Indoor ecological environment design method, system and storage medium based on digital twin

Abstract

The present invention discloses a digital twin-based indoor ecological environment design method, system, and storage medium, relating to the technical field of indoor environment design. This digital twin-based indoor ecological environment design method constructs an indoor digital twin model that includes spatial structure, material properties, and equipment layout, periodically acquires and updates ecological environment data, and analyzes and generates a comprehensive ecological status index. When the index deviates from a preset range, a multi-scheme control simulation is executed in the twin model, and after selecting the standard scheme, the equipment is driven to execute, achieving accurate identification and stable control of ecological anomalies. The present invention introduces a spatial comfort index, an air load index, and an ecological restoration index, respectively, to establish models based on multiple ecological parameters, and construct a comprehensive ecological status index, achieving a unified quantitative assessment of the indoor ecological environment, solving the existing problem of perceptual data fragmentation, and improving the normalization, accuracy, and cross-scenario applicability of ecological judgment.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor environment design, and specifically to an indoor ecological environment design method, system and storage medium based on digital twins. Background Art

[0002] With the acceleration of urbanization and rising demands for a better living environment, indoor ecological environments have become a key focus in building design and operational management. Traditional indoor environmental control systems primarily rely on air conditioning, ventilation, lighting, and other devices to independently adjust temperature, humidity, air quality, and light conditions. These controls are typically based on threshold settings or simple feedback mechanisms, making it difficult to achieve coordinated optimization across multiple environmental factors and to reflect the impact of building structure, material properties, and space usage on environmental conditions.

[0003] In recent years, with the development of technologies such as the Internet of Things (IoT), sensor networks, and Building Information Modeling (BIM), the application of digital twins in the construction sector has gradually expanded. Digital twins can map the physical structure, equipment layout, and dynamic environmental data of real spaces into virtual models, enabling full-cycle, real-time visualization and simulation prediction of building spaces, providing new technical support for intelligent environmental management. In the field of indoor environmental control, research has attempted to combine digital twins with technologies such as building energy consumption simulation and air flow analysis to develop intelligent system designs for energy-saving control and comfort improvement.

[0004] Existing technology, such as the invention patent application with publication number CN118364721A, discloses a digital twin-based indoor ecological environment design method, system, and storage medium. The main technical solution of this application is to collect three-dimensional indoor spatial environmental data; construct a digital twin model of the interior based on this three-dimensional spatial environmental data, and use the digital twin model to simulate the indoor ecological environment throughout the entire cycle; determine the target environmental design strategy within the digital twin model based on preset user needs; and use the target environmental design strategy to dynamically control indoor smart devices. The main purpose of this application is to accurately simulate a realistic indoor ecological environment and improve the quality of indoor ecological environment design.

[0005] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. When formulating indoor ecological environment control strategies, the existing technology generally lacks a pre-control simulation verification mechanism, that is, once the control strategy is generated, it is directly sent to the equipment system for execution, and its environmental response effect is not predicted and evaluated based on the digital twin model before execution. This method can easily lead to uncontrollable control behavior results, especially under the interaction of multi-source physical factors, such as spatial thermal and humidity inertia, air flow path and material thermal response. The control results may deviate significantly from expectations. In addition, the strategy is directly applied without simulation verification, which can easily cause drastic fluctuations in indoor ecological conditions, increased energy consumption or decreased user experience. Long-term operation may also cause reduced system stability and the risk of strategy failure. More importantly, the lack of a simulation evaluation mechanism makes it difficult for the system to achieve closed-loop optimization of the control logic, and it is also difficult to learn strategies and improve accuracy based on historical execution results. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides an indoor ecological environment design method, system and storage medium based on digital twins, which solves the problem that the existing technology lacks a simulation verification mechanism before the execution of the ecological control strategy, which easily leads to unpredictable control results and unstable execution effects.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an indoor ecological environment design method based on digital twins, comprising the following steps: obtaining spatial structure data, building material data, and equipment layout data of the indoor space, and establishing a digital twin model of the indoor space; based on a set time period, obtaining the ecological environment data of the indoor space at intervals, and updating it to the digital twin model of the indoor space; based on the ecological environment data in the digital twin model of the indoor space, analyzing the comprehensive ecological status index of the indoor space, and performing judgment analysis with the preset ecological status assessment interval; when the comprehensive ecological status index of the indoor space is outside the preset ecological status assessment interval, it is regarded as an abnormal ecological status of the indoor space, and an ecological regulation simulation analysis is performed in the digital twin model of the indoor space; when the results of the ecological regulation simulation analysis meet the expected standards, ecological regulation measures are taken for the indoor space.

[0008] Furthermore, the ecological environment data includes comfort perception data, air load data, and environmental regeneration data. The specific steps for analyzing the comprehensive ecological status index of the indoor space based on the ecological environment data in the digital twin model of the indoor space are as follows: based on the comfort perception data, air load data, and environmental regeneration data in the digital twin model of the indoor space, analyze the ecological environment index set of the indoor space, the ecological environment index set includes the space comfort index, air load index, and ecological restoration index; conduct a comprehensive analysis of the ecological environment index set of the indoor space to obtain the comprehensive ecological status index of the indoor space.

[0009] Furthermore, the specific formula for calculating the comprehensive ecological status index of indoor space is as follows: ;in, is the comprehensive ecological status index of the indoor space, is the spatial comfort index of the indoor space, is the space comfort adjustment coefficient stored in the database, is the air load index of the indoor space, is the air load adjustment coefficient stored in the database, is the ecological restoration index of indoor space, It is the ecological restoration adjustment coefficient stored in the database.

[0010] Furthermore, the comfort perception data includes indoor temperature values, indoor humidity values, and indoor light intensity values. The specific steps for analyzing the spatial comfort index of the indoor space are as follows: obtaining the comfort perception parameter set and the comfort perception allowable deviation set of the indoor space, the comfort perception parameter set including the indoor temperature parameter value, the indoor humidity parameter value, and the indoor light intensity parameter value, and the comfort perception allowable deviation set including the maximum allowable deviation of the indoor temperature, the maximum allowable deviation of the indoor humidity, and the maximum allowable deviation of the indoor light intensity; comprehensively analyzing the comfort perception data in the digital twin model of the indoor space in combination with the comfort perception parameter set and the comfort perception allowable deviation set of the indoor space to obtain the spatial comfort index of the indoor space.

[0011] Furthermore, the air load data includes indoor carbon dioxide concentration values, indoor particulate matter concentration values, indoor volatile organic compound concentration values, and indoor wind speed values. The specific steps for analyzing the air load index of the indoor space are as follows: obtaining the air load parameter set of the indoor space, the air load parameter set including the indoor carbon dioxide concentration parameter value, the indoor particulate matter concentration parameter value, the indoor volatile organic compound concentration parameter value, and the indoor wind speed parameter value; comprehensively analyzing the air load data in the digital twin model of the indoor space in combination with the air load parameter set of the indoor space to obtain the air load index of the indoor space.

[0012] Furthermore, the specific formula for calculating the air load index of the indoor space is as follows: ;in, is the air load index of the indoor space, is the indoor carbon dioxide concentration value in the digital twin model of the indoor space, is the reference value of indoor carbon dioxide concentration in indoor space, is the indoor particulate matter concentration value in the digital twin model of the indoor space, is the indoor particulate matter concentration parameter value of indoor space, is the indoor volatile organic compound concentration value in the digital twin model of the indoor space, is the reference value of indoor volatile organic compound concentration in indoor space, is the pollutant concentration adjustment coefficient stored in the database, is the indoor wind speed value in the digital twin model of the indoor space, is the indoor wind speed parameter value of the indoor space, is the wind speed adjustment coefficient stored in the database.

[0013] Furthermore, the environmental regeneration data includes the transpiration rate value of indoor plants and the thermal lag time of the wall. The specific steps for analyzing the ecological restoration index of the indoor space are as follows: obtaining the maximum allowable thermal lag parameter value of the indoor space; comprehensively analyzing the environmental regeneration data in the digital twin model of the indoor space in combination with the maximum allowable thermal lag parameter value of the indoor space to obtain the ecological restoration index of the indoor space.

[0014] Furthermore, the specific steps for conducting ecological regulation simulation analysis in the digital twin model of indoor space are as follows: based on the comprehensive ecological status index of the indoor space, identify the abnormal factor set and use it as the factors to be regulated; construct several groups of controllable factor combinations, each group is a type of strategy configuration; initialize the regulation simulation process in the digital twin model of the indoor space; execute the regulation scheme simulation in parallel in the digital twin model of the indoor space, and screen the optimal regulation simulation results.

[0015] The indoor ecological environment design system based on digital twins includes: a model building unit, which is used to obtain spatial structure data, building material data, and equipment layout data of the indoor space, and establish a digital twin model of the indoor space; an acquisition and update unit, which is used to obtain the ecological environment data of the indoor space at intervals based on a set time period, and update it to the digital twin model of the indoor space; an ecological environment analysis unit, which is used to analyze the comprehensive ecological status index of the indoor space based on the ecological environment data in the digital twin model of the indoor space; an ecological environment judgment unit, which is used to judge and analyze the comprehensive ecological status index of the indoor space with a preset ecological status assessment interval; a simulation analysis unit, which is used to regard the ecological status of the indoor space as abnormal when the comprehensive ecological status index of the indoor space is outside the preset ecological status assessment interval, and to perform ecological control simulation analysis in the digital twin model of the indoor space; and an ecological control unit, which is used to take ecological control measures for the indoor space when the results of the ecological control simulation analysis meet the expected standards.

[0016] A computer-readable storage medium is used to store a program, which, when executed by a processor, implements the indoor ecological environment design method based on digital twins as described above.

[0017] The present invention has the following beneficial effects:

[0018] (1) This indoor ecological environment design method based on digital twins introduces three types of ecological indicators: space comfort index, air load index and ecological restoration index. Independent calculation models are established based on indoor temperature, humidity and light parameters, pollutant concentration and wind speed parameters, plant transpiration rate and thermal hysteresis parameters, and further constructs a comprehensive ecological status index to uniformly measure the overall ecological status of the indoor environment. This structure overcomes the problems of fragmentation of various types of perception data and difficulty in uniformly evaluating the degree of ecological quality in existing technologies. It has good indicator normalization and comparability, and can perform quantitative calculations based on the actual value of each index and the adjustment coefficient stored in the database. It supports consistent judgment of the ecological status under different functional rooms and different building structure conditions, thereby improving the integrity, logic and cross-scenario applicability of ecological environment evaluation.

[0019] (2) This indoor ecological environment design method based on digital twins does not directly execute the control strategy after detecting an abnormality in the comprehensive ecological status index. Instead, it first conducts ecological control simulation analysis in the digital twin model, performs virtual execution on multiple control parameter combinations, calculates their expected impact values ​​on various ecological status indicators, and issues actual control instructions only after screening out strategy solutions that meet the set standards. This mechanism effectively solves the problems of the existing technology such as the lack of pre-execution verification of control strategies, uncertainty of control effects, and deviation of control results from expectations. Through pre-control simulation calculation and standard screening mechanism, it can avoid invalid or excessive regulation, improve the stability of the control process and the clarity of the target of execution behavior, and provide basic support for establishing a traceable data chain between subsequent control behaviors and changes in ecological status.

[0020] (3) The indoor ecological environment design method based on digital twin collects ecological environment data of indoor space at intervals according to a preset time period, and continuously updates it to the digital twin model dynamically to form a real-time synchronized multi-parameter environmental state field. During operation, this mechanism can dynamically obtain the changing trends of parameters such as temperature and humidity, air pollution concentration, and wind speed fluctuations, and construct an ecological index evolution trajectory based on actual time series data. Compared with the single sampling or staged measurement method commonly used in existing technologies, it realizes the continuity of ecological perception and the timeliness of model driving, providing a more accurate and real-time data basis for subsequent ecological status judgment and regulation simulation. At the same time, this mechanism can also be used to analyze the ecological response delay and trend feedback after the execution of the regulation behavior, providing quantitative support for strategy optimization.

[0021] (4) The indoor ecological environment design system based on digital twin can realize the complete process closed loop from indoor space physical modeling, dynamic collection of environmental data, ecological status calculation and analysis, regulation simulation evaluation to intelligent control execution. Each functional unit has relatively independent input and output logic. It can support the replacement, expansion or upgrade of different modules while maintaining the consistency of the overall data flow, improve the adaptability of the system to different space types and hardware platforms, and establish a logical condition trigger mechanism between the ecological environment judgment unit and the simulation analysis unit, realizing the explicit decoupling of the judgment and execution process of the regulation behavior, ensuring that the system has a structural level of logical error prevention capability between the control behavior generation and the result feedback, thereby enhancing the stability, flexibility and engineering maintainability of the ecological environment management system in engineering applications.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the indoor ecological environment design method based on digital twins of the present invention.

[0024] Figure 2 This is a line graph of the comprehensive ecological status index of the indoor space in six time periods in the indoor ecological environment design method based on digital twins of the present invention.

[0025] Figure 3 This is a flowchart of the specific steps for analyzing the spatial comfort index of indoor space in the indoor ecological environment design method based on digital twins of the present invention.

[0026] Figure 4 This is a block diagram of the indoor ecological environment design system based on digital twins of the present invention. DETAILED DESCRIPTION

[0027] See also Figure 1, an embodiment of the present invention provides a technical solution: an indoor ecological environment design method based on digital twins, comprising the following steps: obtaining spatial structure data, building material data, and equipment layout data of the indoor space, and establishing a digital twin model of the indoor space; obtaining the ecological environment data of the indoor space at intervals based on a set time period (for example, five minutes), and updating it to the digital twin model of the indoor space; analyzing the comprehensive ecological status index of the indoor space based on the ecological environment data in the digital twin model of the indoor space, and performing judgment analysis with a preset ecological status evaluation interval; when the comprehensive ecological status index of the indoor space is outside the preset ecological status evaluation interval, it is deemed that the ecological status of the indoor space is abnormal, and an ecological control simulation analysis is performed in the digital twin model of the indoor space; when the results of the ecological control simulation analysis meet the expected standards, ecological control measures are taken for the indoor space, specifically: comparing whether the boundary conditions used in the simulation are still consistent with the actual current environment (for example, whether there are changes such as personnel flow, opening of external windows, etc.);

[0028] If the difference is less than the set threshold (e.g., environmental state error ≤ 5%), the simulation result is confirmed to be valid and the actual control process is allowed to proceed;

[0029] Call the device interface mapping table corresponding to the twin model to identify the object to be regulated and the control authority (such as air conditioning equipment number, outlet number, humidifier port);

[0030] Establish a control parameter conversion model between the control scheme and the actual equipment (for example, the simulated temperature is adjusted to 22°C - the control command is HVAC: Mode: Cooling, Setpoint: 22);

[0031] Initialize the control instruction queue;

[0032] Send control instructions to the space control subsystem in sequence or in parallel through the Internet of Things platform (such as BACnet, Modbus, KNX and other protocols);

[0033] The execution status of each device is uploaded in real time, and the twin model dynamically updates the control execution status after receiving feedback;

[0034] If a command fails or a response fails, the system automatically calls a backup plan or prompts for manual intervention;

[0035] Start the control feedback monitoring module and collect the change curve of key indoor parameters according to the control cycle (such as every minute);

[0036] Recalculate the comprehensive ecological status index in real time;

[0037] If the comprehensive ecological status index meets the standard and remains stable for the preset time (for example, the fluctuation is less than ±5% within 10 minutes), the control is confirmed to be successful;

[0038] Write the control plan ID, execution time, control equipment response, post-control environmental data, CESI change curve, etc. into the database;

[0039] Establish a mapping record between control behavior logs and control results for subsequent control strategy iteration and self-learning optimization;

[0040] If the simulation plan deviates too much from the actual execution result, the plan is marked as simulation deviation type, which is used to eliminate or adjust the simulation model parameters.

[0041] Among them, the spatial structure data includes the length, width and height of the room and walls, and the opening distribution of doors and windows.

[0042] Building material data includes basic material types of walls / floors / windows (e.g. brick walls, glass, wooden doors).

[0043] Equipment layout data includes the locations of equipment such as air conditioners and lights (e.g., air outlets, air inlets, and light positions).

[0044] Specifically, the ecological environment data includes comfort perception data, air load data, and environmental regeneration data. The specific steps for analyzing the comprehensive ecological status index of the indoor space based on the ecological environment data in the digital twin model of the indoor space are as follows: based on the comfort perception data, air load data, and environmental regeneration data in the digital twin model of the indoor space, analyze the ecological environment index set of the indoor space, the ecological environment index set includes the space comfort index, air load index, and ecological restoration index; conduct a comprehensive analysis of the ecological environment index set of the indoor space to obtain the comprehensive ecological status index of the indoor space.

[0045] The specific formula for calculating the comprehensive ecological status index of indoor space is as follows: ;in, is the comprehensive ecological status index of the indoor space, is the spatial comfort index of the indoor space, is the space comfort adjustment coefficient stored in the database, is the air load index of the indoor space, is the air load adjustment coefficient stored in the database, is the ecological restoration index of indoor space, It is the ecological restoration adjustment coefficient stored in the database.

[0046] It should be explained that the spatial comfort adjustment coefficient stored in the database , air load adjustment coefficient , ecological restoration adjustment coefficient The specific acquisition steps are as follows: first, extract the original environmental data such as temperature, humidity, light level, CO2 concentration, air flow rate, plant transpiration rate, etc. of several spaces at different times; then, combine the actually calculated spatial comfort index, air load index, and ecological restoration index, and use multiple regression or neural network methods to establish a nonlinear mapping relationship between the adjustment factor and the index; finally, extract the sensitivity factor in the mapping relationship, normalize it, and store it in the database as the adjustment coefficient for the three dimensions of spatial comfort, air load, and ecological restoration.

[0047] The specific implementation example of calculating the comprehensive ecological status index of indoor space is as follows, with the following parameters:

[0048] The spatial comfort index of the indoor space is approximately: 0.872.

[0049] The spatial comfort adjustment coefficient stored in the database is approximately: 0.944.

[0050] The air load index of the indoor space is approximately: 0.436.

[0051] The air load adjustment factor stored in the database is approximately: 0.762.

[0052] The ecological restoration index of indoor space is approximately: 0.524.

[0053] The ecological restoration adjustment coefficient stored in the database is approximately: 0.683.

[0054] Substituting the above data into the specific formula for calculating the comprehensive ecological status index of indoor space, we obtain:

[0055] The comprehensive ecological status index of indoor space = (((0.832)^0.944)+ln(1+(0.436)^0.762))×exp(-((0.524)^0.683))≈0.803.

[0056] In addition, the specific real-time examples of the comprehensive ecological status index of the indoor space in several time periods are as follows. The comprehensive ecological status index of the indoor space in six time periods is shown in Table 1 and Figure 2 As shown:

[0057] Table 1 Examples of comprehensive ecological status index data for indoor spaces in six time periods

[0058]

[0059] In this implementation plan, by modeling three types of ecological and environmental data, namely comfort perception, air load and environmental regeneration, into spatial comfort index, air load index and ecological restoration index respectively, and further constructing a comprehensive ecological status index based on the adjustment coefficient, the unified quantitative integration of multi-source heterogeneous environmental parameters is achieved. The system extracts the original environmental data of different spaces in multiple time periods, and combines it with the actual index output results. It uses algorithms such as multivariate regression or neural networks to construct a nonlinear mapping relationship between the adjustment factor and the index, extracts representative sensitivity factors from them and normalizes them, generates adjustment coefficients and stores them in the database. This mechanism not only enhances the model's adaptability to different building structures, spatial functions and usage scenarios, but also improves the weight expression and dynamic adjustment capabilities of each sub-indices in the comprehensive calculation, ensuring that the index calculation results are targeted, adjustable and sensitive to environmental responses, and provides a highly accurate data basis and parameter support for subsequent ecological status judgment, anomaly identification and regulation simulation.

[0060] Specifically, if Figure 3 As shown, the comfort perception data includes indoor temperature value, indoor humidity value, and indoor light intensity value. The specific steps for analyzing the spatial comfort index of the indoor space are as follows: obtain the comfort perception parameter set and the comfort perception allowable deviation set of the indoor space, the comfort perception parameter set includes the indoor temperature parameter value, the indoor humidity parameter value, and the indoor light intensity parameter value, and the comfort perception allowable deviation set includes the maximum allowable deviation of the indoor temperature, the maximum allowable deviation of the indoor humidity, and the maximum allowable deviation of the indoor light intensity; comprehensively analyze the comfort perception data in the digital twin model of the indoor space, combined with the comfort perception parameter set and the comfort perception allowable deviation set of the indoor space, to obtain the spatial comfort index of the indoor space.

[0061] The indoor temperature value represents the current actual indoor temperature value, which can be measured and obtained by a room temperature sensor.

[0062] The indoor humidity value indicates the current relative humidity of the indoor air and can be measured by a room humidity sensor.

[0063] The indoor light intensity value (current illuminance value) represents the luminous flux per unit area and can be measured by a light intensity sensor.

[0064] The indoor temperature parameter value refers to the ideal indoor temperature benchmark value set based on the current space function, usage scenario and human thermal comfort model. It can be determined according to international standards such as "GB / T 18883-2002 Indoor Air Quality Standard" or ASHRAE55, or it can be customized based on the nature of the building's use (such as 23°C for nursing homes and 22°C for classrooms).

[0065] The indoor humidity reference value refers to the target humidity value set to ensure human thermal comfort and air health. It is usually set at around 50% based on standards such as the ASHRAE comfort model and GB / T 18204. It can be adjusted based on the function of the space (such as exhibition space, office area) or seasonal climate characteristics.

[0066] The indoor light intensity parameter value is the target illuminance value used to ensure visual comfort and work efficiency. It is set according to the classification requirements for different functional spaces in the "GB50034-2013 Building Lighting Design Standard" (such as 150 lux in corridors and 500 lux in offices). It can be set during the building lighting design phase or dynamically configured by the lighting control system. It is usually written to the database during the initialization of the digital twin model.

[0067] The maximum allowable indoor temperature deviation indicates the range of temperature deviation that users can accept in a specific space. It is typically determined by the human body's ability to adapt to thermal environments (e.g., ±2°C). This value can be set based on the experience of environmental experts or learned through long-term user behavior models.

[0068] The maximum allowable deviation of indoor humidity refers to the range of humidity fluctuation that the human body or equipment can tolerate near the comfort reference value. It is usually set at ±10%. This range is determined based on environmental standards and human comfort models, and can also be expanded and adjusted due to space functions (such as archives, libraries) or regional humidity control requirements.

[0069] The maximum allowable deviation of indoor light intensity refers to the acceptable illuminance fluctuation range (e.g., ±100 lux) required to maintain visual clarity and rhythmic lighting. This parameter is typically derived from lighting design standards, visual fatigue models, or user-defined settings. It serves as the trigger threshold for dimming algorithms in intelligent lighting control systems (e.g., DALI and KNX). It can also be linked to spatial location and time period to generate differentiated settings.

[0070] The specific formula for calculating the spatial comfort index of indoor space is as follows: ; in, is the spatial comfort index of the indoor space, is the indoor temperature value in the digital twin model of the indoor space, is the indoor temperature parameter value of the indoor space, is the maximum allowable deviation of the indoor temperature of the indoor space, is the temperature adjustment coefficient stored in the database, is the indoor humidity value in the digital twin model of the indoor space, is the indoor humidity parameter value of the indoor space, is the maximum allowable deviation of indoor humidity in indoor space, is the humidity adjustment coefficient stored in the database, is the indoor light intensity value in the digital twin model of the indoor space, is the indoor light intensity parameter value of the indoor space, is the maximum allowable deviation of indoor light intensity in indoor space, It is the light intensity adjustment coefficient stored in the database.

[0071] It should be explained that the temperature adjustment coefficient stored in the database , humidity adjustment coefficient , light intensity adjustment coefficient The specific acquisition steps are as follows: collect the correlation data between different temperature, humidity and light environment parameters and user comfort feedback scores (or environmental assessment indicators) in multiple historical space samples, combine the standard deviation range of each type of physical parameter and the adjustment equipment capabilities (such as air conditioning response time, dehumidification efficiency, and illumination change response speed) to perform multidimensional regression or machine learning modeling, establish the response weight of physical offset to comfort perception or recovery efficiency, extract the independent influencing factors of temperature, humidity, and light, and normalize them as adjustment coefficients 、 、 Store in database.

[0072] In this implementation scheme, by obtaining comfort perception data such as temperature, humidity and light in the indoor space, and combining the corresponding parameter values ​​and maximum allowable deviation values, a quantitative calculation model of the space comfort index is established, which can effectively evaluate the degree of deviation between the current indoor environment and the ideal comfort state. Compared with the traditional method based on single-parameter threshold judgment, this method introduces a multi-dimensional ecological perception index system, and combines the importance and control sensitivity of each physical parameter with the adjustment coefficient to weightedly process, so that the index calculation is more in line with the actual human perception experience. The adjustment coefficient is obtained through regression or machine learning analysis between historical space sample data and user comfort feedback. It has the characteristics of strong adaptability and high scenario versatility. It can automatically reflect the actual impact of various parameters on comfort under different environmental structures and equipment configurations, thereby improving the scientificity and accuracy of the space comfort index. Through this structured and adjustable model design, a robust comfort evaluation foundation is provided for subsequent comprehensive ecological index calculation and intelligent control judgment.

[0073] Specifically, the air load data includes indoor carbon dioxide concentration values, indoor particulate matter concentration values, indoor volatile organic compound concentration values, and indoor wind speed values. The specific steps for analyzing the air load index of the indoor space are as follows: obtain the air load parameter set of the indoor space, and the air load parameter set includes indoor carbon dioxide concentration parameter values, indoor particulate matter concentration parameter values, indoor volatile organic compound concentration parameter values, and indoor wind speed parameter values; conduct a comprehensive analysis of the air load data in the digital twin model of the indoor space in combination with the air load parameter set of the indoor space to obtain the air load index of the indoor space.

[0074] Among them, the indoor carbon dioxide concentration value is a gas concentration indicator that measures the indoor ventilation conditions and the intensity of human respiratory metabolic emissions. It is measured in ppm and is collected in real time through a wall-mounted or ceiling-mounted CO2 infrared sensor. It can also be obtained through feedback from the built-in detection module of the HVAC system.

[0075] The indoor particulate matter concentration value refers to the real-time mass concentration of PM2.5 (inhalable fine particulate matter), measured in μg / m³, and is usually obtained through a laser scattering air quality sensor.

[0076] The indoor volatile organic compound concentration value reflects the mixed concentration level of TVOC (including formaldehyde, benzene, toluene, etc.) gaseous harmful substances. The unit is mg / m³ or ppm and can be collected by a high-sensitivity metal oxide sensor or an electrochemical VOC sensor.

[0077] The indoor wind speed value refers to the air flow speed per unit time, measured in m / s. It is a dynamic parameter that reflects the air exchange efficiency and pollution dilution capacity of the space. It can be obtained by real-time measurement through equipment such as ultrasonic anemometers and thermal film wind speed sensors.

[0078] The indoor carbon dioxide concentration reference value refers to the upper limit of CO2 concentration set under the premise of ensuring good ventilation and human health. It is usually set at no more than 1000 ppm based on the "GB / T 18883-2002 Indoor Air Quality Standard" or ASHRAE62.1 standard. This value is usually combined with the building use (such as conference rooms, classrooms), ventilation type (natural / mechanical ventilation) and occupancy density conditions, and is generated through air quality assessment models or historical monitoring data fitting.

[0079] The indoor particulate matter concentration reference value is used to characterize the upper limit of the acceptable concentration of indoor PM2.5 suspended particle pollution. It is generally set according to the 75μg / m³ limit specified in the "Indoor Air Quality Standard". It can also be adjusted due to regional background pollution, filtration device efficiency, and the needs of special-purpose places (such as clean laboratories and medical institutions). This value can be derived from the regional reference value published by the environmental monitoring department, the filtration level of the building's fresh air system, or obtained through a pollution risk assessment model.

[0080] The reference value for indoor volatile organic compound concentration is the upper limit of healthy exposure set for indoor volatile pollutants such as formaldehyde, benzene, and TVOC. Its standard is based on the TVOC concentration limit requirements in "GB / T 18883" or "GB / T 50325" (generally not higher than 0.6 mg / m³). During the initial construction of the system, a predictive assessment will be conducted based on building materials, furniture release sources, and ventilation conditions, and combined with historical air quality monitoring data or sensor sampling modeling results.

[0081] The indoor wind speed parameter is used to evaluate air replacement efficiency and pollutant dilution capacity. It represents the optimal wind speed range for maintaining good air flow without causing thermal discomfort or local disturbances. It is typically set at 0.3-0.5 m / s based on ASHRAE wind environment standards or indoor comfort requirements. The system can simulate and extract parameters based on CFD simulation or BIM wind speed simulation modules based on HVAC system specifications, fresh air volume settings, room volume, and other parameters. It also supports real-time wind speed sensor sampling feedback as a basis for set value correction.

[0082] The specific formula for calculating the air load index of indoor space is as follows: ;in, is the air load index of the indoor space, is the indoor carbon dioxide concentration value in the digital twin model of the indoor space, is the reference value of indoor carbon dioxide concentration in indoor space, is the indoor particulate matter concentration value in the digital twin model of the indoor space, is the indoor particulate matter concentration parameter value of indoor space, is the indoor volatile organic compound concentration value in the digital twin model of the indoor space, is the reference value of indoor volatile organic compound concentration in indoor space, is the pollutant concentration adjustment coefficient stored in the database, is the indoor wind speed value in the digital twin model of the indoor space, is the indoor wind speed parameter value of the indoor space, is the wind speed adjustment coefficient stored in the database.

[0083] It should be explained that the pollutant concentration adjustment coefficient stored in the database , wind speed adjustment coefficient The specific acquisition steps are as follows: based on multiple historical spatial samples, respectively collect the concentration data of pollutants such as CO2, particulate matter, and volatile organic compounds (VOC) and the response relationship with the spatial air load index, as well as the wind speed change data in the corresponding time period and the mitigation effect data between them, and use multivariate regression, sensitivity analysis or machine learning methods to extract the nonlinear influence weights of each pollutant category and wind speed factor on the air load index; further combine the performance boundaries of pollution control equipment (such as fresh air volume, filtration efficiency) and pollution index thresholds, perform normalized modeling and sensitivity segmentation processing, and finally form the pollutant concentration adjustment coefficient and wind speed adjustment coefficient .

[0084] In this implementation, an air load index calculation model is constructed by acquiring indoor air load data such as CO2 concentration, particulate matter concentration, volatile organic compound concentration, and wind speed, and combining it with corresponding parameter values. This achieves a comprehensive multi-factor assessment of indoor air pollution levels and ventilation capacity. Compared with the traditional method of relying solely on a single concentration limit for threshold judgment, this index model can quantitatively reflect the true balance between pollution load and dilution capacity through dynamic coupling analysis of pollutant concentration and wind speed factors. By collecting multiple historical spatial samples and combining them with actual air quality response results, the system uses multivariate regression or machine learning methods to extract the nonlinear influence weights of each pollutant category and wind speed factor, and generates pollutant concentration adjustment coefficients and wind speed adjustment coefficients based on these weights, improving the accuracy and adaptability of the model. After the adjustment coefficients are introduced, the influence of pollution indicators in the comprehensive load index can be dynamically adjusted according to different room structures, usage functions, or equipment performance, making the air quality assessment results closer to the actual risk level and providing a stable and reliable quantitative basis for subsequent control strategies.

[0085] Specifically, the environmental regeneration data includes the transpiration rate value of indoor plants and the thermal lag time of the wall. The specific steps for analyzing the ecological restoration index of the indoor space are as follows: obtaining the maximum allowable thermal lag parameter value of the indoor space; comprehensively analyzing the environmental regeneration data in the digital twin model of the indoor space in combination with the maximum allowable thermal lag parameter value of the indoor space to obtain the ecological restoration index of the indoor space.

[0086] The transpiration rate of indoor plants, measured in g / m²·h, reflects the ability of plants to release water vapor through their stomata per unit time and per unit area. It is an important physical parameter for assessing a plant's ability to regulate indoor temperature and humidity and its ecological resilience. This value can be calculated using a plant physiological transpiration model (such as the Penman-Monteith model) by measuring the rate of temperature and humidity change using environmental sensing nodes installed close to the plants, combined with parameters such as plant species, leaf area index, and light conditions. It can also be inverted and analyzed using embedded soil moisture and leaf temperature sensors.

[0087] Wall thermal hysteresis time refers to the delay time (in seconds or hours) required for heat to be transferred from the wall structure to the interior and cause a temperature response when the external temperature changes. This parameter is used to measure the thermal inertia of the building envelope and its ability to buffer temperature fluctuations. It can be obtained through thermal simulation software (such as EnergyPlus, TRNSYS) or material physical properties in the building BIM model. The parameters involved include wall thickness, material thermal conductivity, specific heat capacity, density, etc.

[0088] The maximum allowable thermal hysteresis parameter is the upper limit value used to standardize the wall response capacity in the ecological restoration model. It represents the maximum delay time that can be tolerated in the wall thermal response under the conditions of a given building type and climate zone. Exceeding this value will be considered as a weak thermal environment regulation ability. This parameter can be given by building energy conservation codes or thermal design standards (such as the thermal inertia determination criteria of envelope structures in the "GB50176 Building Energy Conservation Design Standard"), or it can be extracted through percentile regression of the simulation results of a large number of sample walls of different structural types.

[0089] The specific formula for calculating the ecological restoration index of indoor space is as follows: ;in, is the ecological restoration index of indoor space, is the transpiration rate value of indoor plants in the digital twin model of indoor space, is the plant transpiration regulation coefficient stored in the database, is the thermal lag time of the wall in the digital twin model of the indoor space, is the maximum allowable thermal hysteresis parameter value for indoor space, is the thermal hysteresis adjustment coefficient stored in the database.

[0090] It should be explained that the plant transpiration regulation coefficient stored in the database , thermal hysteresis adjustment coefficient The specific steps for obtaining the index are as follows: first, based on multiple typical spatial samples, collect the transpiration rate data per unit area of ​​different plant species under temperature, humidity, and light conditions in multiple time periods, and analyze their sensitivity to the impact on ecological restoration efficiency (such as humidity stabilization time and thermal comfort recovery speed). Through regression fitting or response surface modeling, extract the actual contribution rate of transpiration effect to the restoration index, and obtain the plant transpiration regulation coefficient after normalization. Secondly, combining the heat capacity, thermal conductivity and wall response curve of building structural materials, a dynamic relationship model between the wall thermal hysteresis time and the recovery index change is established. By fitting the curve slope with the adjustment hysteresis effect, the thermal hysteresis influence weight is extracted and standardized into the thermal hysteresis adjustment coefficient. .

[0091] In this implementation, an ecological restoration index calculation model is constructed by acquiring indoor plant transpiration rates and wall thermal hysteresis times from environmental regeneration data and combining them with a maximum allowable thermal hysteresis parameter. This effectively quantifies the strength of a space's natural regulatory capacity. Plant transpiration rates reflect the effectiveness of plants in actively fine-tuning humidity and heat, while wall thermal hysteresis measures the building envelope's ability to buffer external temperature fluctuations. By introducing plant transpiration and thermal hysteresis regulation coefficients trained based on historical sample data, the system can identify the response weights of different plant species and building structures during the actual regulation process and use them in the parameter weighting of the index calculation. Compared to traditional design methods that ignore the dynamic nature of the recovery process, this invention can reflect the ecosystem's potential for self-recovery after disturbance, providing a basis for regulatory simulations under abnormal ecological conditions and improving the objective evaluation of the resilience of low-intervention spaces (such as green offices and energy-saving buildings). This index not only enhances the integrity of ecological assessments but also provides basic parameter support for zoning regulation and energy optimization.

[0092] Specifically, the specific steps for conducting ecological regulation simulation analysis in the digital twin model of indoor space are as follows: based on the comprehensive ecological status index of the indoor space, identify the abnormal factor set and use it as the factors to be regulated. Specifically, the following are: analyze the index that causes the abnormal index (such as space comfort index, air load index, ecological recovery index); call the ecological environment data recorded in the twin model to identify the main physical parameter set that deviates from the normal threshold in the current period, such as CO2 concentration, temperature, PM2.5 concentration, light fluctuations, etc.; pass the abnormal factor set as the factor set to be regulated into the simulation module.

[0093] Construct several groups of controllable factor combinations (such as fresh air volume adjustment level, air conditioning temperature setting, humidifier output, and plant lighting time), each group is a type of strategy configuration; each combination includes a mapping relationship between control factors and expected impact targets (such as increasing wind speed - reducing CO2); all combinations are written into the strategy library of the twin model.

[0094] The control simulation process is initialized in the digital twin model of the indoor space. Specifically, without changing the real physical environment, the current indoor space structure, material response, equipment layout, and environmental boundary conditions are called in the digital twin environment; the initial state field of the control at the current time node (including full-parameter environmental fields such as temperature, humidity, scenery, etc.) is constructed; and the control simulation engine is activated to prepare for parallel simulation of the impact responses of all control scheme groups.

[0095] Control scheme simulations are performed in parallel in the digital twin model of the indoor space, and the optimal control simulation results are screened. Specifically, each set of control schemes is independently simulated in the twin space based on the thermal-humidity coupling model, aerodynamic model, light-heat transmission model, etc. The simulation duration covers a control cycle (e.g., 15 minutes), and the index change path under each set of simulations is collected. The comprehensive ecological status index and sub-indicator improvement degree after control are calculated for each set of simulation results, and all simulation scheme results are compared with the set expected control standards. If multiple schemes meet expectations, the scheme with the best energy consumption, the shortest response time, or the best user experience is given priority. Finally, the control scheme ID + response indicator path + energy consumption prediction value are packaged and output as a candidate control scheme.

[0096] In this implementation, by introducing an ecological control simulation and analysis mechanism into the digital twin model, the expected effects of multiple control strategies can be virtually calculated and optimally screened before actual control measures are implemented. The system first identifies the key influencing factors of abnormal comprehensive ecological status indexes, extracts environmental physical parameters that currently deviate from thresholds, and constructs a set of factors to be controlled. Based on their impact paths, it constructs multiple control strategy combinations, covering equipment operating parameters such as fresh air adjustment, air conditioning settings, and humidification control. Without affecting the real environment, the system replicates the structural layout, boundary conditions, and equipment performance in the twin space, initializes the state field, and activates the simulation engine. Each set of scenarios is simulated in parallel based on the multi-physics field model. By calculating and evaluating the change paths of various ecological indices in the simulation results, the system can quantitatively determine the ecological improvement effect, energy consumption performance, and response speed of each strategy. Ultimately, the optimal strategy is selected and output as a candidate control instruction. This mechanism avoids control deviations caused by blind strategy execution, improves the predictability and security of system behavior, and significantly enhances the robustness, energy efficiency, and policy decision-making transparency of the intelligent control system.

[0097] See also Figure 4, an embodiment of the present invention provides a technical solution: an indoor ecological environment design system based on digital twins, including: a model building unit, used to obtain spatial structure data, building material data, and equipment layout data of the indoor space, and establish a digital twin model of the indoor space; an acquisition and update unit, used to obtain the ecological environment data of the indoor space at intervals based on a set time period (for example, five minutes), and update it to the digital twin model of the indoor space; an ecological environment analysis unit, used to analyze the comprehensive ecological status index of the indoor space based on the ecological environment data in the digital twin model of the indoor space; an ecological environment judgment unit, used to judge and analyze the comprehensive ecological status index of the indoor space with a preset ecological status assessment interval; a simulation analysis unit, used to regard the ecological status of the indoor space as abnormal when the comprehensive ecological status index of the indoor space is outside the preset ecological status assessment interval, and to perform ecological control simulation analysis in the digital twin model of the indoor space; and an ecological control unit, used to take ecological control measures for the indoor space when the result of the ecological control simulation analysis meets the expected standard.

[0098] A computer-readable storage medium is used to store a program, which, when executed by a processor, implements the indoor ecological environment design method based on digital twins as described above.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. The indoor ecological environment design method based on digital twin is characterized by: The following steps are involved: Obtain spatial structure data, building material data, and equipment layout data of the indoor space, and establish a digital twin model of the indoor space; Based on a set time period, the ecological environment data of the indoor space is acquired at intervals. The ecological environment data includes comfort perception data, air load data, and environmental regeneration data, and is updated to the digital twin model of the indoor space. The environmental regeneration data includes the transpiration rate value of indoor plants and the thermal hysteresis time of walls. The specific steps for analyzing the ecological restoration index of the indoor space are as follows: Obtain the maximum allowable thermal hysteresis parameter value of the indoor space; The environmental regeneration data in the digital twin model of the indoor space is combined with the maximum allowable thermal hysteresis parameter value of the indoor space for comprehensive analysis to obtain the ecological restoration index of the indoor space. Based on the ecological environment data in the digital twin model of the indoor space, the comprehensive ecological status index of the indoor space is analyzed. The specific steps are as follows: Based on the comfort perception data, air load data, and environmental regeneration data in the digital twin model of the indoor space, the ecological environment index set of the indoor space is analyzed. The ecological environment index set includes the space comfort index, air load index, and ecological restoration index. A comprehensive analysis of the ecological environment index set of the indoor space is performed to obtain the comprehensive ecological status index of the indoor space; and a judgment analysis is performed with the preset ecological status assessment interval. The air load data includes indoor carbon dioxide concentration, indoor particulate matter concentration, indoor volatile organic compound concentration, and indoor wind speed. The specific steps for analyzing the air load index of an indoor space are as follows: Acquire an air load parameter set of the indoor space, the air load parameter set including an indoor carbon dioxide concentration parameter value, an indoor particulate matter concentration parameter value, an indoor volatile organic compound concentration parameter value, and an indoor wind speed parameter value; The air load data in the digital twin model of the indoor space is combined with the air load parameter set of the indoor space for comprehensive analysis to obtain the air load index of the indoor space. When the comprehensive ecological status index of an indoor space is outside the preset ecological status assessment range, the indoor space ecological status is considered abnormal, and an ecological regulation simulation analysis is performed in the digital twin model of the indoor space. The specific steps are as follows: Based on the comprehensive ecological status index of the indoor space, a set of abnormal factors is identified and used as factors to be regulated; several groups of controllable factor combinations are constructed, each group representing a type of strategy configuration; and the regulation simulation process is initialized in the digital twin model of the indoor space. Execute control scheme simulations in parallel in the digital twin model of the indoor space and select the optimal control simulation results; When the results of the ecological control simulation analysis meet the expected standards, ecological control measures are taken for the indoor space.

2. The indoor ecological environment design method based on digital twin according to claim 1 is characterized in that: The comfort perception data includes indoor temperature, indoor humidity, and indoor light intensity. The specific steps for analyzing the spatial comfort index of an indoor space are as follows: Acquire a comfort perception parameter set and a comfort perception allowable deviation set of the indoor space, wherein the comfort perception parameter set includes an indoor temperature parameter value, an indoor humidity parameter value, and an indoor light intensity parameter value, and the comfort perception allowable deviation set includes a maximum allowable deviation of the indoor temperature, a maximum allowable deviation of the indoor humidity, and a maximum allowable deviation of the indoor light intensity; The comfort perception data in the digital twin model of the indoor space is comprehensively analyzed in combination with the comfort perception parameter set and the comfort perception allowable deviation set of the indoor space to obtain the spatial comfort index of the indoor space.

3. The indoor ecological environment design method based on digital twin according to claim 1 is characterized in that: The specific formula for calculating the comprehensive ecological status index of indoor space is as follows: ； in, 、 、 、 They are the comprehensive ecological status index of indoor space, space comfort index, air load index, and ecological restoration index. 、 、 These are the space comfort adjustment coefficient, air load adjustment coefficient, and ecological restoration adjustment coefficient stored in the database; The specific formula for calculating the air load index of indoor space is as follows: ； in, is the air load index of the indoor space, 、 、 、 They are the indoor carbon dioxide concentration value, indoor particulate matter concentration value, indoor volatile organic compound concentration value, and indoor wind speed value in the digital twin model of the indoor space. 、 、 、 The following are the indoor carbon dioxide concentration parameter value, indoor particulate matter concentration parameter value, indoor volatile organic compound concentration parameter value, and indoor wind speed parameter value of the indoor space. 、 They are the pollutant concentration adjustment coefficient and wind speed adjustment coefficient stored in the database respectively.

4. An indoor ecological environment design system based on digital twins, applying the indoor ecological environment design method based on digital twins according to any one of claims 1 to 3, characterized in that: include: The model building unit is used to obtain the spatial structure data, building material data, and equipment layout data of the indoor space, and to build a digital twin model of the indoor space; An acquisition and update unit is used to obtain ecological environment data of the indoor space at intervals based on a set time period and update it into the digital twin model of the indoor space; The ecological environment analysis unit is used to analyze the comprehensive ecological status index of the indoor space based on the ecological environment data in the digital twin model of the indoor space; The ecological environment judgment unit is used to judge and analyze the comprehensive ecological status index of the indoor space and the preset ecological status assessment interval; A simulation analysis unit is used to determine that the indoor space's ecological state is abnormal when the comprehensive ecological state index is outside the preset ecological state assessment range, and to perform ecological regulation simulation analysis in the digital twin model of the indoor space; The ecological control unit is used to take ecological control measures for the indoor space when the ecological control simulation analysis results meet the expected standards.

5. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the indoor ecological environment design method based on digital twins as described in any one of claims 1 to 3 is implemented.

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