Instant nano floor heating device control system and method

Abstract

The invention provides an instant-heating nanometer floor heating device control system and method, the instant-heating nanometer floor heating device control system comprises a sensing module, a communication module, a temperature adjusting module, a safety module and an intelligent analysis module, and data interaction connection is established among the modules through a preset communication protocol. Through combination of embedded real-time control and an artificial intelligence self-learning algorithm, the problems that in the prior art, only setting of the same power of all areas can be achieved, or the real-time heating power is manually adjusted according to needs, the automation degree is low, and energy conservation is not facilitated are solved. The floor heating control and adjustment course can be automatically adjusted according to different areas, actual requirements and the like, accurate heating is achieved, and the energy utilization rate is increased.

Description

Technical Field

[0001] This invention relates to the field of underfloor heating control technology, and in particular to a control system and method for an instantaneous nano underfloor heating device. Background Technology

[0002] Most traditional underfloor heating systems use a uniform, constantly operating heating mode for the entire area, resulting in significant energy waste. On one hand, the slow heating of most traditional underfloor heating systems necessitates maintaining a continuous operating mode throughout the winter, leading to substantial energy waste. On the other hand, unoccupied areas of the house still receive the same heating power as occupied areas, resulting in wasted energy, especially in multi-room homes and apartments where energy efficiency is particularly low. With the development of smart home technology, energy conservation and intelligentization of heating systems are inevitable trends. How to achieve intelligent adjustment of underfloor heating systems based on different areas and needs is currently a key research focus. Summary of the Invention

[0003] This invention provides a control system and method for an instantaneous nano-floor heating device. By combining embedded real-time control with an artificial intelligence self-learning algorithm, it solves the problems of existing technologies that can only achieve the same power setting for all areas, or require manual adjustment of real-time heating power according to needs, resulting in low automation and poor energy efficiency. It enables automatic adjustment of the floor heating control according to different areas and actual needs, achieving precise heating and improving energy utilization.

[0004] The first aspect of this invention is to provide a control system for an instantaneous nano-floor heating device, including a sensing module, a communication module, a temperature regulation module, a safety module, and an intelligent analysis module, wherein each module establishes a data interaction connection through a preset communication protocol. The sensing module includes sensors for collecting human body position, human activity status, door and window opening and closing status, indoor temperature, and electric heating film temperature, and transmits the collected data to the communication module. The communication module includes a local communication unit and a remote communication unit. The local communication unit is used to receive data collected by the sensing module and the security module, and transmit the processed data to the temperature regulation module and the intelligent analysis module; and return the control strategy formed by the intelligent analysis module to the temperature regulation module. The temperature regulation module is used to adjust the power of each zone heating film through the power module according to the control strategy, and output the result to the communication module. The safety module is used to receive data collected by the sensing module and then monitor the operating status of the control system in real time and handle anomalies. The intelligent analysis module receives data from the communication module, analyzes it, generates a temperature control strategy, and transmits it to the temperature regulation module.

[0005] Furthermore, the instant nano floor heating system control system also includes a cloud server, which connects to the user terminal. As the core node for system data storage, remote control, and information exchange, the cloud server stores data sent by the communication module and transmits control commands sent by the user terminal to the communication module for remote control of the temperature adjustment module.

[0006] The user end, including the APP and / or Web end, is the direct entry point for users to interact with the control system.

[0007] Furthermore, the electrothermal film consists of multiple independent electrothermal film units connected together in parallel.

[0008] Furthermore, the sensing module includes a millimeter-wave radar sensor, an infrared sensor, a temperature sensor, a pressure sensor, and a door / window opening / closing sensor. The millimeter-wave radar and infrared sensor work together with the pressure sensor to collect the position and activity status of the human body. The temperature sensor is used to collect the indoor temperature and the temperature of the heating film. The door / window opening / closing sensor is used to collect the opening and closing status of the doors and windows.

[0009] Millimeter-wave radar sensors and infrared sensors can be installed in the ceiling or baseboard, with one or more placed in each independent area; the two work together to detect the presence and location of people.

[0010] Pressure sensors are installed under the floor, with multiple sensors evenly distributed in each enclosed space. They work in conjunction with millimeter-wave radar sensors to detect the activity status of personnel by measuring changes in pressure.

[0011] Temperature sensors for detecting indoor temperature are placed at any location in contact with air indoors, with multiple sensors installed in each enclosed space; temperature sensors for detecting the temperature of the heating film are placed on the surface of each heating film, with a measurement range of 0℃~100℃ and an accuracy of ±0.5℃, to monitor the operating temperature of the heating film in real time.

[0012] To ensure more accurate detection, the pressure sensors, millimeter-wave radar sensors, infrared sensors, and temperature sensors used to detect indoor temperature are distributed in units of 15 square meters, ensuring that one set is installed in each unit.

[0013] The sensing module also includes a data preprocessing unit. After the sensing module processes the collected raw data through the data preprocessing unit, such as filtering and noise reduction, outlier removal, and standardization, the processed data is transmitted to the communication module.

[0014] Furthermore, the local communication unit uses Zigbee wireless communication to enable data interaction between the local communication unit and the sensing module, temperature control module, and security module; the remote communication unit uses 5G / 4G mobile communication to enable the communication module to connect with the cloud server.

[0015] Furthermore, the temperature regulation module includes a power module, a drive unit, and a control unit. The drive unit receives signals sent by the control unit, amplifies the signals, and transmits them to the power module to adjust the power of the heating film, thereby regulating the temperature of the heating film.

[0016] The power module is an IGBT power module, which uses PWM modulation technology to precisely adjust the input power of each zone's heating film, thereby achieving continuous and adjustable heating power of the heating film and meeting the requirements for precise temperature control and energy saving in each zone.

[0017] Preferably, the rated voltage of the IGBT power module is not less than 380V, the rated current is not less than 50A, and the maximum switching frequency is not less than 20kHz. Each zone's heating film is independently connected via a star topology. Based on PWM pulse width modulation technology, the PID algorithm of the control unit converts the deviation between the target temperature and the actual temperature into a duty cycle signal, and controls the IGBT's on-time to adjust the output power.

[0018] Furthermore, the method for adjusting the zone temperature of the power module is as follows: when the temperature difference is >5℃, the temperature needs to be increased rapidly, and the output power is 100% of the rated power; when the temperature difference is less than or equal to ±1℃, the temperature needs to be maintained, and the duty cycle is kept stable at 30%-50% and overshoot is suppressed by ±5% fluctuation; when an over-temperature signal is received from the safety module or a pause command is received from the cloud, the duty cycle is immediately set to 0 and the output is cut off, while the maximum duty cycle is limited in conjunction with the electricity price strategy.

[0019] The sensing module also includes a current and voltage detection unit, which is used to collect the input current and voltage of the power module and transmit them to the control unit of the temperature regulation module in real time.

[0020] Furthermore, the intelligent analysis module includes a data receiving unit, a data analysis unit, and a strategy optimization unit. The data receiving unit receives data information transmitted by the sensing module through the communication module, stores the received data information in the database, and uses the data analysis unit to analyze the data. Based on the analysis results, a preset temperature control strategy is formed, and after optimization by the strategy optimization unit, it is transmitted to the temperature regulation module.

[0021] Furthermore, the sensing module also includes a humidity sensor.

[0022] Furthermore, the humidity sensor is a capacitive humidity sensor with a measurement range of 20% RH - 90% RH, a measurement accuracy of ±3% RH, and an operating temperature range of -10℃ - 60℃.

[0023] Furthermore, the humidity sensor is located next to the temperature sensor and is used to collect indoor humidity data. After preprocessing, the humidity sensor transmits the collected humidity data to the communication module. During data analysis, the intelligent analysis module combines the humidity data with the temperature data to generate a temperature control strategy that better suits human comfort.

[0024] A second aspect of the present invention is to provide a control method for a control system of an instantaneous nano-floor heating device, comprising the following steps: S1. The sensing module collects data on human body position, human body activity status, door and window opening and closing status, indoor temperature, and electric heating film temperature, and transmits the collected data to the communication module. S2. The data collected by the sensing module in step S1 is transmitted to the local communication unit. After receiving the data, the local communication unit verifies the data. If the verification is successful, the data is packaged according to a preset format and transmitted to the security module, temperature regulation module, and intelligent analysis module respectively. If the verification fails, the communication module sends a data retransmission request to the sensing module. The communication module enables bidirectional data transmission. S3. After receiving the data transmitted in step S2, the intelligent analysis module uses decision tree algorithm and neural network algorithm to perform multi-dimensional analysis on the data. Then, it combines the analysis results with the system's preset energy-saving control heating strategy and uses genetic algorithm to optimize the control strategy. Finally, it transmits the optimized control strategy to the temperature regulation module. S4. After receiving the control strategy transmitted by the intelligent analysis module, the temperature regulation module generates a PWM control signal by combining the data collected by the sensor module, and adjusts the input power of the heating film in each zone through the power module to achieve continuous power adjustment; then the power adjustment status is uploaded to the cloud server through the communication module. S5. The security module compares the received collected data with the system preset data in real time. The security module processes abnormal situations according to the comparison results and transmits the signals to the cloud server and user terminal through the communication module, so as to realize real-time monitoring and abnormal handling of the system. S6. Repeat S1-S5.

[0025] Furthermore, the method for multi-dimensional data analysis based on decision tree algorithms and neural network algorithms is as follows: Based on human location data and activity status data, analyze the distribution and activity intensity of people indoors to determine the priority of heating demand in each area. Areas with high activity intensity have a higher priority than areas with low activity intensity, and areas with human activity have a higher priority than areas without human activity. Based on the data on the opening and closing of doors and windows, the heat exchange rate between indoors and outdoors is calculated. The heat loss rate when doors and windows are open is calculated as 0.5-1℃ / min, and the heat loss rate when doors and windows are closed is calculated as 0.1-0.3℃ / min. Based on indoor temperature data and electric heating film temperature data, the control effect of the temperature regulation module is analyzed. If the indoor temperature deviates from the target temperature by more than ±0.5℃, the control effect is deemed poor and the control parameters need to be adjusted. At the same time, the heating efficiency of the electric heating film is analyzed and the temperature rise rate of the electric heating film is calculated. If the rise rate is less than 0.3℃ / min, the heating efficiency is deemed abnormal. The decision tree analysis results are formed with indoor temperature deviation as the root node, personnel location data, door and window opening status, and humidity as intermediate nodes, and heating demand priority as leaf nodes. Based on the analysis results of the decision tree, a three-layer BP neural network is used for training. The data from the decision tree analysis is then input into the trained neural network. The nonlinear relationship between the multi-dimensional data is further fitted according to the neural network algorithm, and the output result is obtained after correction.

[0026] Furthermore, the intelligent analysis module has an automatic learning function, which learns user habits, indoor environmental change patterns, and outdoor building change patterns. The outdoor building change pattern learning uses an LSTM neural network to train the building structure parameters, historical temperature change curves, and outdoor ambient temperature data to establish a thermal inertia model for each zone. Based on the thermal response coefficient output by the model, the preheating time required for different areas to reach the target temperature is calculated. In the automatic control phase, the temperature regulation module starts the corresponding zone's electric heating film in advance according to the preheating time to ensure that the temperature reaches the set value when the user enters, reducing the time spent on ineffective heating.

[0027] Furthermore, the system's preset energy-saving heating strategy includes energy-saving control methods based on electricity price response and environmental linkage. The analysis results of the intelligent analysis module generate control commands based on the energy-saving heating strategy. Specifically, the energy-saving control methods are as follows: the energy-saving control method based on electricity price response is activated according to the usage time; when a large temperature change is detected, such as when doors and windows are open or a cold wave is approaching, the energy-saving control method based on environmental linkage is activated.

[0028] Furthermore, the energy-saving control method based on electricity price response includes the following steps: The system acquires time-of-use electricity price signals from the external power grid and transmits them to the intelligent analysis module. The strategy optimization unit of the intelligent analysis module, combined with the room occupancy prediction results collected by the sensor module, sets electricity price thresholds: during off-peak hours, a full-power heat storage strategy is triggered to maintain the surface temperature of the electric heating film at the upper limit of the threshold; during peak hours, a basic temperature maintenance strategy is triggered, and the power is reduced to 30%. The temperature regulation module automatically switches the operating mode according to the time-of-use strategy issued by the intelligent analysis module to optimize electricity costs.

[0029] Furthermore, the energy-saving control method based on environmental linkage includes the following steps: The door and window opening and closing sensors collect the status of doors and windows in each area in real time and transmit it to the temperature regulation module through the local communication module. When a door or window is detected to be open for more than 30 seconds, the temperature regulation module is directly triggered to pause the heating of the corresponding zone until the door or window is closed and then resumes operation. When the remote communication module receives a warning message from the outdoor weather platform about a large change in temperature, the intelligent analysis module generates a temporary adjustment command to control the temperature regulation module to raise the base temperature of each zone by 2°C 2 hours in advance to avoid high power compensation caused by a sudden drop in room temperature.

[0030] Compared with existing technologies, the instantaneous nano-floor heating device control system and control method of the present invention have the following beneficial technical effects: This invention breaks through the limitations of traditional underfloor heating systems that rely solely on thermostat settings or simple time programming. Through multimodal sensor fusion technology, it can proactively and accurately identify the real-time location and activity status of people indoors. It also possesses powerful self-learning capabilities, deeply learning users' daily routines, temperature preferences, and usage habits through long-term data accumulation, thereby achieving personalized temperature control. The system can predict user needs, preheating frequently used areas before the user arrives home or automatically lowering the temperature after the user falls asleep, without requiring manual intervention, providing an unprecedentedly convenient and comfortable experience. This invention achieves intelligent control of the instant nano-underfloor heating device through intelligent learning of various forms of monitoring, such as human location, activity status, and indoor temperature, as well as user habits. This invention deeply integrates advanced sensing technology, artificial intelligence algorithms, and a highly efficient instant nano-underfloor heating device, successfully solving industry pain points such as high energy consumption, slow response, and unintelligent control in traditional underfloor heating systems. While providing an excellent comfort experience, it maximizes energy efficiency and represents the future direction of heating technology development.

[0031] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0033] Figure 1 This is a system framework diagram of the control system for an instantaneous nano-floor heating device according to the present invention; Figure 2 This is a flowchart of a control system for an instantaneous nano-floor heating device according to the present invention. Figure 3 This is a flowchart of a control method for an instantaneous nano-floor heating device according to the present invention; Detailed Implementation

[0034] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0035] A control system for an instantaneous nano-floor heating device includes a sensing module, a communication module, a temperature regulation module, a safety module, and an intelligent analysis module. The modules establish data interaction connections through a preset communication protocol. The sensing module includes sensors for collecting data on human body position, human activity status, door and window opening / closing status, indoor temperature, and electric heating film temperature, and transmits the collected data to the communication module. The communication module includes a local communication unit and a remote communication unit. The local communication unit is used to receive data collected by the sensing module and the security module, and transmit the processed data to the temperature control module and the intelligent analysis module; and to return the control strategy formed by the intelligent analysis module to the temperature control module. The temperature control module is used to adjust the power of each zone's heating film through the power module according to the control strategy, and output the result to the communication module. The safety module is used to receive data collected by the sensing module and then monitor the operating status of the control system in real time and handle any anomalies. The intelligent analysis module receives data from the communication module, analyzes it, generates a temperature control strategy, and transmits it to the temperature regulation module.

[0036] The electric heating film consists of multiple independent electric heating film units connected in parallel, which facilitates zoned control and repair and replacement of individual electric heating film units after damage.

[0037] Specifically, the sensing module is used to collect data on the status of people indoors, environmental parameters, and equipment operating parameters, providing data support for system control. The sensing module includes millimeter-wave radar sensors, infrared sensors, temperature sensors, pressure sensors, and door and window opening / closing sensors. The millimeter-wave radar, infrared sensors, and pressure sensors work together to collect the position and activity status of people, the temperature sensors collect the indoor temperature and the temperature of the heating film, and the door and window opening / closing sensors collect the opening and closing status of doors and windows.

[0038] Millimeter-wave radar sensors and infrared sensors can be installed in the ceiling or baseboard, with one or more placed in each independent area; they work together to detect the presence and location of people. The millimeter-wave radar sensor can identify the stationary, walking, and running states of a person, with an accuracy rate of no less than 92%. Its operating frequency is set to 60GHz to avoid interference with other electronic devices. Door and window opening / closing sensors use magnetic proximity sensors, including magnets fixed to the door / window frame and Hall effect elements fixed to the door / window sash. When the door / window is opened or closed, the Hall effect element senses the change in magnetic field and outputs a corresponding electrical signal. The millimeter-wave radar sensor and infrared sensor work together to compensate for the blind spots of a single sensor in obstructed environments.

[0039] Pressure sensors are installed beneath the floor, with multiple sensors evenly distributed within each enclosed space. Changes in pressure, combined with millimeter-wave radar sensors, identify the activity of personnel. Temperature sensors for detecting indoor temperature are placed at any location exposed to air within the room, with multiple sensors in each enclosed space. Temperature sensors for detecting the temperature of the heating film are installed on the surface of each heating film, with a measurement range of 0℃ to 100℃ and an accuracy of ±0.5℃, monitoring the operating temperature of the heating film in real time. To ensure higher accuracy, the pressure sensors, millimeter-wave radar sensors, infrared sensors, and temperature sensors are distributed in units of 15 square meters, ensuring one set is installed in each unit.

[0040] Specifically, the sensing module also includes a data preprocessing unit. After the raw data is processed by the data preprocessing unit, such as filtering and noise reduction, outlier removal, and standardization, the sensor module transmits the processed data to the communication module.

[0041] The local communication unit uses Zigbee wireless communication to enable data interaction between the local communication unit and the sensing module, temperature control module, and security module; the remote communication unit uses 5G / 4G mobile communication to enable the local communication unit to connect with the cloud server.

[0042] The temperature regulation module includes a power module, a drive unit, and a control unit. The drive unit receives signals sent by the control unit, amplifies the signals, and transmits them to the power module to adjust the power of the heating film, thereby regulating the temperature of the heating film.

[0043] The power module is an IGBT power module, which uses PWM modulation technology to precisely adjust the input power of each zone heating film, so as to realize the continuous adjustment of the heating power of the heating film and meet the requirements of precise temperature control and energy saving of each zone.

[0044] The IGBT power module has a rated voltage of no less than 380V, a rated current of no less than 50A, and a maximum switching frequency of no less than 20kHz. Each zone's heating film is independently connected via a star topology. Based on PWM pulse width modulation technology, the PID algorithm of the control unit converts the deviation between the target temperature and the actual temperature into a duty cycle signal, and controls the IGBT's on-time to adjust the output power.

[0045] The power module adjusts the zone temperature as follows: when the temperature difference is greater than 5℃, the temperature needs to be increased rapidly, and the output power is 100% of the rated power; when the temperature difference is less than or equal to ±1℃, the temperature needs to be maintained, and the duty cycle is kept stable at 30%-50% and overshoot is suppressed by ±5% fluctuation; when an over-temperature signal is received from the safety module or a pause command is received from the cloud, the duty cycle is immediately set to 0 and the output is cut off, while the maximum duty cycle is limited in conjunction with the electricity price strategy.

[0046] The sensing module also includes a current and voltage detection unit, which is used to collect the input current and voltage of the power module and transmit them to the control unit of the temperature regulation module in real time, so that the control unit can perform closed-loop control of the power output and fault diagnosis.

[0047] The intelligent analysis module includes a data receiving unit, a data analysis unit, and a strategy optimization unit. After receiving the data information transmitted by the sensing module through the communication module, the data receiving unit stores the received data information in the database. The data analysis unit analyzes the data and forms a preset temperature control strategy based on the analysis results. After optimization by the strategy optimization unit, the strategy is transmitted to the temperature regulation module.

[0048] The sensing module also includes a humidity sensor. This is a capacitive humidity sensor with a measurement range of 20%RH - 90%RH, a measurement accuracy of ±3%RH, and an operating temperature range of -10℃ - 60℃. The humidity sensor is located next to the temperature sensor and is used to collect indoor humidity data. After preprocessing, the humidity sensor transmits the collected humidity data to the communication module. The intelligent analysis module combines the humidity and temperature data during data analysis to generate a temperature control strategy that better suits human comfort.

[0049] The safety module includes an overload protection unit, an over-temperature protection unit, a leakage current protection unit, and an abnormal alarm unit.

[0050] A control method for an instantaneous nano-floor heating device control system includes the following steps: S1, the sensing module collects data on human body position, human activity status, door and window opening and closing status, indoor temperature, and electric heating film temperature, and transmits the collected data to the communication module.

[0051] Millimeter-wave radar, infrared sensors, and pressure sensors are used together to collect the location and activity status of the human body; temperature sensors are used to collect indoor temperature and the temperature of the electric heating film; and door and window opening and closing sensors are used to collect the opening and closing status of doors and windows.

[0052] S2. The data collected by the sensing module in step S1 is transmitted to the local communication unit. After receiving the data, the local communication unit verifies the data. If the verification is successful, the data is packaged according to a preset format and transmitted to the security module, temperature regulation module, and intelligent analysis module respectively. If the verification fails, the communication module sends a data retransmission request to the sensing module, thereby realizing bidirectional data transmission.

[0053] The local communication unit uses Zigbee wireless communication to achieve data interaction between the local communication unit and the sensing module, temperature control module, and security module. The communication rate is no less than 100Mbps, and the communication distance is no less than 100m when there are no obstructions. Before transmission, the data is encrypted using the AES-256 encryption standard, and CRC32 cyclic redundancy check is used to ensure data integrity. The remote communication unit uploads the data processed by the local communication unit to the cloud server via 5G / 4G mobile communication. If the 5G / 4G network signal is poor, it automatically switches to WiFi communication mode. At the same time, it receives control commands from the cloud server, transmits them to the local communication unit, and then the local communication unit distributes them to the temperature control module and intelligent analysis module.

[0054] S3. After receiving the data transmitted in step S2, the intelligent analysis module uses decision tree algorithm and neural network algorithm to perform multi-dimensional analysis on the data. Then, it combines the analysis results with the system's preset energy-saving control heating strategy and uses genetic algorithm to optimize the control strategy. Finally, the optimized control strategy is transmitted to the temperature regulation module.

[0055] S31. The data receiving unit of the intelligent analysis module receives the data transmitted in step S2 through the communication module and stores it in the SQLite database. S32. The data analysis unit uses decision tree algorithms and neural network algorithms to perform multi-dimensional analysis of the data: S321. Based on human location data and activity status data, analyze the distribution and activity intensity of people indoors, determine the priority of heating demand in each area, and prioritize areas with high activity intensity over areas with low activity intensity, and prioritize areas with human activity over areas without human activity.

[0056] S322. Based on the data of the opening and closing status of doors and windows, calculate the heat exchange rate between indoors and outdoors. The heat loss rate when doors and windows are open is calculated as 0.5-1℃ / min, and the heat loss rate when doors and windows are closed is calculated as 0.1-0.3℃ / min.

[0057] S323. Based on the indoor temperature data and the electric heating film temperature data, analyze the control effect of the temperature regulation module. If the indoor temperature deviates from the target temperature by more than ±0.5℃, the control effect is deemed poor and the control parameters need to be adjusted. At the same time, analyze the heating efficiency of the electric heating film and calculate the temperature rise rate of the electric heating film. If the rise rate is less than 0.3℃ / min, the heating efficiency is deemed abnormal. S324. The decision tree analysis results are formed with indoor temperature deviation as the root node, personnel location status data, door and window opening status, and humidity as intermediate nodes, and heating demand priority as leaf nodes. S325. Based on the analysis results of the decision tree, a three-layer BP neural network is used for training. The data analyzed by the decision tree is input into the trained neural network. The nonlinear relationship between the multi-dimensional data is further fitted according to the neural network algorithm, and the output result is obtained after correction.

[0058] S33. The analysis results of S32 are combined with the system's preset energy-saving heating control strategy. The genetic algorithm is used to optimize the control strategy, and then the optimized control strategy is transmitted to the temperature regulation module.

[0059] The system's preset energy-saving heating strategy includes energy-saving control methods based on electricity price response and environmental linkage. The analysis results of the intelligent analysis module generate control commands based on the energy-saving heating strategy. The specific energy-saving control methods are as follows: the energy-saving control method based on electricity price response is activated according to the usage time; when a large temperature change is detected, such as when doors and windows are open or a cold wave is approaching, the energy-saving control method based on environmental linkage is activated.

[0060] The energy-saving control method based on electricity price response includes the following steps: The system acquires time-of-use electricity price signals from the external power grid and transmits them to the intelligent analysis module. The strategy optimization unit of the intelligent analysis module, combined with the room occupancy prediction results collected by the sensor module, sets electricity price thresholds: during off-peak hours, a full-power heat storage strategy is triggered to maintain the surface temperature of the electric heating film at the upper limit of the threshold; during peak hours, a basic temperature maintenance strategy is triggered, and the power is reduced to 30%. The temperature regulation module automatically switches the operating mode according to the time-of-use strategy issued by the intelligent analysis module to optimize electricity costs.

[0061] An energy-saving control method based on environmental linkage includes the following steps: The door and window opening and closing sensors collect the status of doors and windows in each area in real time and transmit it to the temperature regulation module through the local communication module. When a door or window is detected to be open for more than 30 seconds, the temperature regulation module is directly triggered to pause the heating of the corresponding zone until the door or window is closed and then resumes operation. When the remote communication module receives a warning message from the outdoor weather platform about a large change in temperature, the intelligent analysis module generates a temporary adjustment command to control the temperature regulation module to raise the base temperature of each zone by 2°C 2 hours in advance to avoid high power compensation caused by a sudden drop in room temperature.

[0062] S4. After receiving the control strategy transmitted by the intelligent analysis module, the temperature regulation module generates a PWM control signal by combining the data collected by the sensor module, and adjusts the input power of the heating film in each zone through the power module to achieve continuous power adjustment; then the power adjustment status is uploaded to the cloud server through the communication module. S5. The security module compares the received collected data with the system's preset data in real time. Based on the comparison results, the security module handles abnormal situations and transmits the signals to the cloud server and user terminal through the communication module, thereby realizing real-time monitoring and abnormal handling of the system. S6. Repeat S1-S5.

[0063] The security module provides real-time monitoring and anomaly handling for the system, including: Overload protection: The overload protection unit collects the real-time current and voltage values ​​acquired by the current and voltage detection unit. When the current exceeds the set value and lasts for more than 5 seconds, it automatically cuts off the circuit and outputs an overload protection signal to the communication module. Over-temperature protection: The over-temperature protection unit collects the detected temperature of the heating film. When the temperature of the heating film exceeds 60℃ or the temperature of the power module exceeds 85℃, it immediately sends a control signal to the temperature regulation module to control the power module to stop working. At the same time, it outputs an over-temperature protection signal to the communication module. After over-temperature protection, the power can only be restored if the condition of 'temperature < 50℃ + user confirmation' is met. Leakage protection: The leakage protection unit detects the leakage current in the circuit. When leakage is detected, it quickly cuts off the main circuit and outputs a leakage protection signal to the communication module. Abnormal alarm: When overload, overtemperature, leakage, short circuit and other protection signals are received, the audible and visual alarm is activated, and the abnormal information is sent to the cloud server and user mobile terminal through the communication module.

[0064] Specifically, in step S32, the data analysis unit uses decision tree algorithm and neural network algorithm to perform multi-dimensional analysis of the data. The specific steps are as follows: S321. Collect human location data, activity status data, environmental data, etc., and normalize the collected data to determine the priority of heating demand in each area: In one specific embodiment, decision tree nodes are determined, with indoor temperature deviation as the root node, human habits and environmental characteristics as intermediate nodes, and heating demand as leaf nodes to form priority results, categorizing heating demand into five priorities: extremely high, high, medium, low, and extremely low. During setup, areas with high activity intensity have higher priority than areas with low activity intensity, and areas with human activity have higher priority than areas without human activity.

[0065] Based on the analysis results of the decision tree, a three-layer BP neural network is trained, and the data from the decision tree analysis is input into the trained neural network. The nonlinear relationship between the multi-dimensional data is further fitted according to the neural network algorithm. By using the confidence weight of the decision tree and the accuracy of the neural network as weighting factors, a weighted average is performed to correct the analysis error, thereby forming a heating demand strategy.

[0066] For example, if data collected in the living room area shows that people stayed for 8 minutes, the percentage of doors and windows open / closed was 20%, and the indoor temperature deviation was -1.5℃, the initial priority is determined as "extremely high demand" through decision tree analysis. The neural network outputs a heating demand coefficient of 0.85. Using a decision tree confidence weight of 95% and a neural network accuracy of 95%, the coefficient is corrected to 0.825, and the final determination is "extremely high demand". The strategy generation unit increases the target power of this area by 25% - 30%.

[0067] S322. Based on the data of the opening and closing status of doors and windows, calculate the heat exchange rate between indoors and outdoors. The heat loss rate when doors and windows are open is calculated as 0.5-1℃ / min, and the heat loss rate when doors and windows are closed is calculated as 0.1-0.3℃ / min.

[0068] S323. Based on the indoor temperature data and the electric heating film temperature data, analyze the control effect of the temperature regulation module. If the indoor temperature deviates from the target temperature by more than ±0.5℃, the control effect is deemed poor and the control parameters need to be adjusted. At the same time, analyze the heating efficiency of the electric heating film and calculate the temperature rise rate of the electric heating film. If the rise rate is less than 0.3℃ / min, the heating efficiency is deemed abnormal.

[0069] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0070] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0071] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A control system for an instantaneous nano-floor heating device, characterized in that: It includes a sensing module, a communication module, a temperature control module, a safety module, and an intelligent analysis module. The modules establish data interaction connections with each other through a preset communication protocol. The sensing module includes sensors for collecting human body position, human activity status, door and window opening and closing status, indoor temperature, and electric heating film temperature, and transmits the collected data to the communication module. The communication module includes a local communication unit and a remote communication unit. The local communication unit is used to receive data collected by the sensing module and the security module, and transmit the processed data to the temperature regulation module and the intelligent analysis module. The control strategy generated by the intelligent analysis module is returned to the temperature regulation module; The temperature regulation module is used to adjust the power of each zone heating film through the power module according to the control strategy, and output the result to the communication module. The safety module is used to receive data collected by the sensing module and then monitor the operating status of the control system in real time and handle anomalies. The intelligent analysis module receives data from the communication module, analyzes it, generates a temperature control strategy, and transmits it to the temperature regulation module.

2. The instantaneous nano-floor heating device control system as described in claim 1, characterized in that: The sensing module includes a millimeter-wave radar sensor, an infrared sensor, a temperature sensor, a pressure sensor, and a door / window opening / closing sensor. The millimeter-wave radar, infrared sensor, and pressure sensor work together to collect the position and activity status of the human body. The temperature sensor is used to collect the indoor temperature and the temperature of the heating film. The door / window opening / closing sensor is used to collect the opening and closing status of doors and windows.

3. The instantaneous nano-floor heating device control system as described in claim 1, characterized in that: The local communication unit uses Zigbee wireless communication to enable data interaction between the local communication unit and the sensing module, temperature control module, and security module; the remote communication unit uses 5G / 4G mobile communication to enable the communication module to connect with the cloud server.

4. The instantaneous nano-floor heating device control system as described in claim 1, characterized in that: The temperature regulation module includes a power module, a drive unit, and a control unit. The drive unit receives signals sent by the control unit, amplifies the signals, and transmits them to the power module to adjust the power of the heating film, thereby regulating the temperature of the heating film.

5. The instantaneous nano-floor heating device control system as described in claim 1, characterized in that: The intelligent analysis module includes a data receiving unit, a data analysis unit, and a strategy optimization unit. The data receiving unit receives data information transmitted by the sensing module through the communication module, stores the received data information in the database, and uses the data analysis unit to analyze the data. Based on the analysis results, a preset temperature control strategy is formed, and after optimization by the strategy optimization unit, it is transmitted to the temperature regulation module.

6. A control method for the control system of the instantaneous nano-floor heating device according to any one of claims 1-5, characterized in that, Includes the following steps: S1. The sensing module collects data on human body position, human body activity status, door and window opening and closing status, indoor temperature, and electric heating film temperature, and transmits the collected data to the communication module. S2. The data collected by the sensing module in step S1 is transmitted to the local communication unit. After receiving the data, the local communication unit verifies the data. If the verification is successful, the data is packaged according to a preset format and transmitted to the security module, temperature regulation module and intelligent analysis module respectively. If the verification fails, a data retransmission request is sent to the sensing module through the communication module, and bidirectional data transmission is achieved through the communication module. S3. After receiving the data transmitted in step S2, the intelligent analysis module uses decision tree algorithm and neural network algorithm to perform multi-dimensional analysis on the data. Then, it combines the analysis results with the system's preset energy-saving control heating strategy and uses genetic algorithm to optimize the control strategy. Finally, it transmits the optimized control strategy to the temperature regulation module. S4. After receiving the control strategy transmitted by the intelligent analysis module, the temperature regulation module generates a PWM control signal by combining the data collected by the sensor module, and adjusts the input power of the heating film in each zone through the power module to achieve continuous power adjustment; then the power adjustment status is uploaded to the cloud server through the communication module. S5. The security module compares the received collected data with the system preset data in real time. The security module processes abnormal situations according to the comparison results and transmits the signals to the cloud server and user terminal through the communication module, so as to realize real-time monitoring and abnormal handling of the system. S6. Repeat S1-S5.

7. The control method for the instantaneous nano-floor heating device control system as described in claim 6, characterized in that, The method for multi-dimensional data analysis based on decision tree algorithm and neural network algorithm is as follows: Based on human location data and activity status data, analyze the distribution and activity intensity of people indoors to determine the priority of heating demand in each area. Areas with high activity intensity have a higher priority than areas with low activity intensity, and areas with human activity have a higher priority than areas without human activity. Based on the data on the opening and closing of doors and windows, the heat exchange rate between indoors and outdoors is calculated. The heat loss rate when doors and windows are open is calculated as 0.5-1℃ / min, and the heat loss rate when doors and windows are closed is calculated as 0.1-0.3℃ / min. Based on indoor temperature data and electric heating film temperature data, the control effect of the temperature regulation module is analyzed. If the indoor temperature deviates from the target temperature by more than ±0.5℃, the control effect is deemed poor and the control parameters need to be adjusted. At the same time, the heating efficiency of the electric heating film is analyzed and the temperature rise rate of the electric heating film is calculated. If the rise rate is less than 0.3℃ / min, the heating efficiency is deemed abnormal. The decision tree analysis results are formed with indoor temperature deviation as the root node, personnel location data, door and window opening status, and humidity as intermediate nodes, and heating demand priority as leaf nodes. Based on the analysis results of the decision tree, a three-layer BP neural network is used for training. The data from the decision tree analysis is then input into the trained neural network. The nonlinear relationship between the multi-dimensional data is further fitted according to the neural network algorithm, and the output result is obtained after correction.

8. The control method of the instantaneous nano-floor heating device control system as described in claim 6, characterized in that: The system's preset energy-saving heating strategy includes energy-saving control methods based on electricity price response and environmental linkage. The analysis results of the intelligent analysis module generate control commands based on the energy-saving heating strategy. The specific energy-saving control methods are as follows: the energy-saving control method based on electricity price response is activated according to the usage time; when a large temperature change is detected, such as when doors and windows are open or a cold wave is approaching, the energy-saving control method based on environmental linkage is activated.

9. The control method for the instantaneous nano-floor heating device control system as described in claim 8, characterized in that, The energy-saving control method based on electricity price response includes the following steps: The system acquires time-of-use electricity price signals from the external power grid and transmits them to the intelligent analysis module. The strategy optimization unit of the intelligent analysis module, combined with the room occupancy prediction results collected by the sensor module, sets electricity price thresholds: during off-peak hours, a full-power heat storage strategy is triggered to maintain the surface temperature of the electric heating film at the upper limit of the threshold; during peak hours, a basic temperature maintenance strategy is triggered, and the power is reduced to 30%. The temperature regulation module automatically switches the operating mode according to the time-of-use strategy issued by the intelligent analysis module to optimize electricity costs.

10. The control method of the instantaneous nano-floor heating device control system as described in claim 8, characterized in that, The energy-saving control method based on environmental linkage includes the following steps: The door and window opening and closing sensors collect the status of doors and windows in each area in real time and transmit it to the temperature regulation module through the local communication module. When a door or window is detected to be open for more than 30 seconds, the temperature regulation module is directly triggered to pause the heating of the corresponding zone until the door or window is closed and then resumes operation. When the remote communication module receives a warning message from the outdoor weather platform about a large change in temperature, the intelligent analysis module generates a temporary adjustment command to control the temperature regulation module to raise the base temperature of each zone by 2°C 2 hours in advance to avoid high power compensation caused by a sudden drop in room temperature.

Images