Interior design method, terminal equipment and storage medium

Abstract

The invention relates to the technical field of interior design, and discloses an interior design method, terminal equipment and a storage medium. The method comprises the steps of collecting a user design request and spatial parameter data, obtaining a design environment corresponding relation through a configuration server, and determining a design environment where a user design task is located based on the design environment corresponding relation; in the design environment, utilizing a design model generation module to generate an initial design model; and then executing data verification on the initial design model through the data analysis server, optimizing the initial design model in combination with a data verification result and a resource vacancy rate provided by the resource monitoring module to generate a final design model, and finally outputting the final design model to a user terminal. According to the method, the quality of the design model is guaranteed, resource utilization is optimized, the problems of poor demand matching, model verification deficiency, resource waste and the like in traditional design are solved, the accuracy, efficiency and economical efficiency of interior design are improved, and the individuation and standardization demands of modern interior design are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of interior design, in particular to an interior design method, a terminal device and a storage medium. BACKGROUND

[0002] With the improvement of people's living quality, the demand for interior design is increasingly diversified, and users' personalized requirements for space layout, style presentation and functional adaptation are continuously improved. In the current interior design process, traditional methods rely on the experience of designers, lack of systematic data support and standardized processes. In the user demand collection stage, there are problems of incomplete information acquisition, and designers can only record user preferences through simple communication, which is difficult to accurately capture users' potential needs. At the same time, the measurement and integration of space parameters are mostly done manually, which is easy to cause data errors, leading to the deviation of the subsequent design direction from the user's expectations. In the design environment determination link, the existing method does not establish a unified design environment corresponding relationship system, and the designer often divides the design scene according to subjective experience, such as the lack of clear association of design standards for residential, office, commercial and other spaces, resulting in low design element reuse in different scenes and limited design efficiency. In the initial design model generation stage, most tools only provide basic template application functions and cannot generate personalized models based on user-specific needs and space parameters, resulting in serious model homogeneity and difficulty in meeting users' pursuit of uniqueness. After the initial model is generated, there is a lack of professional data analysis and verification link, and the rationality, safety and functionality of the model (such as whether the space flow is smooth, whether the lighting and ventilation meet the standards, and whether the furniture size is suitable) cannot be effectively verified, often resulting in problems found in the construction phase of the design scheme, leading to increased rework costs. At the same time, the idle rate of resources such as material inventory, equipment usage status and designer working hours is not considered in the design process, resulting in resource waste. For example, when a certain type of material is in sufficient inventory but has a high idle rate, the existing design scheme may still choose other scarce materials, increasing design costs; or when the designer's working hours are idle, the design progress is not adjusted in time, prolonging the overall design cycle. These problems collectively result in obvious shortcomings in user satisfaction, design quality, resource utilization and process efficiency of the existing interior design method, making it difficult to meet the efficient and accurate development needs of the modern interior design industry. SUMMARY

[0003] The purpose of the present application is to provide an interior design method, a terminal device and a storage medium to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present application provides an interior design method, a terminal device and a storage medium, the method comprising: collecting user design requests and space parameter data; obtaining a design environment corresponding relationship by a configuration server according to the user design request and the space parameter data; determining a design environment where the user design task is located based on the design environment corresponding relationship; generating an initial design model by a design model generation module in the design environment; performing data verification on the initial design model by a data analysis server; optimizing the initial design model to generate a final design model according to a data verification result and an idle rate of resources provided by a resource monitoring module; outputting the final design model to a user terminal.

[0005] Preferably, 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.

[0006] Preferably, 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 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.

[0007] Preferably, the step of generating the initial design model by 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 collection module.

[0008] Preferably, the step of performing data verification on the initial design model by the data analysis server comprises: extracting key design nodes in the initial design model; obtain a design safety coefficient and a design comfort coefficient based on the key design node; verify the design safety coefficient and the design comfort coefficient through a data verification algorithm; generate a data verification result and transmit the data verification result to a model optimization module.

[0009] Preferably, the step of optimizing the initial design model to generate a final design model according to the data verification result and a resource idle rate provided by a resource monitoring module comprises: activate a high-concurrency design state when the resource monitoring module detects that the system resource idle rate is lower than a warning threshold; sort user design tasks according to design priority labels in the high-concurrency design state; select tasks with matching resource type labels for priority processing based on the resource idle rate; adjust the initial design model by using an optimization algorithm to generate the final design model.

[0010] Preferably, the step of activating the high-concurrency design state comprises: monitor the number of concurrent design tasks; issue a high-concurrency warning when the number of concurrent design tasks reaches a warning number or the system resource idle rate reaches a warning threshold; put new user design tasks into a waiting sequence in the high-concurrency warning state; remove the high-concurrency design state when the number of concurrent design tasks is lower than an exit number and the system resource idle rate is higher than an exit threshold according to an exit mechanism condition.

[0011] Preferably, the step of outputting the final design model to a user terminal comprises: format the final design model by using a design output device; transmit the formatted final design model to a designated user terminal; update a historical design database to record resource consumption data.

[0012] Preferably, the present application further comprises a terminal device comprising a processor and a memory; the processor is configured to execute instructions stored in the memory to implement the above-mentioned interior design method.

[0013] Preferably, the present application further comprises a computer-readable storage medium having a computer program stored thereon; the computer program is configured to be executed by a processor to implement the above-mentioned interior design method.

[0014] Compared with the prior art, the present application has the following beneficial effects: The interior design method realizes the overall optimization of the traditional interior design mode through a systematic process design. In the user demand and space parameter collection link, it is no longer dependent on manual subjective recording, but through standardized data collection to integrate user design requests and space parameter data, ensuring the integrity and accuracy of information acquisition, so that the subsequent design link can accurately connect the real needs of users, reduce the design adjustment caused by demand understanding deviation, and make the final design scheme more in line with the user's living habits and aesthetic preferences. In the design environment determination stage, the design environment corresponding relationship is obtained through the configuration server, breaking the traditional limitation of relying on experience to divide scenes. By establishing a standardized design environment association system, the design standards, element library and process requirements of different space types are effectively associated, designers can quickly locate the specific environment of the user design task, improve the accuracy of design scene matching, and promote the reuse of design resources in different scenes, reduce repeated design work, and improve the overall design efficiency. In the initial design model generation link, the design model generation module replaces the traditional template application method. The module can be personalized based on user demand and space parameters to generate an initial model that meets the specific scene. This generation method avoids model homogenization, can integrate the user's unique functional requirements and style preferences, and makes the initial model more targeted and practical, laying a good foundation for subsequent optimization.

[0015] The introduction of the data analysis server provides professional data verification support for the initial design model. The verification process can cover multiple dimensions of the model, such as the rationality of space layout, structural safety, and functional adaptability. Through data analysis, potential problems in the model can be found in time to avoid hidden dangers in the subsequent construction or use stage, ensuring the stability of design quality. In the model optimization link, combined with the data verification results and the resource idle rate provided by the resource monitoring module, the design scheme and resource utilization are optimized. The resource monitoring module provides real-time feedback on the idle conditions of material inventory, equipment usage, and human working hours. In the optimization process, resources with high idle rates can be preferentially selected, such as when a certain type of decorative material is in stock and has been idle for a long time, the design scheme can adjust the material selection to digest the idle resources; or when the designer's working hours are idle, the model optimization progress can be accelerated, and the design cycle can be shortened. This optimization method not only reduces resource waste, but also controls design costs and improves the economic efficiency of the design process. The whole method realizes the full-link standardization from requirement collection to scheme output through modular and process design, reduces the error caused by manual intervention, and improves the stability and replicability of the design process. The final output design model is checked and optimized for multiple rounds, meets the personalized needs of users, and takes into account safety, functionality and resource economy, providing users with better design schemes, and promoting the development of the interior design industry to be more efficient, accurate and environmentally friendly. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A working principle diagram of the interior design method described in the present application; Figure 2 A working flowchart for collecting user design requests and space parameter data; Figure 3 A working flowchart for determining the design environment of the user design task; Figure 4 A working flowchart for generating an initial design model. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] Please refer to Figure 1 The present application provides an interior design method, a terminal device and a storage medium, the method comprising: The user design request includes the user's design preferences and requirements, and the space parameter data includes geometric properties such as room size, shape, door and window location, and existing material information. The configuration server obtains the design environment corresponding relationship according to these data, which defines the configuration parameters and resource allocation rules of different design environments. Based on this corresponding relationship, the system determines the specific design environment server to which the user design task should be allocated. In the design environment, the design model generation module constructs a three-dimensional model using the space parameter data and adjusts it using the environment data to generate an initial design model. The data analysis server then performs data verification on the initial design model to check whether the design safety factor and comfort factor meet the standard. After verification, the resource monitoring module provides the idle rate data of system resources, combined with the verification result, and adjusts the initial design model through an optimization algorithm to generate a final design model. Finally, the design output device formats and transmits the final design model to the user terminal, and updates the historical design database to record the resource consumption data of this design. The whole process realizes efficient and adaptive interior design, which can optimize task processing according to real-time resource status.

[0019] Embodiment 1: see Figure 2 In the process of collecting user design request and space parameter data, the system receives the design priority label and space type label input by the user through the graphical user interface. The design priority label is usually in the form of a drop-down menu or a slider, allowing the user to select "high", "medium" or "low" priority levels. For example, the user may choose "high" priority for an urgent business project, or "low" priority for a long-term renovation project of a personal residence. The space type label is input through a category selector, and the user can choose a space type such as "living room", "open office", "medical clinic" or "hotel lobby" from a predefined list. These labels are combined with the user's core design request as metadata.

[0020] The system then accesses a historical design database, which stores records of thousands of past completed design projects. Each record contains project metadata as well as detailed resource consumption snapshots. For example, when the user selects "open office" as a space type tag, the system performs a query to retrieve all historical projects tagged as "open 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 output (high network bandwidth usage). The system performs an aggregate analysis of these historical resource consumption data to identify consumption patterns. For "open office", the analysis might reveal that its resource consumption characteristics belong to the "high computation" type, as the complex workstation layout and lighting simulation require significant processing power; while a "hotel lobby" project might be classified as "high storage" type, as it typically contains large amounts of high-resolution textures and complex models.

[0021] Based on this analysis, the system calculates and assigns a resource type tag. This tag is not a simple classification, but rather a generalization of the expected resource requirements. The calculation process does not rely on a single metric, but rather considers historical data in multiple dimensions, including average CPU occupancy time, peak memory usage, file storage size, and network transfer volume. The system uses a weighted decision logic to assign the most appropriate tag. For example, if historical data shows that "open office" projects consistently have significantly higher CPU usage than other metrics, then the "high computation" tag is assigned; if a space type exhibits high demand across multiple resource dimensions, then the "comprehensive high load" tag might be assigned.

[0022] The system appends both the user's original input design priority tag and the system-generated resource type tag to the structured data of the user's design request. This process enriches the user's request, making it not only contain the design intent, but also carry key information about the urgency and expected resource requirements. These additional tags provide crucial basis for intelligent allocation of computing environments and priority scheduling in subsequent steps. For example, a request with "high" priority and "high computation" tag would be identified as a task that needs to be allocated to a server node with strong processing power as soon as possible. The entire implementation relies on the continuous accumulation and iterative analysis of historical data, enabling the resource type tag assignment to increasingly accurately reflect the actual computing demand characteristics of different types of design projects.

[0023] Embodiment 2: Refer to Figure 3In determining the design environment where the user's design task resides, the system parses the received design request through a user routing apparatus. The apparatus first extracts user information contained in the request, which is usually embedded in a structured data format in the request header or message body. For example, a request from an architectural design company might contain fields such as user ID "Arch_Co_12345", account type "enterprise_premium", and number of historical projects "127". The routing apparatus uses parsing algorithms to identify these key fields, distinguishing user identity attributes, permission levels, and past behavior patterns.

[0024] After parsing, the system initiates 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 state: "enterprise_premium" users' projects are assigned to preview environment servers by default; "trial" users' first three projects are assigned to draft environment servers; projects involving complex light and shadow calculations are preferentially assigned to production environment servers, etc. When the system performs a query, it performs pattern matching between the parsed user attributes and the rules in the mapping table.

[0025] Based on the 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 are configured with basic computing resources, focusing on quickly generating low-precision design prototypes; preview environment servers are equipped with medium-scale computing clusters, capable of detailed rendering and preliminary effect display; production environment servers have the highest specification hardware configuration, used to complete final high-fidelity design output. The allocation algorithm not only considers user mapping rules, but also takes into account factors such as the current load of each environment server, task queue length, and estimated processing time.

[0026] For example, when the system receives a design request from a senior enterprise user, the user environment mapping table query shows that this type of user is usually mapped to a preview environment server. However, the allocation algorithm detects that the task queue of the current preview environment server has reached a saturated state, while the production environment server has idle resources. In this case, the system may dynamically adjust the allocation strategy to temporarily promote the task to the production environment server for processing, in order to optimize overall resource utilization and reduce user waiting time.

[0027] The entire environment determination process is guaranteed consistency through a distributed transaction mechanism, ensuring that each design task can be accurately routed to the most suitable processing environment. After completing the allocation, the user routing device will record the metadata of the allocation decision, including allocation time, target environment server identifier and allocation reason code, which are written into the system log for subsequent analysis and audit use. The selection result of the environment server is also returned to the user request processing pipeline, providing the execution context for the subsequent design model generation step. This dynamic environment allocation mechanism enables the system to flexibly adapt to different user needs and real-time system state changes.

[0028] Embodiment 3: refer to Figure 4 In the process of generating the initial design model by the design model generation module, the system first processes the geometric properties and material properties contained in the spatial parameter data. Geometric properties are imported through the building information model interface, including precise size data of rooms, position coordinates of structural members, geometric parameters of door and window openings, and layout information of fixed equipment within the space. These data are converted into a parameterized three-dimensional grid model, where each vertex contains position coordinates, normal vector and texture coordinate information. Material properties are obtained from the material database, including the reflectivity of wall paint, the friction coefficient of the floor, the elastic modulus of furniture wood, and the sound absorption performance of decorative fabrics, etc. Physical properties.

[0029] The environment data acquisition module collects temperature data and humidity data in real time through the Internet of Things sensor network deployed on the construction site. Temperature sensors record environmental temperature changes with an accuracy of 0.1 degrees Celsius, and humidity sensors measure air relative humidity in percentage form. These data are updated every five minutes and sent to the system data processing center through an encrypted data transmission protocol. The collected environmental data is preprocessed and converted into environmental correction factors, which are used to adjust material properties to reflect the impact of real environmental conditions on material performance.

[0030] The adjustment of material properties is calculated by the following formula: Where: represents the adjusted material property value, represents the original material property value, represents the difference between the current temperature and the standard temperature (20 degrees Celsius), represents the difference between the current humidity and the standard humidity (50% relative humidity). is the temperature influence coefficient, which represents the sensitivity of material properties to temperature changes; is the humidity influence coefficient, which represents the sensitivity of material properties to humidity changes. Different material types have different and The coefficients are stored in a material properties database, for example, the humidity influence coefficient of wood is usually higher than that of metal materials.

[0031] 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 environment-corrected 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.

[0032] When the data analysis server performs data verification on the initial design model, it first identifies key design nodes in the model through 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 the comprehensive use 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, and ventilation nodes are identified by analyzing opening position and size.

[0033] Based on the identified key design nodes, the system obtains the corresponding design safety coefficients and design comfort coefficients from the design standard database. Safety coefficients include structural stability coefficients, fire safety coefficients, and emergency evacuation coefficients, which are calculated according to building codes and safety standards. Comfort coefficients include daylight uniformity coefficients, acoustic comfort coefficients, and spatial flowability coefficients, which are based on human engineering and environmental psychology principles.

[0034] The data verification algorithm uses a multi-rule verification engine to compare the calculated design coefficients with standard thresholds. The verification process includes multiple verification dimensions: structural safety dimension verifies whether the stress distribution of load-bearing nodes is within the allowed range, environmental comfort dimension verifies whether lighting and ventilation indicators meet comfort requirements, and functional applicability dimension verifies whether the spatial layout meets usage requirements. Each dimension has multiple verification rules, including mandatory rules and recommended rules.

[0035] Problems found during the verification process are classified and recorded, and a detailed data verification report is generated. The report contains problem descriptions, impact assessments, and correction suggestions. The verification results are transmitted to the model optimization module through a data interface, providing a basis for subsequent model optimization. The entire data verification process uses asynchronous processing, which does not affect the running efficiency of the main design process, while ensuring the accuracy and completeness of the verification results.

[0036] The model optimization module receives the verification results and schedules optimization tasks based on problem priority and resource availability. The optimization process can involve adjustments to the geometric structure, changes to material selection, or re-planning of system layout. Each optimization operation is recorded in the version history, allowing designers to trace back and review the optimization process. The final optimized solution is applied to the initial design model, resulting in a design output that meets all verification criteria.

[0037] In the process of optimizing the initial design model to generate the final design model, the system continuously receives a real-time data stream from the resource monitoring module. This module collects system resource indicators at a frequency of once per second, including the usage rate of the central processor, the available capacity of the memory, the load of the graphics processor, the read-write speed of the storage device, and the occupation of the network bandwidth. These indicators are converted into a comprehensive resource idle rate after weighted calculation, which is a value expressed in percentage form representing the remaining processing capacity of the current system.

[0038] When the resource monitoring module detects that the system resource idle rate drops to the preset warning threshold, the system automatically activates the high-concurrency design state. This state indicates that the system is approaching its maximum processing capacity and special strategies need to be taken to maintain operational efficiency. In the high-concurrency state, the system starts the task priority management mechanism, which reorders all design tasks that are queuing and being processed.

[0039] The sorting mechanism is based on the design priority labels 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 delayed accordingly. This sorting not only considers the priority label itself, but also takes into account factors such as task waiting time, estimated processing time, and other factors for comprehensive evaluation.

[0040] In the task execution phase, the system selects the most suitable task type for the current resource state based on the real-time resource idle rate. Resource type labels play a key role in this process, identifying the demand characteristics of each task for specific resources. By analyzing the remaining resources of each type, the system prioritizes tasks whose resource requirements best match the current idle resources, as shown in Table 1.

[0041] Table 1: Task processing order in high-concurrency state Task number Design priority label Resource type label Resource idle rate matching degree Processing order T-2045 High High computing type 92% 1 T-1987 High High storage type 85% 2 T-2102 Medium Balanced type 78% 3 T-2056 Low High computing type 92% 4 T-1998 Medium High storage type 85% 5 The optimization algorithm adjusts the initial design model according to the data verification results. These algorithms analyze the problems found during the verification process, such as insufficient structural strength, uneven lighting distribution, or low ventilation efficiency, and generate corresponding correction schemes. The algorithm considers the current system resource status during operation. In resource-constrained situations, it uses optimization strategies with smaller computational complexity, while in resource-abundant situations, it uses more detailed optimization methods. For example, when processing a large office space design project, the data verification results show that the illumination level in some areas does not meet the standard requirements. The optimization algorithm may choose a simple solution to adjust the position and angle of the lamps in a resource-constrained situation, while in a resource-abundant situation, it may use a comprehensive optimization scheme that includes ray tracing simulation.

[0042] The entire optimization process is carried out iteratively, and the resource usage and optimization effect are re-evaluated after each round of optimization. The system dynamically adjusts the complexity of the optimization strategy by monitoring the change in resource usage during the optimization process. When the resource idle rate further decreases, the system may simplify the optimization process to reduce the computational burden; when the resource situation improves, it may use more detailed optimization methods.

[0043] The generation of the final design model is a gradual process. The system adjusts the optimization depth according to the available resources while ensuring the quality of the design. The optimized design model needs to be re-verified by the simplified data verification process to confirm that the main problems have been solved before it can be output. All operations during the entire optimization process are recorded in the system log, including the content of each optimization adjustment, the amount of resources used, and the effect evaluation data after optimization.

[0044] Example 5: During the system operation, the activation of the high-concurrency design state depends on the continuous monitoring of the number of concurrent design tasks. The system uses the task scheduler to real-time count the number of active design tasks, which includes tasks being processed and tasks in the ready queue waiting for resource allocation. The monitoring data is updated at a frequency of seconds and stored in the system state database. When the number of concurrent tasks reaches the preset warning number value, the system triggers the state evaluation process, which also considers the real-time data of the system resource idle rate. If the resource idle rate also reaches the warning threshold, the system will issue a high-concurrency warning signal.

[0045] The high-concurrency warning is released through a distributed message mechanism, and the warning information is broadcast to all relevant system modules and service nodes. The warning information includes current system state data, warning level, and expected duration estimate. After receiving the warning signal, the system entry controller begins special processing for newly arrived user design requests. These new requests are no longer immediately entered into the processing pipeline, but are redirected to a special waiting sequence. The waiting sequence uses a priority queue data structure for management, where the ordering of requests is based not only on their design priority label, but also considers multiple factors such as request arrival time, estimated processing time, and resource demand type.

[0046] Tasks in the waiting sequence are in a suspended state, but the system regularly sends status notifications to the corresponding client, informing the current queue position and estimated waiting time. The sequence management algorithm dynamically adjusts the task order, and when a higher priority task appears, the entire queue may be rearranged. At the same time, the system continuously monitors the progress of tasks being processed, and for tasks that have entered the execution phase, the system attempts to optimize resource usage efficiency, and may adjust the calculation accuracy as necessary to speed up processing.

[0047] The conditions for exiting the high-concurrency design state include two core indicators: the number of concurrent design tasks must fall below the exit number value, and the system resource idle rate must rise and stabilize above the exit threshold. The system periodically checks these indicators, and the check frequency is dynamically adjusted according to the system load status. When the conditions are detected to be met, the system generates a state release instruction, which is confirmed through a confirmation process before it takes effect. The state release process uses a gradual approach, first restoring some of the processing capabilities for new requests, while continuing to monitor system state changes, and only when the system can operate stably, does it completely release the high-concurrency state.

[0048] In the output stage of the final design model, the design output device performs standardization processing on the model data. The processing process includes serialization of model data, format verification, and metadata addition. The serialization process converts internal data structures into standardized file formats that support common three-dimensional model representation standards. Format verification ensures that the generated file meets the specification requirements, including checking data integrity, structure correctness, and compatibility. The metadata addition operation embeds relevant information from the design process, such as design time, resource usage, environmental parameters, etc., into the output file.

[0049] The formatted final design model is sent to the designated user terminal through a secure transmission protocol. The transmission process uses a block transmission mechanism, supporting breakpoint resume and progress query. After transmission is complete, the system sends a delivery confirmation notification to the user terminal and waits for the receiving end's confirmation reply. If an interruption or error occurs during transmission, the system will automatically retry or degrade transmission quality to ensure the accessibility of basic design data.

[0050] The update operation of the historical design database is synchronized with the output process. The update record contains the complete resource consumption details of the current design task, including processing time, memory usage, storage occupation, and network transmission volume. These data are classified and aggregated and added to the historical record, while updating the relevant statistical indicators and trend data. The database update adopts transactional operation to ensure data consistency and integrity. The updated data can be immediately used for reference in subsequent design tasks, forming a continuous optimization data closed loop. The log records generated during the entire output and update process are stored centrally for system performance analysis and operation audit.

[0051] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one from another entity or action without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0052] While embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, combinations, and variations of the embodiments can be made by those skilled in the art without departing from the spirit and scope of the present application, 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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