Temperature and humidity intelligent adjusting method and system for nanometer floor heating

Abstract

The invention provides an intelligent temperature and humidity adjusting method and system for nanometer floor heating, and relates to the technical field of intelligent temperature and humidity adjustment. The method comprises the steps that space distribution data and floor heating system data are interacted; carrying out floor heating structure layer modularization based on floor heating system data, and carrying out space partitioning based on space distribution data; traversing the management grid, and performing data-driven modeling; performing external environment prediction, and determining prediction environment distribution; monitoring and acquiring a space field based on temperature and humidity, and determining a space adjustment task in combination with the standardized space field; a real-time adjustment decision and a predictive adjustment decision are made; and the pre-adjustment strategy responds to the floor heating system, and space temperature and humidity adjustment is conducted. The technical problem that in the prior art, a traditional floor heating system lacks predictive adjusting capacity when coping with external environment changes can be solved, the technical target of intelligent and precise adjusting of the floor heating system is achieved, and the technical effects of improving the comfort level of a user and reducing energy consumption are achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent temperature and humidity control technology, and in particular to an intelligent temperature and humidity control method and system for nano-floor heating. Background Technology

[0002] In modern buildings, underfloor heating systems are widely used as an efficient heating method. These systems transfer heat to the surface by laying heating pipes or electric heating films beneath the floor, thereby increasing indoor temperature and providing a comfortable living environment.

[0003] Currently, traditional underfloor heating systems are slow to respond to changes in the external environment and lack predictive adjustment capabilities. External environmental factors such as temperature and humidity change with time and seasons, directly impacting the indoor environment. Existing systems typically only adjust when significant changes occur in the internal environment, often missing the optimal adjustment window, leading to increased energy consumption and decreased user comfort. Furthermore, existing systems neglect the human body's tolerance to temperature changes during adjustment, potentially causing discomfort to users and even affecting the system's lifespan if adjustments are too drastic.

[0004] In summary, existing technologies suffer from the technical problems that traditional underfloor heating systems are slow to respond to changes in the external environment, lack predictive adjustment capabilities, resulting in adjustment lag and increased energy consumption, which further affect user comfort and system lifespan. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for intelligent temperature and humidity regulation for nano-floor heating, in order to solve the technical problems in the prior art where traditional floor heating systems are slow to respond to changes in the external environment, lack predictive regulation capabilities, resulting in regulation lag and increased energy consumption, which further affects user comfort and system lifespan.

[0006] In view of the above problems, this application provides a method and system for intelligent temperature and humidity regulation for nano-floor heating.

[0007] Firstly, this application provides a method for intelligent temperature and humidity regulation for nano-underfloor heating, implemented through an intelligent temperature and humidity regulation system for nano-underfloor heating, comprising: interacting with spatial distribution data of the nano-underfloor heating installation area and underfloor heating system data; modularizing the underfloor heating structure layer based on the underfloor heating system data, and spatially partitioning based on the spatial distribution data to determine a management grid; traversing the management grid, combining fuzzy control rules and temperature and humidity coupling functions to perform data-driven modeling and generate an intelligent regulation model, wherein the intelligent regulation model includes a temperature regulation branch, a humidity regulation branch, and a subsequent coupling coordination unit; predicting the external environment, combining the building heat and mass transfer effect to determine the predicted environmental distribution, wherein the predicted environmental distribution has spatiotemporal characteristics; monitoring and acquiring a spatial field based on temperature and humidity, and combining a standardized spatial field to determine a spatial regulation task; based on the spatial regulation task and the predicted environmental distribution, and combining the intelligent regulation model to perform real-time regulation decisions and predictive regulation decisions to determine a pre-regulation strategy; the pre-regulation strategy responds to the underfloor heating system to regulate the spatial temperature and humidity of the underfloor heating installation area.

[0008] Secondly, this application also provides a temperature and humidity intelligent regulation system for nano-underfloor heating, used to execute a temperature and humidity intelligent regulation method for nano-underfloor heating as described in the first aspect, comprising: a data interaction module, used to interact with spatial distribution data of the nano-underfloor heating installation area and underfloor heating system data; a management grid determination module, used to modularize the underfloor heating structure layer based on the underfloor heating system data, and to perform spatial partitioning based on the spatial distribution data to determine the management grid; and an intelligent regulation model generation module, used to traverse the management grid, combine fuzzy control rules and temperature and humidity coupling functions to perform data-driven modeling, and generate an intelligent regulation model, wherein the intelligent regulation model includes a temperature regulation branch, a humidity regulation branch, and... The system includes: a post-coupling coordination unit; a predicted environmental distribution determination module, which predicts the external environment and determines the predicted environmental distribution based on building heat and mass transfer effects, wherein the predicted environmental distribution has spatiotemporal characteristics; a spatial regulation task determination module, which monitors and acquires a temperature and humidity-based spatial field and determines the spatial regulation task based on a standardized spatial field; a pre-regulation strategy determination module, which determines a pre-regulation strategy based on the spatial regulation task and the predicted environmental distribution, combined with the intelligent regulation model, to make real-time and predictive regulation decisions; and a spatial temperature and humidity regulation module, which responds to the pre-regulation strategy and regulates the spatial temperature and humidity of the underfloor heating system.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By interacting with the spatial distribution data of the nano-underfloor heating installation area and the underfloor heating system data; based on the underfloor heating system data, modularization of the underfloor heating structure layer is performed; based on the spatial distribution data, spatial partitioning is performed to determine the management grid; traversing the management grid, combined with fuzzy control rules and temperature and humidity coupling functions, data-driven modeling is performed to generate an intelligent adjustment model, wherein the intelligent adjustment model includes a temperature adjustment branch, a humidity adjustment branch, and a subsequent coupling coordination unit; external environment prediction is performed, combined with building heat and mass transfer effects, to determine the predicted environmental distribution, wherein the predicted environmental distribution has spatiotemporal characteristics; monitoring and acquiring the spatial field based on temperature and humidity, combined with the standardized spatial field, to determine the spatial adjustment task; based on the spatial adjustment task and the predicted environmental distribution, combined with the intelligent adjustment model, real-time adjustment decisions and predictive adjustment decisions are made to determine the pre-adjustment strategy; the pre-adjustment strategy responds to the underfloor heating system to adjust the spatial temperature and humidity of the underfloor heating installation area. In other words, by combining real-time monitoring data of spatial temperature and humidity and external environment prediction data, the adjustment algorithm can achieve precise adjustment of each area. Meanwhile, by introducing anomaly monitoring and source tracing technologies, abnormal situations during the adjustment process can be quickly identified and handled, ensuring the efficient and stable operation of the system. Ultimately, this achieves the technical goal of intelligent and precise adjustment of the underfloor heating system, thereby improving user comfort, reducing energy consumption, and extending the system's service life.

[0011] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0013] Figure 1 This is a schematic flowchart of a method for intelligent temperature and humidity regulation for nano-floor heating according to this application.

[0014] Figure 2This is a schematic diagram of the structure of an intelligent temperature and humidity control system for nano-floor heating according to this application.

[0015] Explanation of reference numerals in the attached figures:

[0016] Data interaction module 11, management grid determination module 12, intelligent adjustment model generation module 13, predicted environment distribution determination module 14, spatial adjustment task determination module 15, pre-adjustment strategy determination module 16, and spatial temperature and humidity adjustment module 17. Detailed Implementation

[0017] This application provides a method and system for intelligent temperature and humidity regulation in nano-underfloor heating, solving the technical problems in existing technologies where traditional underfloor heating systems are slow to respond to changes in the external environment, lack predictive adjustment capabilities, leading to lag in regulation and increased energy consumption, which further affects user comfort and system lifespan. It achieves the technical goal of intelligent and precise regulation of the underfloor heating system, resulting in improved user comfort, reduced energy consumption, and extended system lifespan.

[0018] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0019] Example 1, please refer to the appendix. Figure 1 This application provides a method for intelligent temperature and humidity control for nano-underfloor heating, applied to an intelligent temperature and humidity control system for nano-underfloor heating, specifically including the following steps:

[0020] Step 1: Interact with the spatial distribution data of the nano-underfloor heating installation area and the underfloor heating system data.

[0021] Specifically, by interacting with the spatial distribution data of the nano-underfloor heating installation area and the underfloor heating system data, precise temperature and humidity control can be achieved. First, the spatial distribution data provides detailed information about the installation area, including the size, location, and coverage of each area. Combining this spatial distribution data with the underfloor heating system data allows for a better understanding of the underfloor heating's operational status and heat distribution. Second, by monitoring and analyzing the underfloor heating system data in real time, such as current temperature, humidity, and energy consumption, heating parameters in each area can be dynamically adjusted, thereby optimizing temperature and humidity in different areas. Finally, based on the interactive data combining spatial distribution data and underfloor heating system data, the intelligent adjustment system can anticipate potential temperature and humidity changes and make adjustments in advance, ensuring overall environmental comfort and energy efficiency.

[0022] Step 2: Based on the floor heating system data, modularize the floor heating structure layer; based on the spatial distribution data, partition the space and determine the management grid.

[0023] Specifically, based on underfloor heating system data, the underfloor heating structure can be modularized, improving the system's flexibility and adaptability. First, by analyzing the system data, the underfloor heating structure can be divided into several independent modules, each corresponding to different heating needs and operating parameters. Next, based on spatial distribution data, the underfloor heating area can be divided into detailed spatial zones, determining corresponding management grids according to the characteristics and needs of different areas. Through modularization and zoning, each management grid can be independently adjusted for temperature and humidity, ensuring optimal comfort and energy efficiency in each underfloor heating area. Ultimately, this intelligent adjustment method not only improves the system's operating efficiency but also adjusts heating strategies based on real-time data, achieving refined management and optimized control.

[0024] Step 3: Traverse the management grid, combine fuzzy control rules with temperature and humidity coupling functions to perform data-driven modeling, and generate an intelligent regulation model, wherein the intelligent regulation model includes a temperature regulation branch, a humidity regulation branch, and a subsequent coupling coordination unit.

[0025] Specifically, by sequentially accessing the management grid, a comprehensive understanding of the temperature and humidity status and needs of each area can be obtained. Data-driven modeling, combining fuzzy control rules and temperature-humidity coupling functions, captures the patterns of temperature and humidity changes. This modeling method generates an intelligent regulation model, comprising a temperature regulation branch, a humidity regulation branch, and a subsequent coupling coordination unit. The temperature regulation branch focuses on controlling the temperature of each area, ensuring it remains within the set range; the humidity regulation branch is responsible for adjusting humidity levels to guarantee environmental comfort. The coupling coordination unit integrates the results of temperature and humidity regulation, achieving a balance and optimization between the two. The intelligent regulation model can accurately respond to the needs of different areas, improving the operating efficiency of the underfloor heating system and user comfort.

[0026] Step 4: Perform external environment prediction, and determine the predicted environment distribution by combining the building heat and mass transfer effects. The predicted environment distribution has spatiotemporal characteristics.

[0027] Specifically, by analyzing trends in the external environment and making environmental predictions, we can obtain information on changes in climate parameters such as temperature and humidity over a future period. Combined with building heat and mass transfer effects, this allows us to consider the heat and humidity transfer within the building under different environmental conditions, thereby determining the predicted environmental distribution. This predicted environmental distribution is spatiotemporal, encompassing not only the environmental state at each point in time but also environmental changes at different spatial locations, making the predictions more comprehensive and accurate. This spatiotemporal predictive distribution helps in developing more scientific regulation strategies, optimizing temperature and humidity management within buildings, and improving energy efficiency and user comfort.

[0028] Step 5: Monitor and acquire the spatial field based on temperature and humidity, and combine it with the standardized spatial field to determine the spatial regulation task.

[0029] Specifically, by monitoring and acquiring temperature and humidity-based spatial fields, and by collecting temperature and humidity data from various regions in real time, a comprehensive understanding of the current environmental conditions of the space can be obtained. By combining the actual monitoring data with the ideal standardized data, the deviations and deficiencies of each region can be analyzed, thereby determining the specific tasks that need to be adjusted. Targeted adjustment strategies can be formulated to ensure that the temperature and humidity of each region reach the set standards, thereby improving the overall environmental comfort and energy efficiency.

[0030] Step Six: Based on the spatial regulation task and the predicted environmental distribution, and in conjunction with the intelligent regulation model, make real-time regulation decisions and predictive regulation decisions to determine the pre-regulation strategy.

[0031] Specifically, the spatial regulation task provides the current temperature and humidity requirements for each region, while the predicted environmental distribution indicates the environmental change trend over a future period. Through an intelligent regulation model, the spatial regulation task and the predicted environmental distribution are comprehensively analyzed to adjust current environmental parameters in real time to meet immediate needs, while simultaneously performing predictive regulation to address future changes. This ensures the foresight and accuracy of the regulation strategy, thereby determining the optimal pre-regulation strategy and achieving efficient system operation and a comfortable user experience.

[0032] Step 7: The pre-adjustment strategy responds to the underfloor heating system to adjust the temperature and humidity of the underfloor heating area.

[0033] Specifically, the pre-regulation strategy responds to the underfloor heating system by analyzing the current environment and user needs to determine the optimal temperature and humidity control scheme. It then adjusts the temperature and humidity of the underfloor heating area to ensure that each zone meets the set standards. By precisely controlling each module of the underfloor heating system, the regulation strategy can be personalized according to the specific conditions of different areas, thereby improving overall comfort and energy efficiency. Furthermore, it monitors temperature and humidity changes in real time during the adjustment process, adjusting the strategy promptly to adapt to environmental changes and ensuring the continuity and stability of the regulation effect. By responding to the underfloor heating system and precisely regulating the temperature and humidity of the installed area, the pre-regulation strategy achieves efficient environmental control and improved user comfort.

[0034] The aforementioned intelligent temperature and humidity control method for nano-underfloor heating is applied to an intelligent temperature and humidity control system for nano-underfloor heating. It can achieve the technical goal of intelligent and precise control of the underfloor heating system, thereby improving user comfort, reducing energy consumption, and extending the system's service life.

[0035] Furthermore, this application also includes:

[0036] The system traverses the floor heating structure layers, modularizes each layer, and determines the one-dimensional module distribution; it traverses the spatial distribution data, divides the space into regions based on a preset difference in temperature and humidity requirements, and determines the two-dimensional region distribution; it integrates the one-dimensional module distribution and the two-dimensional region distribution to determine the management grid.

[0037] Specifically, by systematically examining each layer of the underfloor heating system, the physical properties, material composition, and thermal conductivity of each layer are analyzed in detail, dividing it into several independent modules. Modular processing helps to precisely control the temperature distribution and energy consumption of each structural layer, and the determination of the one-dimensional module distribution provides a foundation for subsequent temperature and humidity regulation.

[0038] Next, by accessing the spatial distribution data sequentially, detailed information about the laying area can be obtained, such as location, area, and temperature and humidity requirements for each area. Using preset temperature and humidity differences, the spatial distribution data is used to divide the space into regions, determining the specific distribution of each region. Determining the two-dimensional region distribution facilitates precise adjustment of different regions, ensuring that the temperature and humidity of each region meet the set requirements.

[0039] Then, by integrating the previously obtained one-dimensional module distribution and two-dimensional area distribution, a complete management grid can be formed. The management grid not only includes module information of each structural layer of the underfloor heating system, but also the distribution of spatial areas, enabling more effective temperature and humidity regulation of each module and area, and achieving optimized system management.

[0040] By traversing the underfloor heating structure layers and performing modular processing, a one-dimensional module distribution is determined. Then, based on temperature and humidity requirements, spatial areas are divided to determine a two-dimensional area distribution. Finally, the two are integrated to form a comprehensive management grid, ensuring that the temperature and humidity regulation of the underfloor heating system can be refined and precise, thereby improving the overall efficiency and comfort of the system.

[0041] Furthermore, this application also includes:

[0042] The underfloor heating structural layer includes at least an extruded polystyrene board, an electric underfloor heating film, and a wiring harness layer.

[0043] Specifically, the underfloor heating structural layer includes at least extruded polystyrene (XPS) board, electric underfloor heating film, and a wiring harness layer. The XPS board serves as the base layer, providing insulation and support; the electric underfloor heating film, as the main heating layer, is responsible for heat generation and conduction; and the wiring harness layer is responsible for power transmission and control. Modularizing each layer into several independent modules helps to precisely control the temperature distribution and energy consumption of each layer, ensuring efficient system operation.

[0044] By traversing the entire underfloor heating structure and modularizing the extruded polystyrene board, electric underfloor heating film, and wiring harness layer, a one-dimensional module distribution is determined. Then, based on temperature and humidity requirements, spatial areas are divided to determine a two-dimensional area distribution. Finally, the two are integrated to form a comprehensive management grid, ensuring that the temperature and humidity regulation of the underfloor heating system can be refined and precise, thereby improving the overall efficiency and comfort of the system.

[0045] Furthermore, this application also includes:

[0046] An objective adjustment rule is set, which includes adjustment triggering and adjustment logic; user subjective adjustment records in a preset historical time zone are obtained, user habits are mined, and subjective adjustment rules are determined; the objective adjustment rule and the subjective adjustment rule are integrated, and rule fuzzification processing is performed to determine the fuzzy control rule, wherein the subjective adjustment rule has adjustment collision priority.

[0047] Specifically, by setting objective adjustment rules, we can define the conditions under which temperature and humidity adjustments are needed, as well as the specific adjustment logic, namely, the adjustment trigger and the adjustment logic. The adjustment trigger rules include the response when temperature and humidity deviate from the set range, while the adjustment logic details how to restore the system to the ideal state. The setting of the adjustment trigger and adjustment logic provides a stable adjustment basis for the system, ensuring that basic operational requirements are met.

[0048] Next, by analyzing users' adjustment records at different time periods, we can discover their preferences and habits regarding temperature and humidity. These subjective user adjustment records provide valuable data that can be used to develop subjective adjustment rules that better suit individual user needs. These records reflect users' actual usage and preferences, making system adjustments more personalized and flexible.

[0049] Then, by integrating objective and subjective adjustment rules and applying fuzzy processing, more complex and flexible control can be achieved within the system. When objective and subjective adjustment rules conflict, the collision priority is adjusted to determine which rule to use, thereby ensuring the rationality of the adjustment and user satisfaction.

[0050] By first setting objective adjustment rules that include adjustment triggers and adjustment logic, then determining subjective adjustment rules by analyzing users' subjective adjustment records, and finally integrating the two and performing fuzzification processing, fuzzy control rules with adjustment collision priority are determined. This not only ensures the basic operational requirements of the system, but also meets users' personalized needs, improving the accuracy of adjustment and user experience.

[0051] Furthermore, this application also includes:

[0052] The heat and mass transfer effect of the building is obtained, and combined with historical environmental records, an attenuation coefficient based on temperature and humidity is determined. The attenuation coefficient measures the conduction attenuation between the outside and inside of the building. Based on the attenuation coefficient, the predicted external environment data of a preset time zone is compensated to determine the predicted environmental distribution.

[0053] Specifically, by analyzing the heat and humidity transfer of a building under different environmental conditions, and using historical environmental data, the attenuation coefficient of temperature and humidity between the building's exterior and interior can be calculated. The attenuation coefficient reflects the building's thermal insulation and moisture-proofing performance, taking into account the material properties of the building structure and the influence of insulation panels, providing a quantitative metric. For example, different environmental conditions can refer to indoor and outdoor conditions.

[0054] Next, the attenuation coefficient measures the conduction attenuation between the building's exterior and interior. The attenuation coefficient quantifies the degree to which external environmental conditions are reduced as they are conducted through the building structure to the interior. Specifically, the attenuation coefficient measures the proportion of external temperature and humidity that are attenuated during conduction due to material properties and building design, providing a more accurate understanding of a building's response to external climate change.

[0055] Then, the attenuation coefficient can be used to adjust the predicted external environment data for a future period, compensating for the differences in indoor and outdoor temperature and humidity caused by the conduction effect of the building structure. By considering the actual conduction characteristics of the building and the influence of the insulation panels, more accurate predicted internal environment data can be obtained, which serves as the basis for predicting environmental distribution and improves the accuracy of the prediction.

[0056] By acquiring building heat and mass transfer effects and historical environmental records, a temperature and humidity-based attenuation coefficient is determined to measure the conduction attenuation from the outside to the inside. Based on the attenuation coefficient, external environmental prediction data is compensated, ultimately determining a more accurate predicted environmental distribution. This takes into account indoor and outdoor temperature and humidity differences and building structural characteristics, improving the accuracy of indoor temperature and humidity prediction and providing a scientific basis for the formulation of environmental regulation strategies.

[0057] Furthermore, this application also includes:

[0058] The spatial regulation task is identified, and task division and branch matching are performed based on temperature and humidity characteristics. Parallel decision-making is executed for the temperature regulation branch and the humidity regulation branch to determine the island strategy. The island strategy is then transferred to the coupling coordination unit for strategy coupling processing to determine the pre-regulation strategy.

[0059] Specifically, by monitoring and analyzing temperature and humidity data in the space in real time, specific tasks requiring adjustment are identified, i.e., space regulation tasks. Space regulation tasks are classified according to temperature and humidity characteristics, identifying which areas need temperature regulation, which areas need humidity regulation, and the priority and urgency of the tasks.

[0060] Next, by analyzing the identified regulation tasks, and based on the temperature and humidity characteristics of each area, the spatial regulation tasks are divided into different categories and matched with the corresponding regulation branches. Temperature regulation tasks are assigned to the temperature regulation branch, and humidity regulation tasks are assigned to the humidity regulation branch, ensuring that each task can be handled professionally.

[0061] Then, parallel decision-making is performed on the temperature and humidity control branches to determine the islanded strategy. Each humidity control branch operates independently, making parallel decisions based on its own characteristic data and control objectives to determine the optimal control strategy. The control strategies exist independently in their respective "islands," ensuring that the control tasks of each humidity control branch can be completed efficiently without interference from other tasks.

[0062] Then, the isolated strategies determined by each humidity regulation branch are transmitted to the coupling coordination unit for comprehensive analysis and processing. By coupling the strategies, the independent isolated strategies are integrated to resolve potential conflicts and optimize the overall regulation effect, ultimately determining a coordinated pre-regulation strategy.

[0063] By identifying spatial regulation tasks and dividing and matching tasks based on temperature and humidity characteristics, parallel decision-making is performed on temperature and humidity regulation branches to determine isolated strategies. These isolated strategies are then transferred to the coupling and coordination unit for strategy coupling processing, ultimately determining the pre-regulation strategy. This ensures the efficient execution and overall coordination of regulation tasks, improving the system's regulation accuracy and operating efficiency.

[0064] Furthermore, this application also includes:

[0065] By combining the temperature and humidity coupling function, islanded strategy coupling is performed to determine the initialization strategy; by balancing the relationship between low carbon energy saving and comfort, the initialization strategy is optimized and adjusted to determine the pre-adjustment strategy.

[0066] Specifically, by using the temperature and humidity coupling function, the various isolated strategies can be analyzed and integrated through coupling processing. The independent temperature and humidity regulation strategies can be unified, and the mutual influence between the two can be considered to ensure that the regulation strategies are coordinated and consistent as a whole, forming an initial regulation scheme, i.e., the initialization strategy.

[0067] Next, although the initialization strategy has considered the coupling relationship between temperature and humidity, further optimization is needed to achieve the optimal balance between low-carbon energy saving and user comfort. Through optimization and adjustment, it is possible to reduce energy consumption while ensuring environmental comfort. This includes analyzing energy consumption data, user feedback, and environmental parameters, gradually adjusting the regulation parameters, and ultimately determining the optimal pre-regulation strategy.

[0068] By combining temperature and humidity coupling functions and integrating various isolated strategies, an initialization strategy is determined. Then, by balancing the relationship between low-carbon energy saving and comfort, the initialization strategy is optimized and adjusted, and finally a pre-regulation strategy is determined. This not only ensures the coordination and integrity of the regulation strategy, but also provides a comfortable indoor environment while improving energy efficiency.

[0069] Furthermore, this application also includes:

[0070] A preset amplitude is determined, which is based on the tolerance of the body surface; the pre-adjustment strategy is identified, and the strategy amplitude is determined; if the strategy amplitude is less than or equal to the preset amplitude, the pre-adjustment strategy is executed; if the strategy amplitude is greater than the preset amplitude, a multi-step strategy conversion is performed with the preset amplitude as a constraint.

[0071] Specifically, the preset adjustment range is a threshold set based on the human body's tolerance to changes in temperature and humidity, ensuring that it will not cause discomfort to the human body during the adjustment process. The preset adjustment range is derived from medical and physiological research to ensure human comfort and safety. For example, if the preset adjustment range is too large, instantaneous adjustment may affect human comfort and even affect the adjustment effect and lifespan of the underfloor heating system. Therefore, the preset adjustment range needs to be adjusted gradually.

[0072] Next, before implementing the pre-conditioning strategy, it is necessary to identify the specific content of the pre-conditioning strategy and determine the strategy amplitude, that is, the temperature and humidity change range required by the pre-conditioning strategy, which helps to assess the feasibility of the strategy and its impact on the environment.

[0073] Then, if the strategy adjustment amplitude is less than or equal to the preset adjustment amplitude, that is, if the strategy adjustment amplitude is within the preset adjustment amplitude range, it means that the adjustment amplitude is within the range acceptable to the human body, and the pre-adjustment strategy can be executed directly. In other words, the adjustment process can meet the needs without causing discomfort to the human body.

[0074] Then, if the strategy adjustment is greater than the preset adjustment, that is, if the strategy adjustment exceeds the preset adjustment, it is necessary to adjust in multiple steps, with each adjustment not exceeding the preset adjustment, to gradually reach the expected goal, thereby avoiding the discomfort caused by instantaneous adjustment, reducing the impact on the underfloor heating system, and extending its service life.

[0075] By determining a preset amplitude based on the body surface tolerance, identifying a pre-adjustment strategy and determining its amplitude, and then deciding whether to directly execute the strategy or perform a multi-step length conversion based on the amplitude, it is ensured that the adjustment strategy can be effectively implemented while guaranteeing human comfort and the stability of the underfloor heating system.

[0076] Furthermore, this application also includes:

[0077] After adjusting the temperature and humidity of the underfloor heating area, an abnormal adjustment response is obtained, and the abnormality is traced and located based on the management grid.

[0078] Specifically, after adjusting the temperature and humidity in the underfloor heating area, a real-time monitoring system is used to detect any abnormal responses during the adjustment process and identify any anomalies. Using a management grid as a baseline, the system precisely traces and locates the source of these anomalies, pinpointing the exact location and cause of the problem. By analyzing the temperature and humidity data and adjustment records of each area within the management grid, it is possible to determine which area or part of the system is experiencing a problem, allowing for targeted remedial measures. This not only enables rapid response and resolution of adjustment issues but also improves the stability and reliability of the underfloor heating system.

[0079] In summary, by monitoring abnormal responses after temperature and humidity adjustment and tracing the source based on the management grid, abnormal problems during adjustment can be effectively identified and resolved, ensuring the efficient and stable operation of the system.

[0080] In summary, the intelligent temperature and humidity control method for nano-floor heating provided in this application has the following technical effects:

[0081] By interacting with the spatial distribution data of the nano-underfloor heating installation area and the underfloor heating system data; based on the underfloor heating system data, modularization of the underfloor heating structure layer is performed; based on the spatial distribution data, spatial partitioning is performed to determine the management grid; traversing the management grid, combined with fuzzy control rules and temperature and humidity coupling functions, data-driven modeling is performed to generate an intelligent adjustment model, wherein the intelligent adjustment model includes a temperature adjustment branch, a humidity adjustment branch, and a subsequent coupling coordination unit; external environment prediction is performed, combined with building heat and mass transfer effects, to determine the predicted environmental distribution, wherein the predicted environmental distribution has spatiotemporal characteristics; monitoring and acquiring the spatial field based on temperature and humidity, combined with the standardized spatial field, to determine the spatial adjustment task; based on the spatial adjustment task and the predicted environmental distribution, combined with the intelligent adjustment model, real-time adjustment decisions and predictive adjustment decisions are made to determine the pre-adjustment strategy; the pre-adjustment strategy responds to the underfloor heating system to adjust the spatial temperature and humidity of the underfloor heating installation area. In other words, by combining real-time monitoring data of spatial temperature and humidity and external environment prediction data, the adjustment algorithm can achieve precise adjustment of each area. Meanwhile, by introducing anomaly monitoring and source tracing technologies, abnormal situations during the adjustment process can be quickly identified and handled, ensuring the efficient and stable operation of the system. Ultimately, this achieves the technical goal of intelligent and precise adjustment of the underfloor heating system, thereby improving user comfort, reducing energy consumption, and extending the system's service life.

[0082] Example 2: Based on the same inventive concept as the intelligent temperature and humidity control method for nano-underfloor heating described in the previous examples, this application also provides an intelligent temperature and humidity control system for nano-underfloor heating. Please refer to the appendix. Figure 2 ,include:

[0083] Data interaction module 11, used to interact with the spatial distribution data of the nano-underfloor heating installation area and the underfloor heating system data; management grid determination module 12, used to modularize the underfloor heating structure layer based on the underfloor heating system data, and to perform spatial partitioning based on the spatial distribution data to determine the management grid; intelligent adjustment model generation module 13, used to traverse the management grid, combine fuzzy control rules and temperature and humidity coupling functions to perform data-driven modeling, and generate an intelligent adjustment model, wherein the intelligent adjustment model includes a temperature adjustment branch, a humidity adjustment branch, and a subsequent coupling coordination unit; prediction environment distribution determination module 14, the prediction environment distribution determination module... 14 is used for external environment prediction, combining building heat and mass transfer effects to determine the predicted environment distribution, wherein the predicted environment distribution has spatiotemporal characteristics; 15 is used for spatial adjustment task determination, which monitors and acquires a spatial field based on temperature and humidity, and determines the spatial adjustment task in combination with a standardized spatial field; 16 is used for pre-adjustment strategy determination, which determines the pre-adjustment strategy based on the spatial adjustment task and the predicted environment distribution, combined with the intelligent adjustment model to make real-time adjustment decisions and predictive adjustment decisions; 17 is used for spatial temperature and humidity adjustment, which responds to the underfloor heating system with the pre-adjustment strategy and adjusts the spatial temperature and humidity of the underfloor heating area.

[0084] Furthermore, the management grid determination module 12 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0085] The system traverses the floor heating structure layers, modularizes each layer, and determines the one-dimensional module distribution; it traverses the spatial distribution data, divides the space into regions based on a preset difference in temperature and humidity requirements, and determines the two-dimensional region distribution; it integrates the one-dimensional module distribution and the two-dimensional region distribution to determine the management grid.

[0086] Furthermore, the management grid determination module 12 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0087] The underfloor heating structural layer includes at least an extruded polystyrene board, an electric underfloor heating film, and a wiring harness layer.

[0088] Furthermore, the intelligent regulation model generation module 13 in the aforementioned intelligent temperature and humidity regulation system for nano-floor heating is also used for:

[0089] An objective adjustment rule is set, which includes adjustment triggering and adjustment logic; user subjective adjustment records in a preset historical time zone are obtained, user habits are mined, and subjective adjustment rules are determined; the objective adjustment rule and the subjective adjustment rule are integrated, and rule fuzzification processing is performed to determine the fuzzy control rule, wherein the subjective adjustment rule has adjustment collision priority.

[0090] Furthermore, the predicted environmental distribution determination module 14 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0091] The heat and mass transfer effect of the building is obtained, and combined with historical environmental records, an attenuation coefficient based on temperature and humidity is determined. The attenuation coefficient measures the conduction attenuation between the outside and inside of the building. Based on the attenuation coefficient, the predicted external environment data of a preset time zone is compensated to determine the predicted environmental distribution.

[0092] Furthermore, the pre-adjustment strategy determination module 16 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0093] The spatial regulation task is identified, and task division and branch matching are performed based on temperature and humidity characteristics. Parallel decision-making is executed for the temperature regulation branch and the humidity regulation branch to determine the island strategy. The island strategy is then transferred to the coupling coordination unit for strategy coupling processing to determine the pre-regulation strategy.

[0094] Furthermore, the pre-adjustment strategy determination module 16 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0095] By combining the temperature and humidity coupling function, islanded strategy coupling is performed to determine the initialization strategy; by balancing the relationship between low carbon energy saving and comfort, the initialization strategy is optimized and adjusted to determine the pre-adjustment strategy.

[0096] Furthermore, the space temperature and humidity control module 17 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0097] A preset amplitude is determined, which is based on the tolerance of the body surface; the pre-adjustment strategy is identified, and the strategy amplitude is determined; if the strategy amplitude is less than or equal to the preset amplitude, the pre-adjustment strategy is executed; if the strategy amplitude is greater than the preset amplitude, a multi-step strategy conversion is performed with the preset amplitude as a constraint.

[0098] Furthermore, the space temperature and humidity control module 17 in the aforementioned intelligent temperature and humidity control system for nano-floor heating is also used for:

[0099] After adjusting the temperature and humidity of the underfloor heating area, an abnormal adjustment response is obtained, and the abnormality is traced and located based on the management grid.

[0100] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. The intelligent temperature and humidity control method and specific examples for nano-underfloor heating described in Embodiment 1 are also applicable to the intelligent temperature and humidity control system for nano-underfloor heating described in this embodiment. Through the foregoing detailed description of the intelligent temperature and humidity control method for nano-underfloor heating, those skilled in the art can clearly understand the intelligent temperature and humidity control system for nano-underfloor heating described in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0101] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0102] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligently adjusting temperature and humidity for nano floor heating, characterized in that: The method comprises: Spatial distribution data of interactive nano floor heating installation areas and floor heating system data; Based on the floor heating system data, modularize the floor heating structure layer, and based on the spatial distribution data, partition the space and determine the management grid; Traversing the management grid, combining fuzzy control rules with temperature and humidity coupling functions, performing data-driven modeling, and generating an intelligent adjustment model, wherein the intelligent adjustment model includes a temperature adjustment branch, a humidity adjustment branch, and a post-coupling coordination unit; Perform external environment prediction, combine the heat and mass transfer effects of the building, and determine the predicted environmental distribution, wherein the predicted environmental distribution has temporal and spatial characteristics; Monitor and obtain the space field based on temperature and humidity, combine with the standardized space field, and determine the space conditioning task; Based on the spatial adjustment task and the predicted environmental distribution, the intelligent adjustment model is combined to make real-time adjustment decisions and predictive adjustment decisions to determine a pre-adjustment strategy; The pre-adjustment strategy responds to the floor heating system to adjust the spatial temperature and humidity of the floor heating installation area.

2. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 1, characterized in that: The determining of the management grid includes: Traversing the floor heating structure layers, modularizing each structure layer, and determining a one-dimensional module distribution; Traversing the spatial distribution data, dividing the spatial regions based on the preset difference of the temperature and humidity requirements, and determining the two-dimensional regional distribution; The one-dimensional module distribution and the two-dimensional area distribution are integrated to determine the management grid.

3. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 2, characterized in that: The floor heating structure layer at least includes an extruded board, an electric floor heating film and a wiring harness layer.

4. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 1, characterized in that: Determine the fuzzy control rules, including: Setting objective adjustment rules, wherein the objective adjustment rules include adjustment triggers and adjustment logic; Obtain user subjective adjustment records for preset historical time zones, explore user habits, and determine subjective adjustment rules; The objective adjustment rule and the subjective adjustment rule are integrated, and rule fuzzification processing is performed to determine the fuzzy control rule, wherein the subjective adjustment rule has an adjustment collision priority.

5. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 1, characterized in that: The determining of the predicted environment distribution includes: Obtain the heat and mass transfer effects of the building, and determine the attenuation coefficient based on temperature and humidity in combination with historical environmental records. The attenuation coefficient measures the conduction attenuation from the outside of the building to the inside of the building; Based on the attenuation coefficient, the external environment prediction data of the preset time zone is compensated to determine the predicted environment distribution.

6. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 1, characterized in that: Combining the intelligent adjustment model to make real-time adjustment decisions includes: Identify the space conditioning task, perform task division and branch matching based on temperature and humidity characteristics, execute parallel decision making of the temperature conditioning branch and the humidity conditioning branch, and determine the island strategy. The island strategy is transferred to the coupling coordination unit to perform strategy coupling processing to determine the pre-adjustment strategy.

7. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 6, characterized in that: The strategy coupling process includes: In combination with the temperature and humidity coupling function, island strategy coupling is performed to determine the initialization strategy; By balancing the relationship between low-carbon energy saving and comfort, the initialization strategy is optimized and adjusted to determine the pre-adjustment strategy.

8. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 1, characterized in that: Before adjusting the temperature and humidity of the floor heating area, the following steps are performed: Determining a preset amplitude modulation, wherein the preset amplitude modulation is determined based on body surface tolerance; Identify the pre-adjustment strategy and determine the strategy amplitude adjustment; If the strategy amplitude modulation is less than or equal to the preset amplitude modulation, executing the pre-adjustment strategy; If the strategy amplitude modulation is greater than the preset amplitude modulation, the strategy multi-step conversion is performed with the preset amplitude modulation as a constraint.

9. The temperature and humidity intelligent adjustment method for nano floor heating according to claim 1, characterized in that: After adjusting the spatial temperature and humidity of the floor heating installation area, an abnormal adjustment response is obtained, and the abnormality is traced and located based on the management grid.

10. An intelligent temperature and humidity adjustment system for nano floor heating, characterized in that: The steps for implementing the temperature and humidity intelligent adjustment method for nano floor heating as described in any one of claims 1 to 9 include: A data interaction module, which is used to interact with the spatial distribution data of the nano floor heating laying area and the floor heating system data; A management grid determination module, the management grid determination module is used to perform floor heating structure layer modularization based on the floor heating system data, perform space partitioning based on the spatial distribution data, and determine the management grid; An intelligent adjustment model generation module, which is used to traverse the management grid, combine fuzzy control rules with temperature and humidity coupling functions, perform data-driven modeling, and generate an intelligent adjustment model, wherein the intelligent adjustment model includes a temperature adjustment branch, a humidity adjustment branch, and a post-coupling coordination unit; A predicted environment distribution determination module, which is used to perform external environment prediction and determine the predicted environment distribution in combination with the heat and mass transfer effects of the building, wherein the predicted environment distribution has spatiotemporal characteristics; A space conditioning task determination module, the space conditioning task determination module is used to monitor and obtain a space field based on temperature and humidity, and determine a space conditioning task in combination with a standardized space field; A pre-adjustment strategy determination module, the pre-adjustment strategy determination module is used to make real-time adjustment decisions and predictive adjustment decisions based on the space adjustment task and the predicted environmental distribution in combination with the intelligent adjustment model to determine the pre-adjustment strategy; A space temperature and humidity adjustment module is used for responding the pre-adjustment strategy to the floor heating system to adjust the space temperature and humidity of the floor heating installation area.

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