An interior design method, a terminal device, and a storage medium

Abstract

The application relates to the technical field of interior design, and discloses an interior design method, a terminal device and a storage medium. The method collects user design requests and space parameter data, then obtains a design environment corresponding relationship through a configuration server, and determines a design environment where a user design task is located based on the design environment corresponding relationship; in the design environment, an initial design model is generated by using a design model generation module; then, data verification is performed on the initial design model by using a data analysis server, and the initial design model is optimized to generate a final design model in combination with a resource idle rate provided by a resource monitoring module according to a data verification result; and finally, the final design model is output to a user terminal. The method guarantees the quality of the design model, optimizes resource utilization, solves problems such as poor demand matching, missing model verification and resource waste in traditional design, improves the accuracy, efficiency and economy of interior design, and meets the individualization and standardization requirements of modern interior design.

Description

Technical Field

[0001] This invention relates to the field of interior design technology, specifically to an interior design method, terminal equipment, and storage medium. Background Technology

[0002] As people's living standards improve, the demands for interior design are becoming increasingly diversified, with users placing ever higher demands on personalized spatial layout, style presentation, and functional adaptation. Currently, traditional methods in interior design largely rely on designers' experience and judgment, lacking systematic data support and standardized processes. During the user needs gathering phase, incomplete information is often a problem; designers merely record user preferences through simple communication, making it difficult to accurately capture potential user needs. Furthermore, the measurement and integration of spatial parameters are often done manually, which is prone to data errors, leading to deviations between the subsequent design direction and user expectations.

[0003] In the design environment determination stage, existing methods lack a unified system of corresponding design environments. Designers often divide design scenarios based on subjective experience, and the design standards for residential, office, and commercial spaces lack clear connections. This results in low reusability of design elements across different scenarios and limited design efficiency. In the initial design model generation stage, most tools only provide basic template application functions and cannot personalize the model based on specific user needs and spatial parameters. This leads to severe model homogenization and makes it difficult to meet users' pursuit of uniqueness.

[0004] After the initial model is generated, the lack of professional data analysis and verification means that the model's rationality, safety, and functionality (such as the smoothness of spatial circulation, the adequacy of lighting and ventilation, and the suitability of furniture dimensions) cannot be effectively verified. This often leads to problems being discovered during the construction phase, resulting in increased rework costs. Furthermore, the design process fails to consider resource idleness, such as material inventory, equipment usage status, and designer man-hours. Idle rates of these resources are not incorporated into model optimization, leading to resource waste. For example, if a certain type of material has sufficient inventory but a high idle rate, the existing design may still choose other scarce materials, increasing design costs; or if designer man-hours are idle, the design schedule is not adjusted in time, extending the overall design cycle. These problems collectively result in significant shortcomings in current interior design methods regarding user satisfaction, design quality, resource utilization, and process efficiency, making it difficult to adapt to the high-efficiency and precise development needs of the modern interior design industry. Summary of the Invention

[0005] The purpose of this invention is to provide an interior design method, terminal device, and storage medium to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an interior design method, a terminal device, and a storage medium, the method comprising:

[0007] Collect user design requests and spatial parameter data;

[0008] Based on the user's design request and spatial parameter data, the corresponding relationship of the design environment is obtained through the configuration server;

[0009] Based on the aforementioned design environment correspondence, the design environment in which the user's design task is located is determined;

[0010] In the design environment, an initial design model is generated using the design model generation module;

[0011] The initial design model is validated using a data analysis server.

[0012] Based on the data verification results and the resource idle rate provided by the resource monitoring module, the initial design model is optimized to generate the final design model;

[0013] The final design model is then output to the user terminal.

[0014] Preferably, the step of collecting user design requests and spatial parameter data includes:

[0015] Receive user input for design priority labels and space type labels;

[0016] Based on the historical design database, obtain the historical resource consumption data corresponding to the space type label;

[0017] Calculate the resource type label based on the historical resource consumption data;

[0018] Attach the design priority label and resource type label to the user's design request.

[0019] Preferably, the step of determining the design environment where the user's design task is located based on the design environment correspondence includes:

[0020] The user information in the user design request is parsed by the user routing device;

[0021] Query the user environment mapping table from the configuration server;

[0022] Based on the user information and user environment mapping table, user design tasks are assigned to the design environment server;

[0023] The design environment server includes a draft environment server, a preview environment server, and a production environment server.

[0024] Preferably, the step of generating the initial design model using the design model generation module includes:

[0025] Construct a three-dimensional spatial model based on the geometric and material properties in the spatial parameter data;

[0026] Environmental correction factors are introduced to adjust the material properties;

[0027] Based on the adjusted material properties, an initial design model is generated;

[0028] The environmental correction factor is derived from the temperature and humidity data provided by the environmental data acquisition module.

[0029] Preferably, the step of performing data verification on the initial design model through a data analysis server includes:

[0030] Extract the key design nodes from the initial design model;

[0031] Based on the aforementioned key design nodes, the design safety factor and design comfort factor are obtained;

[0032] The design safety factor and design comfort factor are verified using a data verification algorithm.

[0033] Generate data verification results and transmit them to the model optimization module.

[0034] Preferably, the step of optimizing the initial design model to generate the final design model based on the data verification results and the resource idle rate provided by the resource monitoring module includes:

[0035] When the resource monitoring module detects that the system resource idle rate is lower than the warning threshold, the high-concurrency design state is activated;

[0036] In the high-concurrency design state, user design tasks are sorted according to design priority labels;

[0037] Tasks matching resource type tags are prioritized based on resource idle rate;

[0038] The initial design model is adjusted using optimization algorithms to generate the final design model.

[0039] Preferably, the step of activating the high-concurrency design state includes:

[0040] The number of concurrent monitoring tasks;

[0041] A high concurrency warning is issued when the number of concurrent design tasks reaches the warning threshold or the system resource idle rate reaches the warning threshold.

[0042] Under the aforementioned high concurrency warning, the new user design task enters the waiting sequence;

[0043] According to the exit mechanism conditions, when the number of concurrent design tasks is lower than the number of exits and the system resource idle rate exceeds the exit threshold, the high-concurrency design state is terminated.

[0044] Preferably, the step of outputting the final design model to the user terminal includes:

[0045] The final design model is formatted by designing the output device;

[0046] Transmit the formatted final design model to the designated user terminal;

[0047] Update the historical design database to record resource consumption data.

[0048] Preferably, the present invention further includes a terminal device, comprising a processor and a memory; the processor is used to execute instructions stored in the memory to implement the above-described interior design method.

[0049] Preferably, the present invention further includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described interior design method.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This interior design methodology achieves a comprehensive optimization of traditional interior design models through a systematic process design. In the user needs and spatial parameter collection phase, it no longer relies on subjective manual recording. Instead, it integrates user design requests and spatial parameter data through standardized data collection methods, ensuring the completeness and accuracy of information acquisition. This allows subsequent design phases to accurately align with the user's actual needs, reducing design adjustments caused by misunderstandings of requirements, and making the final design scheme more in line with the user's lifestyle and aesthetic preferences.

[0052] During the design environment determination phase, the configuration server is used to obtain the corresponding relationships between design environments, breaking the limitations of traditional experience-based scenario division. By establishing a standardized design environment association system, design standards, element libraries, and process requirements for different space types can be effectively linked. Designers can quickly locate the specific environment to which the user's design task belongs, improving the accuracy of design scenario matching. At the same time, it promotes the reuse of design resources in different scenarios, reduces repetitive design work, and improves overall design efficiency.

[0053] In the initial design model generation stage, a design model generation module replaces the traditional template application method. This module can perform personalized calculations based on user needs and spatial parameters to generate an initial model that fits the specific scenario. This generation method avoids the problem of model homogenization, incorporating unique user functional requirements and style preferences, making the initial model more targeted and practical, and laying a solid foundation for subsequent optimization.

[0054] The introduction of a data analysis server provides professional data verification support for the initial design model. The verification process can cover multi-dimensional indicators of the model, such as the rationality of spatial layout, structural safety, and functional adaptability. Through data analysis, potential problems in the model can be identified in a timely manner, avoiding hidden dangers in the design scheme during subsequent construction or use, and ensuring the stability of design quality.

[0055] In the model optimization phase, by combining data verification results with the resource idle rate provided by the resource monitoring module, collaborative optimization of the design scheme and resource utilization was achieved. The resource monitoring module provides real-time feedback on the idle status of resources such as material inventory, equipment usage, and manpower hours. During the optimization process, resources with high idle rates can be prioritized. For example, if a certain type of decorative material has sufficient inventory and has been idle for a long time, the design scheme can adjust the material selection to utilize the idle resources; or when designers have idle manpower hours, the model optimization progress can be accelerated, shortening the design cycle. This optimization method not only reduces resource waste but also controls design costs and improves the economy of the design process.

[0056] The entire methodology, through modular and process-oriented design, achieves standardization across the entire chain from requirements gathering to solution output, reducing errors caused by manual intervention and improving the stability and replicability of the design process. The final output design model undergoes multiple rounds of verification and optimization, meeting users' personalized needs while also taking into account safety, functionality, and resource economy, providing users with higher-quality design solutions, and promoting the interior design industry towards a more efficient, precise, and environmentally friendly direction. Attached Figure Description

[0057] Figure 1 This is a schematic diagram illustrating the working principle of the interior design method described in this invention.

[0058] Figure 2 Workflow diagram for collecting user design requests and spatial parameter data;

[0059] Figure 3 A workflow diagram to determine the design environment in which the user's design task takes place;

[0060] Figure 4 A workflow diagram for generating the initial design model. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1 This invention provides an interior design method, a terminal device, and a storage medium, the method comprising:

[0063] The system collects user design requests and spatial parameter data. User design requests include the user's design preferences and needs, while spatial parameter data includes geometric attributes such as room dimensions, shape, and door / window locations, as well as existing material information. The configuration server uses this data to obtain the mapping relationship between design environments, which defines the configuration parameters and resource allocation rules for different design environments. Based on this mapping relationship, the system determines the specific design environment server to which the user's design task should be assigned. Within the design environment, the design model generation module uses the spatial parameter data to construct a 3D model and applies environmental data for adjustments, generating an initial design model. The data analysis server then performs data validation on the initial design model, checking whether the design safety and comfort coefficients meet the standards. After validation, the resource monitoring module provides system resource idle rate data. Based on the validation results, the initial design model is adjusted using optimization algorithms to generate the final design model. Finally, the design output device formats the final design model and transmits it to the user terminal, while simultaneously updating the historical design database to record the resource consumption data for this design. The entire process achieves efficient and adaptive interior design, optimizing task processing based on real-time resource conditions.

[0064] Example 1: See Figure 2 During the process of collecting user design requests and spatial parameter data, the system receives user-input design priority tags and space type tags through a graphical user interface. Design priority tags are typically presented as drop-down menus or sliders, allowing users to select "high," "medium," or "low" priority levels. For example, a user might select "high" priority for an urgent commercial project or "low" priority for a long-term renovation project of a private residence. Space type tags are entered through a category selector, allowing users to choose space types such as "residential living room," "open-plan office," "medical clinic," or "hotel lobby" from a predefined list. These tags are integrated as metadata with the user's core design request.

[0065] The system then accesses a historical design database, which stores records of tens of thousands of past design projects. Each record contains project metadata and a detailed snapshot of resource consumption. For example, when a user selects "open-plan office" as the space type label, the system executes a query to retrieve all historical projects tagged "open-plan office." The data from these projects indicates that such designs typically involve significant lighting calculations (high CPU consumption), storage of large layout models (high memory requirements), and frequent 3D rendering outputs (high network bandwidth usage). The system aggregates and analyzes this historical resource consumption data to identify consumption patterns. For "open-plan office," the analysis might reveal its resource consumption characteristics as "computationally high," as the complex workstation layout and lighting simulation require substantial processing power; while a "hotel lobby" project might be classified as "storage-intensive," as it typically contains numerous high-resolution textures and complex models.

[0066] Based on this analysis, the system calculates and assigns a resource type label. This label is not a simple classification, but rather a summary of expected resource needs. The calculation process does not rely on a single metric, but considers historical data across multiple dimensions, including average CPU usage time, peak memory usage, file storage size, and network traffic. The system uses a weighted decision logic to assign the most appropriate label. For example, if historical data shows that the "open-plan office" project consistently and significantly outperforms other metrics in CPU usage, it is labeled "high-computational"; if a space type exhibits high demand across multiple resource dimensions, it may be labeled "generally high-load."

[0067] The system appends design priority tags from the user's original input and system-generated resource type tags to the structured data of the user's design request. This process enriches the meaning of the user request, making it not only contain design intent but also carry key information about processing urgency and expected resource requirements. These appended tags provide crucial basis for intelligent allocation of computing environments and priority scheduling in subsequent steps. For example, a request with the tags "high" priority and "high computational demand" will be identified as a task that needs to be assigned to a server node with powerful processing capabilities as soon as possible. The entire implementation relies on the continuous accumulation and iterative analysis of historical data, enabling the allocation of resource type tags to increasingly accurately reflect the actual computational requirements of different types of design projects.

[0068] Example 2: See Figure 3In determining the design environment for a user's design task, the system parses received design requests using a user routing device. This device first extracts user information from the request, typically embedded in the request header or message body in a structured data format. For example, a request from an architectural design firm might contain fields such as user ID "Arch_Co_12345", account type "enterprise_premium", and the number of historical projects "127". The routing device uses parsing algorithms to identify these key fields, distinguishing user identity attributes, permission levels, and past behavioral patterns.

[0069] After parsing, the system sends a query request to the configuration server, accessing the user environment mapping table stored there. This mapping table is essentially a dynamic rule database that defines the correspondence between different user attributes and environment servers. Mapping rules might be expressed as follows: projects for the "enterprise_premium" user are assigned to the preview environment server by default; the first three projects for the "trial" user are assigned to the draft environment server; projects involving complex lighting calculations are preferentially assigned to the production environment server, etc. When the system executes the query, it performs pattern matching between the parsed user attributes and the rules in the mapping table.

[0070] Based on query results and real-time system status, the system makes task allocation decisions. Design environment servers are divided into three distinct categories: draft environment servers, equipped with basic computing resources, focus on quickly generating low-precision design prototypes; preview environment servers, equipped with medium-sized computing clusters, are capable of detailed rendering and preliminary effect demonstrations; and production environment servers possess the highest-specification hardware configurations for completing the final high-fidelity design output. The allocation algorithm considers not only user mapping rules but also factors such as the current load metrics of each environment server, task queue length, and estimated processing time.

[0071] For example, when the system receives a design request from an advanced enterprise user, a query of the user environment mapping table shows that this type of user is typically mapped to the preview environment server. However, the allocation algorithm detects that the task queue on the current preview environment server is saturated, while the production environment server happens to have idle resources. In this situation, the system may dynamically adjust the allocation strategy, temporarily promoting the task to the production environment server for processing, in order to optimize overall resource utilization and reduce user waiting time.

[0072] The entire environment determination process ensures consistency through a distributed transaction mechanism, guaranteeing that each design task is accurately routed to the most suitable processing environment. After allocation, the user routing unit records metadata about the allocation decision, including allocation time, target environment server identifier, and allocation rationale code. This data is written to the system log for subsequent analysis and auditing. The environment server selection result is also returned to the user request processing pipeline, providing execution context for subsequent design model generation steps. This dynamic environment allocation mechanism allows the system to flexibly adapt to different user needs and changes in real-time system status.

[0073] Example 3: See Figure 4 During the initial design model generation process in the design model generation module, the system first processes the geometric and material properties contained in the spatial parameter data. Geometric properties are imported through the Building Information Modeling (BIM) interface and include precise room dimensions, structural component coordinates, geometric parameters of door and window openings, and layout information of fixed equipment within the space. This data is converted into a parametric 3D mesh model, where each vertex contains position coordinates, normal vectors, and texture coordinates. Material properties are obtained from a material database, including physical properties such as the reflectivity of wall paint, the coefficient of friction of flooring, the elastic modulus of furniture wood, and the sound absorption properties of decorative fabrics.

[0074] The environmental data acquisition module collects temperature and humidity data in real time through an IoT sensor network deployed on the building site. Temperature sensors record changes in ambient temperature with an accuracy of 0.1 degrees Celsius, while humidity sensors measure relative humidity as a percentage. This data is updated every five minutes and transmitted to the system data processing center via an encrypted data transmission protocol. After preprocessing, the collected environmental data is converted into environmental correction factors, which are used to adjust material properties to reflect the impact of real-world environmental conditions on material performance.

[0075] The adjustment of material properties is calculated using the following formula:

[0076]

[0077] in: This indicates the adjusted material property value. Represents the original material property value. This represents the difference between the current temperature and the standard temperature (20 degrees Celsius). This indicates the difference between the current humidity and the standard humidity (50% relative humidity). It is the temperature influence coefficient, which indicates the sensitivity of a material property to temperature changes; This is the humidity effect coefficient, which indicates the sensitivity of a material's properties to changes in humidity. Different material types... and The coefficients are stored in a material property database; for example, the humidity influence coefficient for wood is usually higher than that for metal materials.

[0078] Based on the adjusted material properties, the system generates an initial design model. This model contains a complete geometric representation and material application information, where each surface is associated with environmentally modified material properties. The model generation process employs a progressive refinement algorithm, first constructing a basic geometric framework, then gradually adding detailed elements, and finally applying materials and textures.

[0079] When the data analysis server performs data verification on the initial design model, it first identifies key design nodes in the model using feature extraction algorithms. These nodes include structural load-bearing nodes, ventilation system nodes, lighting control nodes, and spatial transition nodes. Node identification is based on a comprehensive application of geometric feature analysis, topological relationship analysis, and functional semantic analysis. For example, load-bearing wall nodes are identified by analyzing wall thickness, connection relationships, and material strength parameters, while ventilation nodes are identified by analyzing opening locations and dimensions.

[0080] Based on the identified key design nodes, the system retrieves the corresponding design safety factors and design comfort factors from the design standard database. The safety factors include structural stability factors, fire safety factors, and emergency evacuation factors, which are calculated according to building codes and safety standards. The comfort factors include lighting uniformity factors, acoustic comfort factors, and spatial mobility factors, which are based on ergonomic and environmental psychology principles.

[0081] The data verification algorithm employs a multi-rule verification engine, comparing the calculated design coefficients with standard thresholds. The verification process includes multiple dimensions: structural safety verification checks whether the stress distribution of load-bearing nodes is within allowable limits; environmental comfort verification checks whether lighting and ventilation meet comfort requirements; and functional suitability verification checks whether the spatial layout meets usage needs. Each dimension has multi-level verification rules, including mandatory and recommended rules.

[0082] Issues discovered during the verification process are categorized, recorded, and detailed data verification reports are generated. These reports include problem descriptions, impact assessments, and suggested corrective actions. Verification results are transmitted to the model optimization module via a data interface, providing a basis for subsequent model optimization. The entire data verification process is asynchronous, ensuring that it does not affect the efficiency of the main design workflow while guaranteeing the accuracy and completeness of the verification results.

[0083] After receiving the verification results, the model optimization module schedules optimization tasks based on issue priority and resource availability. The optimization process may involve adjustments to geometry, changes in material selection, or a redesign of the system layout. Each optimization operation is recorded in the version history, allowing designers to review the optimization process. The final optimized solution is applied to the initial design model, producing design output that meets all verification criteria.

[0084] Example 4: During the process of optimizing the initial design model to generate the final design model, the system continuously receives real-time data streams from the resource monitoring module. This module collects various system resource indicators once per second, including CPU utilization, available memory capacity, GPU load, storage device read / write speed, and network bandwidth usage. These indicators are weighted and calculated to convert into a comprehensive resource idle rate, which is expressed as a percentage of the system's remaining processing capacity.

[0085] When the resource monitoring module detects that the system resource idle rate has dropped to a preset warning threshold, the system automatically activates a high-concurrency design state. This state indicates that the system is approaching its maximum processing capacity and requires special strategies to maintain operational efficiency. In high-concurrency mode, the system initiates a task priority management mechanism to reorder all queued and processed design tasks.

[0086] The sorting mechanism is based on the design priority label carried by each task. The system maintains a dynamic priority queue, moving tasks labeled "urgent" or "high priority" to the front of the queue, while "normal" or "low priority" tasks are postponed accordingly. This sorting not only considers the priority label itself, but also takes into account factors such as the task's waiting time and estimated processing time for a comprehensive evaluation.

[0087] During the task execution phase, the system selects the task type most suitable for the current resource status based on real-time resource idle rate. Resource type labels play a crucial role here, identifying the specific resource requirements of each task. By analyzing the remaining status of various resources, the system prioritizes tasks whose resource requirements best match the currently available resources (see Table 1).

[0088] Table 1: Task processing order under high concurrency conditions

[0089] Task Number Design priority label Resource type tags Resource idle rate matching degree Processing order T-2045 high High computational 92% 1 T-1987 high High storage type 85% 2 T-2102 middle Balanced 78% 3 T-2056 Low High computational 92% 4 T-1998 middle High storage type 85% 5

[0090] The optimization algorithms adjust the initial design model based on the data verification results. These algorithms analyze problems discovered during the verification process, such as insufficient structural strength, uneven lighting distribution, or low ventilation efficiency, and generate corresponding correction schemes. During runtime, the algorithms consider the current system resource status, employing computationally less demanding optimization strategies when resources are limited, and using more refined optimization methods when resources are sufficient. For example, when processing a large office space design project, data verification results show that the illuminance levels in some areas do not meet the standard requirements. Under resource constraints, the optimization algorithm might choose a simple solution of adjusting the position and angle of the light fixtures, while under sufficient resources, it would employ a comprehensive optimization scheme including ray tracing simulation.

[0091] The entire optimization process is iterative, with resource usage and optimization effectiveness reassessed after each round. The system dynamically adjusts the complexity of the optimization strategy by monitoring changes in resource utilization during the optimization process. When resource idle rate decreases further, the system may simplify the optimization process to reduce computational burden; while when resource conditions improve, more refined optimization methods will be adopted.

[0092] The generation of the final design model is a gradual process. While ensuring design quality, the system adjusts the optimization depth based on available resources. The optimized design model needs to be validated again using a simplified version of the data to confirm that the main issues have been resolved before it can be output. All operations throughout the optimization process are recorded in the system log, including the content of each optimization adjustment, the amount of resources used, and the evaluation data of the optimized effect.

[0093] Example 5: During system operation, the activation of the high-concurrency design state depends on continuous monitoring of the number of concurrent design tasks. The system uses a task scheduler to count the number of active design tasks in real time, including tasks being processed and tasks in the ready queue waiting for resource allocation. Monitoring data is updated every second and stored in the system status database. When the number of concurrent tasks reaches a preset warning threshold, the system triggers a status assessment process, which also considers real-time data on the system resource idle rate. If the resource idle rate also reaches the warning threshold, the system will issue a high-concurrency warning signal.

[0094] High-concurrency alerts are issued via a distributed messaging mechanism, broadcasting alert information to all relevant system modules and service nodes. The alert information includes current system status data, the alert level, and an estimated duration. Upon receiving an alert signal, the system ingress controller begins special processing of newly arriving user design requests. These new requests no longer immediately enter the processing pipeline but are redirected to a dedicated waiting sequence. This waiting sequence is managed using a priority queue data structure, where request ordering is based not only on their design priority label but also on multiple factors such as request arrival time, estimated processing time, and resource requirement type.

[0095] Tasks in the waiting sequence are paused, but the system periodically sends status notifications to the corresponding clients, informing them of their current queue position and estimated waiting time. The sequence management algorithm dynamically adjusts the task order; when a higher-priority task appears, the entire queue may be rearranged. Simultaneously, the system continuously monitors the progress of tasks being processed. For tasks already in the execution phase, the system attempts to optimize resource utilization efficiency and may adjust computational precision to speed up processing if necessary.

[0096] The conditions for exiting the high-concurrency design state include two core metrics: the number of concurrent design tasks must decrease below the exit threshold, and the system resource idle rate must recover and stabilize above the exit threshold. The system periodically checks these metrics, with the check frequency dynamically adjusted according to system load. When the conditions are met, the system generates a state-clearing command, which takes effect after a confirmation process. The state-clearing process is gradual, first restoring processing capacity for some new requests while continuing to monitor system state changes. Only after confirming stable system operation is the high-concurrency state completely cleared.

[0097] In the final design model output stage, the design output device standardizes the model data. This process includes model data serialization, format validation, and metadata appending. Serialization converts the internal data structure into standardized file formats that support common 3D model representation standards. Format validation ensures the generated files meet specifications, including checking data integrity, structural correctness, and compatibility. Metadata appending embeds relevant design process information, such as design time, resource usage, and environmental parameters, into the output file.

[0098] The formatted final design model is sent to the designated user terminal via a secure transmission protocol. The transmission process employs a chunked transmission mechanism, supporting breakpoint resumption and progress tracking. Upon completion of transmission, the system sends a delivery confirmation notification to the user terminal and awaits confirmation from the receiving end. If an interruption or error occurs during transmission, the system will automatically retry or degrade the transmission quality to ensure the accessibility of the basic design data.

[0099] The update and output processes of the historical design database are synchronized. Update records contain complete resource consumption details for each design task, including metrics such as processing time, memory usage, storage consumption, and network transmission volume. This data, after being categorized and aggregated, is added to the historical record, while relevant statistical indicators and trend data are updated simultaneously. Database updates employ transactional operations to ensure data consistency and integrity. Updated data can be immediately used for reference in subsequent design tasks, forming a data loop for continuous optimization. Logs generated throughout the output and update process are centrally stored for system performance analysis and operational auditing.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An interior design method characterized by, The method comprises the following steps: collecting user design request and space parameter data; obtaining a design environment corresponding relationship through a configuration server according to the user design request and the space parameter data; determining a design environment where a user design task is located based on the design environment corresponding relationship; generating an initial design model in the design environment by using a design model generation module; performing data verification on the initial design model by using a data analysis server; optimizing the initial design model to generate a final design model according to a data verification result and a resource idle rate provided by a resource monitoring module; outputting the final design model to a user terminal.

2. The method of claim 1, wherein, The step of collecting the user design request and the space parameter data comprises: receiving a design priority label and a space type label input by a user; obtaining historical resource consumption data corresponding to the space type label based on a historical design database; calculating a resource type label according to the historical resource consumption data; attaching the design priority label and the resource type label to the user design request.

3. The method of claim 2, wherein, The step of determining the design environment where the user design task is located based on the design environment corresponding relationship comprises: analyzing user information in the user design request by using a user routing device; querying a user environment mapping table from the configuration server; allocating the user design task to a design environment server based on the user information and the user environment mapping table; The design environment server comprises a draft environment server, a preview environment server and a production environment server.

4. The method of claim 3, wherein, The step of generating the initial design model by using the design model generation module comprises: constructing a three-dimensional space model according to geometric properties and material properties in the space parameter data; introducing an environment correction factor to adjust the material properties; generating the initial design model based on the adjusted material properties; The environment correction factor is derived from temperature data and humidity data provided by an environment data acquisition module.

5. The method of claim 4, wherein, The step of performing data verification on the initial design model by using the data analysis server comprises: extracting key design nodes in the initial design model; obtaining a design safety coefficient and a design comfort coefficient based on the key design nodes; verifying the design safety coefficient and the design comfort coefficient by using a data verification algorithm; generating a data verification result and transmitting the data verification result to a model optimization module.

6. The method of claim 5, wherein, The step of optimizing the initial design model to generate the final design model according to the data verification result and the resource idle rate provided by the resource monitoring module comprises: activating a high-concurrency design state when the resource monitoring module detects that a system resource idle rate is lower than a warning threshold; sorting user design tasks according to design priority labels in the high-concurrency design state; selecting tasks with matching resource type labels for priority processing based on the resource idle rate; adjusting the initial design model by using an optimization algorithm to generate the final design model.

7. The method of claim 6, wherein, The step of activating the high-concurrency design state comprises: monitoring the number of concurrent design tasks; issuing a high-concurrency warning when the number of concurrent design tasks reaches a warning number or a system resource idle rate reaches a warning threshold; putting new user design tasks into a waiting sequence under the high-concurrency warning; According to the exit mechanism condition, when the number of concurrent design tasks is less than the exit number and the system resource idle rate exceeds the exit threshold, the high concurrency design state is released.

8. The method of claim 7, wherein, The step of outputting the final design model to the user terminal comprises: formatting the final design model through a design output device; transmitting the formatted final design model to the designated user terminal; updating a historical design database to record resource consumption data.

9. A terminal device, comprising: The computer program is executed by the processor to implement the interior design method of any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the interior design method of any one of claims 1 to 8.

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