the link weight based on a preset convergence threshold; The transmission delay coefficient is combined with the redundancy index in the path weight parameter to obtain a set of direct path weight coefficients.

5. The method according to any one of claim 4, characterized in that The link priority score is calculated using the following formula: Where L(p) represents the link priority score, γ represents the adjustment coefficient, and W k represents the weight of the kth path segment, len(P k ) represents the length of the kth path segment, h represents the number of segments the path is divided into, ω n Indicates the maximum weight value in the nth path node.

6. The method according to any one of claim 1, characterized in that The method of obtaining the operating state of a user on any floor using the segmented shared elevator based on the usage of the independent channel and switching the operating state using an algorithm to obtain an operating mode in which users on different floors do not interfere with each other includes: Obtaining a real-time occupancy rate of the independent channel; the real-time occupancy rate is calculated by the ratio of the length of the user request queue on each floor to the elevator capacity; Determine the time slice rotation period according to the real-time occupancy rate and generate a dynamic scheduling instruction; the dynamic scheduling instruction includes a target floor number and a running direction parameter; activating the independent channel of the corresponding floor using the dynamic scheduling instruction according to the target floor number, and monitoring and obtaining response delay data of the independent channel; When the response delay data exceeds a preset threshold, the time slice rotation period is recalculated to obtain an updated time slice rotation period; The dynamic scheduling instruction is updated according to the updated time slice rotation period to execute the motor driving operation, thereby obtaining an operation mode in which users on different floors do not interfere with each other.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images